CONDITIONAL VOLATILITY AND ASSET PRICING (An Empirical ...prr.hec.gov.pk/jspui/.../1/Kashif_Hamid_Finance_HSR... · My sweet Daughter Aleeza Faryal Boparai and Son Hashir Kashif Boparai,

CONDITIONAL VOLATILITY AND ASSET

PRICING

(An Empirical Evidence from Emerging Economies)

Researcher: Supervisor:

Kashif Hamid Dr.Arshad Hasan

REG NO. 10-FMS/PHDFIN/F09

Department of Business Administration

Faculty of Management Sciences

INTERNATIONAL ISLAMIC UNIVERSITY,

ISLAMABAD, PAKISTAN

2017

ii

CONDITIONAL VOLATILITY AND ASSET PRICING

Empirical Evidence from Emerging Economies)

Researcher: Supervisor:

Kashif Hamid Dr. Arshad Hasan


Faculty of Management Sciences

INTERNATIONAL ISLAMIC UNIVERSITY ISLAMABAD

PAKISTAN

iii

CONDITIONAL VOLATILITY AND ASSET PRICING

(An Empirical Evidence from Emerging Economies)

Kashif Hamid

REG. NO. 10-FMS/PHDFIN/F09

Submitted in partial fulfillment of the requirements for the

PhD degree with the specialization in Finance

at the Faculty of Management Sciences,

International Islamic University,

Islamabad

``

Dr. Arshad Hasan August, 2017

iv

ABSTRACT

This study investigates the relative performance of linear versus nonlinear methods to

predict volatility and return in equity markets. The study is performed on the

EAGLEs and NEST markets, including China, India, Indonesia Pakistan,

Bangladesh and Malaysia by using daily data of equity markets from the period

January 4, 2000 to December 30, 2010. Nonlinear and asymmetric ARCH ef f ects

have been test by Lagrange Multiplier test. A range of models from random walk

model to multifaceted ARCH class models are used to predict volatility. The results

reveal that MA (1) model ranks first with use of RMSE criterion in linear models. With

regards to nonlinear models for predicating stock return volatility, the ARCH,

GARCH-in-Mean (1, 1) model and EGARCH (1, 1) model perform well. GARCH-

in- Mean model outperforms on the basis of AIC, SIC and Log Likelihood

method. It is concluded that GARCH specification is best in performance to

capture the volatility. GARCH in mean model is extended with the macroeconomic

variables in the variance equation for SS, BSE, JCI, KSE, KLSE and DSE. The

macroeconomic variables include CPI, Term Structure of interest rate, industrial

production and oil prices. Data for Macroeconomic variable is on monthly basis for the

period Jan 2000 to Dec 2010. For SS, BSE, JCI, DSE, KLSE and KSE markets the

conditional mean is significant and models the persistency in long run scenario and

suggests for an integrated process. The model indicates that oil price have positive

impact on volatility for SS. For BSE change in industrial production index and interest

rate change have negative coefficients which indicate that industrial growth and increase

in interest rate change has negative relationship with the volatility for this economy. For

JCI the model indicates that change in growth in industrial production has positive

impact on volatility. For KSE, ARCH and GARCH terms are not significant but growth

rate in real sector and oil price has significant impact on volatility. However DSE has no

significant results. For KSE the model indicates that inflation has positive impact on

volatility but change in oil price has negative effect on volatility. Bullish market effect

is quite significant in explaining the volatility capturing ability for all the equity

markets. The TGARCH(1,1) model is estimated for SS, BSE, JCI, DSE KLSE and

KSE returns series and results indicate that asymmetric effect exists for all the

equity markets which indicates the presence of leverage effect. Study concludes that

TGARCH (1,1) model is a potential envoy of the asymmetric conditional volatility

procedure for the daily frequency of the data regarding to equity markets of SS, BSE,

JCI, DSE, KLSE, and KLSE. Further GARCH-in-mean model is extended with value

at risk that indicates the variables for variance equation are statistically significant and

the VaR have significant impact on all equity markets in explaining the conditional

volatility. In Last GARCH-in-Mean Model is extended with the semi-variance and

results indicate that the downside risk causes rise in the volatility. It has ability to

capture the asymmetric behavior of equity returns and reports the fat tails of the returns.

It is concluded that volatility plays a significant role in asset price determination.

Keywords: Conditional volatility, linear, nonlinear, Asymmetric effect,

Macroeconomic Variables, Bullish, Value at Risk, Semi-Variance.

v

FORWARDING SHEET

The thesis entitled CONDITIONAL VOLATILITY AND ASSET PRICING (An

Empirical Evidence from Emerging Economies) submitted by KASHIF HAMID in

partial fulfillment of PhD degree in Finance has been completed under my guidance and

supervision. I am satisfied with the quality of student’s research work and allow him to

submit this thesis of further process of as per IIU rules & regulations.

Date: Signature:

Name:

vi

(Acceptance by the Viva Voce Committee)

Title of Thesis: CONDITIONAL VOLATILITY AND ASSET PRICING (An Empirical

Evidence from Emerging Economies)

Name of Student KASHIF HAMID

Registration No REG NO. 10-FMS/PHDFIN/F09

Accepted by the Faculty of Management Science, Department of Business

Administration, INTERNATIONAL ISLAMIC UNIVERSITY, ISLAMABAD in partial

fulfillment of the requirements for the PhD Degree in Finance

Viva Voce Committee

Dean

Chairman/Director/Head

External Examiner

Supervisor

Member

(Day, Month, Year)

vii

viii

COPYRIGHT PAGE

The copy rights of the thesis entitled “Conditional Volatility and Asset Pricing (An

Empirical Evidence from Emerging Economies)” are reserved with the author KASHIF

HAMID©.

ix

STATEMENT OF UNDERSTANDING

DECLARATION

I hereby declare that the research work is my own work and no part of this thesis is

copied out from any source. It is further declared that this research is entirely my

personal effort made under the sincere guidance of my supervisor Dr. Arshad Hasan. No

segment of this work presented in this research thesis has been submitted in support of

any other degree /qualification of this or any other university or institute of learning.

KASHIF HAMID


x

ACKNOWLEDGEMENT

By the core of heart, thanks to almighty ALLAH, the most gracious and caring to all of us because HE

has provided us good health, creative thoughts, loving parents, brilliant teachers, good friends and

nerves to complete the Research work. The most special praises and honors are for the GREAT HOLY

PROPHET MUHAMMAD (Peace by upon him) who enlightened the spirit of mankind with the

strengths and essence of Islam and guided to attain the purposeful knowledge where ever from the

world. It is a matter of enormous respect and pleasure for me to articulate profound spirit of affection

and sincerest feeling of appreciation to my respected and honorable supervisor Dr. Arshad Hassan, for

his kind supervision, true guidance, keen interest, valuable suggestions and sympathetic attitude

throughout my research work. I tender my sincere thanks to honorable Dr Syed Zulfiqar Ali Shah , Dr

Zaheer for their highness, kind of support and motivation throughout the PhD. I tender my sincere

thanks to Dr Iqrar Khan Vice Chancellor, Prof. Dr. Ejaz Bhatti, Dr. Anwar-ul-Haq Gondal, and

Bahadur Ali Kang University of Agriculture, Faisalabad for their inspiring guidance and unstained

help during my study period. I pay my gratitude to my Great Mother (Safia Bibi), Great Father (Haji

Abdul Hamid Boparai), who always desired to see me glittering high on the skies of success. Without

their day and night prayers, sacrifices, encouragement, moral and financial support, the present project

would have been merry dream. My gratitude will remain incomplete if I don’t mentioned my sweet

Brothers Ch. Atif Hamid Boparai, Ch. Asif Hamid Boparai , Dr Saleem Yousaf and Waseem Yousaf,

AMIN and Dr Naeem, Wife (Dr. Kishwar Naheed, Assistant Professor Forensic Medicine (PMC) ,

My sweet Daughter Aleeza Faryal Boparai and Son Hashir Kashif Boparai, Friends (Usman

Khurram, Shahid Imdad, Dr.Inam-ul-Haq, Ahmad Fraz,, Jahanzeb Hundal, Muhammad Jawad

Aulakh, Faisal Mushtaq Sahi, Tahir Suleman, Waseem Ghaffar) whose prayers always with me and

great contribution during whole my studies. Words are deficient to communicate my self-effacing

obligation to my great parents for support and prayers for my successful completion of education.

KASHIF HAMID

xi

TABLE OF CONTENTS

Ch.

No.

TITLE Page

No.

ABSTRACT…………………………………………………………. iv

COPYRIGHT PAGE………………………………………………... vi

STATEMENT OF UNDERSTANDING……………………………. ix

ACKNOWLEDGEMENT…………………………………………… x

TABLE OF CONTNETS……………………………………………. xi

LIST OF FIGURES………………………………………………….. xix

LIST OF TABLES…………………………………………………... xiv

LIST OF APPENDIX………………………………………………... xiv

LIST OFABBREVIATIONS………………………….……………. xx

1 INTRODUCTION…………………………………………………… 1

1.1 Brief Statement of Study……………………………………. 2

1.2 Problem Statement ……………………...………………….. 11

1.3 Theoretical Framework …………………..…………..…….. 11

1.3.1 Efficient Market Theory……………………… 11

1.3.2 Volatility Theories…………………………….. 12

1.3.2.1 Leptokurtosis…………………………….. 12

1.3.2.2 Volatility Clustering or Volatility Pooling 12

1.3.2.3 Leverage Effects………………………… 12

1.3.3 Asset Pricing Theories…………………….. 13

xii

1.4 Significance of the Study……………………………………. 14

1.5 Contribution of the Study …………………………………... 16

1.6 Objectives of the Research ………………………………….. 18

2 LITERATURE REVIEW……………………………………………. 19

2.1 HYPOTHESES OF THE STUDY ……………….…..……... 37

3 DATA AND METHODLOGY……………………………………… 38

3.1 Emerging and Growth-Leading Economies….……………… 39

3.2 NEST ……………………………………………………….. 39

3.3 Stock Market Return 39

3.4 Methodology………………………………………………… 40

3.4.1 Sign and size test………………………………….. 40

3.4.2 The Mean Equation………………………………….. 41

3.4.3 Linear Models ……………………………………. 42

3.4.3.1 Random Walk Model 42

3.4.3.2 Autoregressive Model – (AR) 42

3.4.3.3 Moving Average Model – (MA) 43

3.4.3.4 Exponential Smoothing Model (ESM) 43

3.5 Non-Linear Models ………………………………… 43

3.5.1 The ARCH Model …………………………………. 43

3.5.2 The GARCH Model ……………………………….. 44

3.5.3 Asymmetric GARCH Models 45

3.5.4 Threshold GARCH……………………………….... .. 45

xiii

3.5.5 The EGARCH Model ……………………………….. 46

3.5.6 GJR-GARCH Model ……………………………... 47

3.5.7 Volatility Switching Model………………………….. 47

3.5.8 Quadratic ARCH (QARCH) ………………………... 48

3.6 Econometric Models ……………………………….. 48

3.6.1 Volatility and Return ……………………………. 48

3.6.1.1 Model 1: Return, Volatility and

Macroeconomic Model …………………………

48

3.6.1.2 Model 2(a): Return, Volatility and Market

Conditions Asymmetries ………………………..

50

3.6.1.3 Model 2(b): Return, Volatility and Market

Asymmetric Conditions, Good News and Bad News

Effect….

51

3.6.1.4 Model 3: Return, Volatility and Value at Risk ….. 51

3.6.1.5 Model 4: Return, Volatility and Semi-variance ……. 52

4 RESULTS AND DISCUSSION……………………………………. 54

4.1 Econometric Models For China …………………… 76

4.2 Econometric Models For India…………………….. 83

4.3 Econometric Models For Indonesia………………. 91

4.4 Econometric Models For Bangladesh ……………… 98

4.5 Econometric Models For Malaysia 105

4.6 Econometric Models For Pakistan 112

4.7 Summary of the Results 120

5 CONCLUSION……………….…………………………. 135

5.1 Implications of the study…………………………… 146

REFERENCES ……………………………......................... 148

xiv

APPENDICES…………………………………………... 159

LIST OF TABLES

Table-

No.

TITLE Page

No.

1 Descriptive Statistics for Daily Market Returns ………………….... 55

2 Positive and Negative Returns Summary ………………………….. 57

3 Sign- and Size Bias Tests ……………………………………..……. 59

4 Lagrange-Multiplier Test of ARCH Effects for GARCH model........ 60

5 Lagrange-Multiplier Test of ARCH Effects FOR GJR-GARCH....... 61

6 Lagrange -Multiplier Test of ARCH Effects FOR EGARCH …… 61

7 Lagrange-Multiplier Test of ARCH Effects FOR VS-GARCH …… 62

8 Lagrange-Multiplier Test of ARCH Effects FOR Q-GARCH …….. 63

9 Estimates of GARCH (1, 1) Model………………………………..... 64

10 Estimates of EGARCH (1, 1) Model ………………………………. 65

11 Estimates of GJR-GARCH (1, 1) Model……………………………. 66

12 Estimates of VS-GARCH (1, 1) Model ……………………………. 67

13 Estimates of QARCH (1, 1) Model ………………………………… 68

14 Forecasting Performance of Linear and Nonlinear Models of the

Volatility of Stock Returns ………………………………………..... 72

15 Correlation Matrix for Stock Returns …………………………..... 73

16 Conditional Volatility Correlation Matrix ………………………….. 74

17(a) Estimates of GARCH in Mean (1, 1) Model 1: Return, Volatility and

Macroeconomic Model for SS: Impact of Macroeconomic Variables

on Return………………………………………. 76

xv

17(b) Estimates of GARCH in Mean (1, 1) Model 1: Return, Volatility and

Macroeconomic Model for SS: Impact of Macroeconomic Variables

on Volatility……………………. 77

18(a) Estimates of GARCH in Mean (1, 1) Model 2(a): Return, Volatility

and Market Conditions Asymmetries for SS : Impact of Market

Conditions Asymmetries on Return……………… 78

18(b) Estimates of GARCH in Mean (1, 1) Model 2(a): Return, Volatility

and Market Conditions Asymmetries for SS: Impact of Market

Conditions Asymmetries on Return……………………… 79

19 Estimates of TGARCH Model 2(b): Return, Volatility and Market

Asymmetric Conditions, Good News and Bad News Effect………… 80

20 Estimates of GARCH (1, 1) Model 3: Return, Volatility and

Value at Risk for SS ………………………………………………...

81

21 Estimates of GARCH (1, 1) Model 4: Return, Volatility and Semi

variance for SS………………………………………………… 82


Macroeconomic Model for BSE: Impact of Macroeconomic Variables

on Return……………………………………….

83


Macroeconomic Model for BSE: Impact of Macroeconomic Variables



and Market Conditions Asymmetries for BSE : Impact of Market



and Market Conditions Asymmetries for BSE: Impact of Market



Asymmetric Conditions, Good News and Bad News Effect…………

87


Value at Risk for BSE ……………………………………………….

89

26 Estimates of GARCH (1, 1) Model 4: Return, Volatility and Semi -

Variance for BSE………………………………………………… 90

xvi


Macroeconomic Model for JCI: Impact of Macroeconomic Variables

on Return………………………………………. 91


Macroeconomic Model for JCI: Impact of Macroeconomic Variables



and Market Conditions Asymmetries for JCI : Impact of Market



and Market Conditions Asymmetries for JCI: Impact of Market





Value at Risk for JCI ………………………………………………. 96


Variance for JCI……………………………………………… 97


Macroeconomic Model for DSE: Impact of Macroeconomic Variables

on Return………………………………………. 98





and Market Conditions Asymmetries for DSE : Impact of Market



and Market Conditions Asymmetries for DSE: Impact of Market




xvii


Value at Risk for DSE ………………………………………………. 103


Variance for DSE………………………………………………… 104


Macroeconomic Model for KLSE: Impact of Macroeconomic

Variables on Return………………………………………. 105



Variables on Volatility……………………. 106


and Market Conditions Asymmetries for KLSE: Impact of Market



and Market Conditions Asymmetries for KLSE: Impact of Market





Value at Risk for KLSE …………………………………………. 110


Variance for KLSE………………………………………………… 111


Macroeconomic Model for SE: Impact of Macroeconomic Variables

on Return………………………………………. 112


Macroeconomic Model for KSE: Impact of Macroeconomic Variables



and Market Conditions Asymmetries for KSE : Impact of Market



and Market Conditions Asymmetries for KSE: Impact of Market 115

xviii

Conditions Asymmetries on Return………………………




Value at Risk for KSE ………………………………………………. 117


Variance for KSE………………………………………………… 118

47 Diagnostic Test 119

xix

LIST OF FIGURES

FIG.

No.

TITLE Page

No.

1 Daily Mean Returns of China, India, Indonesia, Bangladesh,

Malaysia and Pakistan from January 2000 to December 2010

56

2 Stock Returns of Equity Market 58

3 Conditional Standard Deviation of BSE 70

4 Conditional Variance of BSE 70

5 Conditional Standard deviation of DSE 70

6 Conditional Variance of DSE 70

7 Conditional Standard Deviation of JCI 70

8 Conditional Variance of JCI 70

9 Conditional Standard deviation of KSE 71

10 Conditional Variance of KSE 71

11 Conditional Standard Deviation of KLSE 71

12 Conditional Variance of KLSE 71

13 Conditional Standard deviation of SS 71

14 Conditional Variance of SS 71

xx

LIST OF ABBREVIATIONS

ABBREVIATIONS COMPLETE WORD

AGARCH Asymmetric Generalized Autoregressive Conditional

Heteroskedasticity

AIC Akaike Information Criterion

AMEX American Stock Exchange

ANST-GARCH Asymmetric Nonlinear Smooth Transition- Generalized Auto

Regressive Conditional Heteroskedasticity

AP-ARCH Asymmetric Power Auto Regressive Conditional

Heteroskedasticity

AR Auto Regressive

ARCH Auto Regressive Conditional Heteroskedasticity

ARMA Auto Regressive Moving Average

BBVA Banco Bilbao Vizcaya Argentaria

BIC Bayesian Information Criterion

BSE Bombay Stock Exchange

CAPM Capital Asset Pricing Model

CGARCH Component Generalized Autoregressive Conditional

Heteroskedasticity

CPI Customer Price Index

DSE Dhaka Stock Exchange

EAGLEs Emerging and Growth Leading Economies

EGARCH Exponential Generalized Auto Regressive Conditional

Heteroskedasticity

EMH Efficient Market Hypothesis

ESTAR Exponential Smoothing Transition Auto Regressive

ESM Exponential Smoothing Model

EWMA Exponentially Weighted Moving Average

FIGARCH Fractionally Integrated Generalized Autoregressive Conditional

Heteroskedasticity

GARCH Generalized Auto Regressive Conditional Heteroskedasticity

GARCH-M Generalized Autoregressive Conditional Heteroskedasticity In

xxi

Mean Model

GDP Gross Domestic Product

GED Generalized Error Distribution

GJR-GARCH Glosten-Jagannathan-Runkle Generalized Autoregressive

Conditional Heteroskedasticity

G7 Great Seven

HML High Minus Low

IID Independent Identical Distribution

JCI Jakarta Stock Exchange

KLSE Kualalumpur Stock Exchange

KSE Karachi Stock Exchange

LM Langrage –Multiplier

LSTAR Long Horizon Smooth Transition Auto Regressive

MA Moving Average

NASDAQ National Association Of Securities Dealers Automated

Quotations

NEST Next to Eagles

NGARCH Nonlinear Asymmetric Generalized Autoregressive Conditional

Heteroskedasticity

NYSE New York Stock Exchange

QGARCH Quadratic Generalized Auto Regressive Conditional

Heteroskedasticity

RWM Random Walk Hypothesis

SIC Schwarz Information Criterion

SMB Small Minus Big

SS Shanghai Stock Exchange

S&P Standard And Poor

TGARCH Threshold Generalized Auto Regressive Conditional

Heteroskedasticity

VS-GARCH Volatility Switching Generalized Auto Regressive Conditional

Heteroskedasticity

xxii

CHAPTER 1

INTRODUCTION

2

1. INTRODUCTION:

1.1 Brief Statement of Study

Predicting future returns and volatility remained a central focus for research

advancement in the area of asset pricing. Either it is possible to forecast tomorrow’s

prices with some certainty or not, and profits may be attained successfully or not.

Current information could be used to predict future prices, and with this information,

investment decisions and trading of assets cab be performed sensibly and rationally. It

is not as simple as it is deemed. Due to key and significant role of financial markets in

justifying the economic positions of countries, number of different studies has been

launched to investigate various phenomena’s in this spectrum. Stock returns and its

volatility analysis remain, one of the key facets of the equity markets that have got long

attention in the financial literature. The term volatility means that the stretching of all

expected outcomes regarding to an unsure variable. Volatility also means

unpredictability or fluctuations in expected outcomes in general and considered as

synonym of risk as measured by standard deviation or variance. However from financial

markets perspective it is a rate at which the price of a security rises or falls for given set

of returns. In financial economics the emerging markets means those markets where

economic progress is following the advanced countries. As economic growth is measured

through GDP and is reflected by liquidity in the local debt market, mechanism of equity

markets, exchange markets and having regulatory bodies. In such markets investors seek

out for high returns but such markets have greater level of risk due to domestic

infrastructure problems, political instability, and high volatility in the financial markets.

In sum we can say emerging markets are those markets which have some characteristics

of a developed markets, however it does not fulfill the complete standards of a developed

market. In finance the term asset pricing is described in a manner that how financial

3

assets are priced. It means that how much amount one person is ready to pay for an asset

when he buys it. Such price is the representative of the amount which is assigned by the

market in a fair or unfair manner. Financial assets include equity, debt, hybrids and

derivative instruments. Asset pricing movements are managed and controlled through the

law of demand and supply, however prices rises or falls as the value of money behaves.

However if price of a financial asset changes rapidly in a short span of time it leads

towards high volatility otherwise if the price of a security fluctuates in a slow manner, it

is known as low volatility. Non-Linearity is the relationship that cannot be expressed in a

linear combination through its input variables. In non-linearity the association among the

variables is not static but dynamic. When we examine the cause and effect relationship

then Nonlinearity is a common issue. Such occurrence needs complex modeling for

nonlinear events against suggested hypothesis. Nonlinearity leads towards random

behavior generally.

In equity markets rise and fall in the stock prices is a natural and normal phenomenon.

The ups and downs of stock prices are continuous process of day to day market

operation. The movement of stock prices indicates that some forces are behind such

dynamics. The demand and supply function is the basic element in the change of share

prices from one level to another level. Higher the existence of volatility is an indicator

that the stock market is also highly liquid. Asset pricing determination depends upon

the volatility of each security. Particularly a sharp rise in volatility of the stock market

carries a higher change in stock price. Investor’s sentiments perceive that a sharp rise

in stock market volatility leads to an amplifying aspect in the associated risk of equity

investment and as a result investors can move their finances towards a lesser amount of

risky assets. This element demonstrates substantial impact on business investment and

ultimately leads towards economic growth and equilibrium through various directions.

4

In stock market theory the rapid and uncertain changing dynamics in the prices of stock

is a mark of efficient market hypothesis which is not a sign of destructive phenomenon.

However high price fluctuation may create destructive effects due to excessive level of

volatility and may affect the efficiency of the market. These elements may end with

financial market crashes or crises. Crashes or crisis are an integral part of developed and

emerging markets. Pakistan, India China is not beyond these phenomena and many

times this particular scene has been performed in the history. Similarly financial crises

in developed market like United States financial crisis spread speedily as a mark of bad

news and it was observed that the Indian equity markets have been dropped around

about 60 percent and approximate $1.3 trillion in market capitalization have been wiped

out since January 2008. As a result a huge amount of foreign portfolio investors wiped out

during September 2008 to December 2008 and ultimately national investors were

psychologically affected (Kumar, 2009).

The association between stock return and its volatility has marvelous history created by

financial researchers. Conditional volatility and contemporaneous returns have negative

association empirically. In the financial literature it is narrated that due to ups and

downs in changing of conditional volatility, the negative or positive returns are

generally seem associated and this phenomenon is known as asymmetric volatility.

During stock market crashes the existence of asymmetric volatility can be mostly seen

when a large decrease in the prices of the stock is related to an increase in the volatility

of the market (Wu, 2001). Leverage effect is also one of the most important theories

that deem the affiliation between stock price and volatility (Black, 1976; Christie,

1982). A negative return up raises the financial leverage that makes the stock more

risky and cause to an increase in volatility due to leverage effect.

Its reality volatility and stock price association is much studied in stabled and

5

developed equity markets, but still little concentration has been given in the direction

of wide-ranging study in the field of the volatility modeling in the emerging markets of

ASIA. There is huge difference of various features in between emerging markets and

developed stock markets and this phenomenon is well known by the financial

researchers. Bekaert and Wu (2000) indicated that there are four distinctive

uniqueness of emerging equity market returns, firstly there are higher average

returns sample secondly there exist low degree of relationship with the returns of

developed equity market. Thirdly returns are more predictable and lastly keep high

level of volatility. Such differentiation can have momentous implications for

investment and policy decision makers. Therefore to focus on the empirical evidences

of developed markets findings can mislead policy makers while in decision.

Franses and Van Dijk (2000) stated that future returns are possible to predict for making

the investment decision but it is complex. Malkiel and Fama (1970) argued that

Efficient Market Hypothesis (EMH) is a major pillar in determining the financial time

series data modeling. Study defined that efficient markets are those markets where

prices always reflect all available information. This element directly implies that it is

impossible to outperform the market by means of available information. While

investigating the stock returns, the first concern is expected return or the mean return

and the second element is the risk consideration with respect to certain level of return.

Majorly the investment decisions take into consideration risk scenario but it is

important that the investors should be rewarded for grasping non-diversifiable risk

through higher expected returns on their investment portfolios. Generally risk is

referred as volatility or the variance. Black and Scholes (1973) had taken into

consideration the variance in determining options pricing and developed the model to

estimate the assets prices. Modern portfolio theory assumes that investors are rational

6

and they prefer large returns over small returns and in the same way low risk preferred

over high risk. The interest of the investor lies in the first and second moment along

with the given level of expected return and risk. The complexity of risk is more

complex than the return. Markowitz (1952) narrated that the assets to construct optimal

portfolios, in line with the theory, the forecasts of the expected returns and co-variances

are required for this purpose. The risk that lies in returns is measured through the

corresponding variance, and hence investors should hold an efficient portfolio by means

of mean-variance with the maximum possible return along with given level of variance.

Mandelbrot (1963) documented and realized some predictability in the variance, this

element caused to introduce a family of models that takes into consideration these

predictable behaviors. Such efforts made possible to develop; the Autoregressive

Conditional Heteroscedasticity (ARCH) model as Engle (1982) explained and

Bollerslev (1986) introduced the GARCH model. Family consisting of these models

turned out to be successful. Here one element to be discussed that the volatility does not

behave constantly and in a stable way but huge variations are normally observed that

make complexity while in predictions. In various studies volatility is forecasted and the

performance of the models is analyzed in linear and nonlinear context thereafter the

development of a new model in capturing and forecasting the volatility. Gokcan (2000)

used ARCH and GARCH family models for modeling the volatility it is seen that where

the financial time series are skewed, the linear models are failed to explain the past

volatility and predicting the future volatility. The study used linear vs Non-linear

GARCH versions by taking monthly stock returns of seven emerging economies for the

period Feb 1988 to Dec 1996. It is concluded that even returns show skewed distribution,

GARCH (1,1) model performs better than the EGARCH model.

7

Karmakar (2005) performed evaluation of volatility forecasting techniques from simple to

complex GARCH family models in Indian equity market and concluded that there are

only two competing models i.e random walk model and GARCH(1,1). Further he

identified that existing literature conclude conflicting evidences regarding to forecasting

ability and quality of stock market volatility. In a further study Mutunga, Islam and

Orawo (2015) used the method of estimating functions in predicting and modeling the

volatility of financial returns. Higher order moments are incorporated in the estimation

approach for modeling. First order GJR-GARCH and EGARCH models are used to

predict the volatility for NIKKEI-225 and S&P-500 markets and the mean absolute error,

loss functions, and mean square error is applied to check the predictive ability of the

asymmetric GARCH models. Franses and Van Dijk (2000) concluded that sometimes

volatility is more moderate while other periods may hold high volatility. This particular

element became a reason to develop such models which attributes to trends and patterns

in the time series. Shocks often disturb the trends and these effects might follow the

data for a while; the shocks might even persist for years. The autocorrelation among the

errors terms may cause problems and might lead to invalid projections therefore it

violates the criteria for linear regression models. The size of the shock or even the sign

of a shock influence the volatility level. Generally, negative returns raise the volatility

higher than the positive returns of the similar size. Asymmetric effects are however not

only important in the variance hence the returns can also behave in an asymmetric way.

It is the advantages of nonlinear models that asymmetries are taken into account by

these models which can be seen in the data series. Generally asymmetry means that

huge negative returns look more frequently in comparison to positive returns of the

same size. This phenomenon is commonly indicating the reality that negative returns

8

are related with greater size of risk but it is also observed that positive returns of the

same size are not following this pattern.

This stud also introduces the role of volatility against macroeconomic variables in asset

pricing modeling as well. Pierdzioch, Döpke and Hartmann (2008) explored the linkage

between stock market volatility and macroeconomic factors but in a limited way but not

focused on relation of interrelated volatilities and their predicting power of returns. Study

concluded that stock market volatility likely to increase in the phase of downturns in

business cycle (Schwert, 1989; Hamilton and Lin, 1996; Errunza and Hogan, 1998). Javid

and Ahmad (2009) used macroeconomic factors with stock market return in a conditional

multifactor capital asset pricing model with GARCH-in-Mean model indicate that

conditional model represents very minor advancement in the description of risk and return

relationship regarding to the equity market of Pakistan for this sample period. It is

observed that some stocks are providing a good level of compensation to the investors for

risk. However results indicate that the variance risk is not contributing for risk premium

Here the model is extended in a way that permits variation in economic risk factors

prevailing according to the business conditions of Pakistan and conditioning information

is taken as lagged macroeconomic variables. Moreover the outcomes reveal that the proof

exists in the favor of conditional multifactor capital asset pricing model. The

macroeconomic factors that are experienced to execute comparatively fit in describing

variations in the returns of the assets which consist of inflation risk, consumption growth,

call money rate, term structure of interest rate. However, it is considerable that the market

return, oil price and foreign exchange risk explains significant component of the

variability of stock returns in the time series, and have confined pressure on the asset

pricing and hence concluded that macroeconomic variations can explain expected returns

variation and this variability may contains few business cycle correlations. It is very

9

interesting for investors that such kind of results put the questions that whether

macroeconomic forces capture business cycle fluctuations and how these forces help to

predict stock market volatility or not. The ultimate answer to such dilemma may assist to

redesign and reframe the models of assets pricing as a better and exact solutions to the

practical issues of optimal portfolio selection process, also provide assistance to watch

and administer financial risks in an efficient way. Such kind of results provide more

purposeful and useful answer about systematic financial sector risk to financial analysts,

macroeconomists, central bankers, and big market players to get a wider range of

consideration of latent macroeconomic determinants. Let see a supposition that an

investor wishes to predict volatility of the stock market which is based on macroeconomic

factors. In reality, only a small amount of studies are accessible that report in the support

of the suggestions for using the macroeconomic data for empirical research in finance as

(Christoffersen, Ghysels and Swanson, 2002; Guo, 2003; Clark and Kozicki, 2005) used.

On the other hand, the financial analysts of macroeconomic data has applied for research

on macroeconomic and business-cycle fluctuations as counted by (Croushore, 2001;

Orphanides and Van-Norden, 2002; Orphanides and Williams, 2002; Croushore and

Stark, 2003; Gerberding, Seitz and Worms, 2005) in their relevant studies. This study is

going to take initiative that particular macroeconomic variables are to be identified that

can influence the volatility of the stock returns and this element contributes to the existing

financial literature. There is no proper empirical evidence available yet in the existing

literature that may assist an individual investor, institutional investors, banker and

regulators to answer this question in a justified manner. The determinants of stock market

volatility are commonly expressed as the orders inflow, growth rate of industrial

production and output gap measure. A small number of previous studies have focused, on

the industrial production growth rate as a macroeconomic variable for business cycle

10

fluctuations measurements (Schwert, 1989; Campbell, Lettau, Malkiel, and Xu, 2001).

Hence it is required that, the volatilities produced by various factors can be incorporated

in asset pricing model to forecast the returns of the assets or portfolios and this

phenomenon provides a genuine and logical contribution in the modeling and

predictability of the risk and the return in a nonlinear way.

The reason of doing so is that the study also takes into account the reality that information

set of an investor changes over time. In previous literature, it is generally practiced that to

utilize information set that may comprised of sample data that either macroeconomic

variables assist to predict volatility of stock market or not (Schwert, 1989; Pesaran and

Timmermann, 1995; 2000) found that the value of volatility projections are based on data

of macroeconomic variables that is roughly analogous to the value of volatility

projections based on other parameters or not. Predicting stock market volatility by means

of such parameters does not systematically reduce investors’ average utility. Moreover,

an investor who uses macroeconomic data for volatility forecasting realizes profits in the

market analogous to those investors who would have reaped out returns based on

volatility forecasts from any other parameters. The expected results, explain that an

investor who desires to situate an investment strategy in practice may in general make use

of the results reported in the present research on the macroeconomic determinants of

stock market volatility that is modeled by using historical data of macroeconomic

variables. Therefore this study is expected to introduce new scope of stock volatility in the

emerging markets scenario over which no previous research has determined the predictive

ability of the GARCH family of models in these equity markets.

Summary

At a glance this study introduces the mechanism of conditional volatility and how this

volatility can be modeled to produce the ultimate outcomes for the best interest of the

11

investors and portfolio diversification across the emerging economies. Emerging

economies are those economies where economic progress is following the advanced

countries. In such markets investors seek out for high returns but such markets have

greater level of risk due to domestic infrastructure problems, political instability, and high

volatility in the financial markets. It is visualized that such emerging economies have

high level of volatility and volatility behave in an asymmetric manner. To capture and

model such asymmetric volatility in emerging economies, it is a dire need of present era

to have a comprehensive study for ascertaining the asset pricing. Asset pricing is a quite

difficult and complex phenomenon where returns are behaving in a nonlinear fashion.

1.2 Problem Statement

The behavior of returns and volatility in emerging markets is always a matter of interest and

Pakistan is no exception. Non-linearities and asymmetric pattern in the returns and

volatility in emerging markets are unique attributes of these markets. Emerging markets

have higher volatility and produce higher returns and macroeconomic variables play a

dynamic role in such economies for the movement of returns and volatility. Asset pricing

in the presence of such behavior is still an unaddressed issue.

Therefore this study is an effort to probe into the matter for the induction of conditional

volatility and non-linearities perspective in an asset pricing model.

1.3 Theoretical Framework.

1.3.1 Efficient Market Theory

Fama (1970) introduced efficient market hypothesis as a fair game model. The movement

of prices cannot be predictable because the behavior of prices is random on each day and

prices absorb all available information. Further this study elaborates that the expected

returns are consistent with its risk based upon its historical price trend. Further study

introduced EMH for empirical testing into three broad categories based upon the given

12

information set i.e., i. Weak form of Efficient Market Hypothesis, ii. Semi Strong Form of

Efficient Market Hypothesis iii. Strong Form of Efficient Market Hypothesis.

According to the Random Walk Model the subsequent price changes are identically

distributed and independent for random parameters and hence conclude that future prices

cannot be projected by using historical information and trends. This theory assist to have

an understanding of volatility of asset returns follows random walk or not in an auto

regressive process and either prediction of volatility is possible or not.

1.3.2 Volatility Theories:

Brooks (2008) explained that linear structural and time series models are incapable to

elucidate various important features which are common to much financial data. It can be

explained through these three parameters.

1.3.2.1 Leptokurtosis

Leptokurtosis term means the tendency for returns having distributions that

display fat tails and also surplus peakedness at the mean.

1.3.2.2 Volatility clustering or volatility pooling

This term means that the trend of volatility in the financial markets is shown in

bunches. Mandelbrot (1963) the large returns (of either negative or positive sign)

are expected to pursue large returns and small returns (of either negative or

positive sign) are expected to follow small returns.

1.3.2.3 Leverage effects

Leverage effect show the tendency for volatility to increase more subsequently in

a large price and falls followed by a price climb of the same magnitude.

In short Brooks (2008) found that a very few number of non-linear models are useful for

modeling the financial data. The most famous non-linear financial models are ARCH or

GARCH models used for modeling and forecasting volatility, hence switching models,

13

which permit the behavior of a financial time series to back up various processes at

different points in time. It is the question element that how it may be determined either a

non-linear model may potentially be appropriate for the data set or not? In response to

this query the answer should arrive at least in part from financial theory “a non-linear

model should be applied where financial theory proposes that the relationship between

variables should be such which requires a non-linear model”. Any how the linear versus

non-linear preference may be ended partly on statistical basis and decision should base on

the answer that whether a linear specification is enough to explain all of the most

important features of the data set at hand. Here the most important is that which tools are

available to identify non-linear behavior in financial time series. There are number of

tests for non-linear patterns in time series that are available to the researcher. While

studying asymmetric patterns in mean and variance support is required to a distribution

that can handle these irregularities and to determine asymmetric models for mean and

variance. These theories motivates for this study on the grounds that such element have

not yet been explored with extended parameters in the markets to be studied.

1.3.3 Asset Pricing Theories

Capital asset pricing model (CAPM), originally introduced by (Sharpe, 1964; Lintner,

1965) based upon the mechanics of mean, variance optimization in (Markowitz, 1952)

has launched a simple and compelling theory of asset pricing for more than 20 years. The

theory predicts that the expected return on an individual asset above the risk-free rate is

proportional to the non-diversifiable risk, which can be measured through the covariance

of the asset return along with a portfolio composed of all the available assets in the equity

market. Chen, Roll and Ross (1986) introduced macroeconomic based risk factor model.

Fama and French (1993) introduced SMB and HML in extension to CAPM in a particular

microeconomic based risk factor model. Carhart (1997) extended the Fama French three

14

factor model by including a fourth common risk factor of momentum factor and estimated

it by taking the average return to a set of stocks with the best performance over the prior

year minus the average return to stock with the worst returns. Volatility theories and asset

pricing theories can be extended in a new modeling approach of conditional volatility and

asset pricing.

1.4 Significance of the Study

(Kulp-Tag, 2008; Mutunga, et al., 2015; Mubarik and Javid, 2016) investigated the risk

and return behavior in emerging economies and explored more flexible models that can

provide better risk estimates than the past recent studies. It is important to study

asymmetric or nonlinear patterns in the variance which is closely related to the stability of

emerging markets. Further unstable markets should be investigated, where shocks are

likely to have a more outstanding effect that leads towards greater influence of the shock.

It is important to introduce not only to information variables or impulses, but also to

introduce flexible asymmetric models for mean and variance for the purpose of estimating

the volatility of the returns correctly whereas (Kulp-Tag, 2007; Rashid and Ahmad, 2008;

Tripathy and Garg, 2013) not focused the broader prospective. The present models for

predicting risk provide assistance to some extent, but the complexity in financial time

series data makes it thorny.

Firstly the study also focus on appraise linear and nonlinear models for the variance in

the perspective of forecasting performance. The interest in finding a volatility model

that can describe the data series most correctly the ability to make the best possible

projections of future risk. Secondly this study also support the introduction of

nonlinearities models and explanation of asymmetries for the purpose of identify and

development of better models. One of the most significant elements of this study is to

examine the asymmetric mean-reverting behavior of both mean and variance on the

15

emerging markets in order to investigate asymmetric patterns not only in the variance,

but also in the mean. It also compare mean reversion pattern of negative returns and

positive returns. Where negative returns also tend to result in higher volatility, meaning

negative returns are producing higher risk than positive returns of the same magnitude

as (Nam, Pyun and Avard, 2001; Nam, Pyun and Arize, 2002) desires to explore

asymmetric patterns in the mean and the variance in modeling of financial time series

data. Moreover to probe into the matter of asymmetric market conditions i.e. Bearish

and Bullish. Thirdly the risk-return-information relationship is probed in a new

dynamics which is not expressed by the (Grootveld and Hallerbach, 1999; Yu, 2006;

McMillan and Speight, 2007; Thupayagale, 2010). The concept of asymmetry is needed

to be investigated both in the conditional mean and variance. Lastly the impact of

macroeconomic forces on stock return volatility is needed to study the basis of best

linear or non-linear volatility forecasting model as recommended by (Attari and Safdar,

2013; Omorokunwa and Ikponmwosa, 2014) did not taken into consideration. This study

is significant because it introduces new models of asset pricing based upon volatility by

considering the conditional volatility and non-linear behavior of variations. This study

is quite significant for Investors, Financial analysts, regulators, brokers, financial

institutions, organization management in different domains. It is worth mentioning that

while employing and understanding the stock market volatility in asset pricing that how

much it is beneficial for venturing into a particular stock or portfolio or any policy

decision for better results.

1.5 Contribution of the Study:

Studies on emerging markets have huge thrust regarding to such issues which address

towards the interaction of risk and return forces. This study contributes in the field of

finance in the following manner.

16

It provides an insight about the conditional volatility and nonlinearities in short

run horizon stock returns as well as the in the long run. (Cheong, Nor and Isa,

2007; Ibrahim, 2010; Engle, Ghysels and Sohn, 2013; Ibrahim, 2010).

In almost previous work the predictability have mostly dealt with the behavior of

stock returns in a linear model and the literature lacks issues in asset pricing

regarding to capture the non-linear behavior of stock returns and volatilities

(Rashid and Ahmad, 2008).

This study provides insight about the behavior of risk and return in emerging

markets which is prime area of interest for investors. (Goudarzi and

Ramanarayan, 2011; Tripathy and Garg, 2013).

Study provides the comparison of Linear and Nonlinear Volatility Models for

equity market returns and proposes an appropriate model for volatility in

emerging equity markets. (Kim, Mollick and Nam, 2008; Kulp-Tag, 2008;

Gyesen, Huang and Kruger, 2013; Mutunga, et al., 2015; Mubarik and Javid,

2016).

This study provides insight about the behavior of volatility in stable and unstable

market so that decision makers can take appropriate measures for mitigating

risk. (Koutmos, 1997; Gokcan, 2000; Salman, 2002; Kumar, 2006; Alagidede, 2011;

Tripathy and Garg, 2013; Raza, Arshad, Ali and Munawar, 2015).

This study provides evidence about impact of information asymmetries and

market conditions asymmetries on returns and volatility. (Verhoeven and

McAleer, 2004; Cheong, Nor and Isa, 2007; Liau and Yang, 2008; Zhang and Li,

2008; Ibrahim, 2010).

17

This study proposes a nonlinear volatility based asset pricing model that may help

in optimal decision making in areas of capital investment, financing, merger and

acquisition and equity valuation (Asteriou and Price, 2001).

18

1.6 Objectives of the Research

The objectives of this study are as follows.

1. To compare Linear and Nonlinear Volatility Models for equity market

returns.

2. To study the behavior of volatility in stable and unstable market.

3. To study the Long-run and Short-run behavior of volatility in emerging

equity markets.

4. To explore the impact of information asymmetries on returns and

volatility.

5. To examine the impact of Macroeconomic variables on Stock return

volatility.

6. To develop a volatility based asset pricing model for emerging markets.

19

CHAPTER 2

LITERATURE REVIEW

20

2. LITERATURE REVIEW

The literature review segment presents individual summary, a little description about

relevant methodology, the contribution and brief discussion about the past results

regarding to the relevant studies being held in past or in present. Therefore the present

study may be designed to meet the objectives and present era challenges.

In the first segment review study initiates from existence of nonlinearities in returns

time series. In second segment various studies about volatility, factors for forecasting of

volatilities and related efficiency in performance. The review literature is initiated with

the evidence that different extensions of the traditional ARCH model as (Engle, 1982)

capture the asymmetric behavior in the variance and proposes that the conditional

variance is asymmetric. Moreover, if returns act asymmetrically, it could be possible to

take into consideration contrarian-type strategies in the situation where “loser-stocks”

outperform “winner-stocks”, However studies reveals that stock returns generally revert

more rapidly after negative returns rather than after the positive returns as concluded by

(Sentana and Wadhwani, 1992). The elements and parameter measuring the reverting

behavior and pattern is negative and significant. (Nam, 2003; Nam, Pyun and Arize

2002, 2001) used an Asymmetric Nonlinear Smooth Transition GARCH (ANST-

GARCH) to measure the mean-reversion patterns and possible consideration of

asymmetries in both mean and variance. The asymmetry in the variance refers to the

leverage effect, and the asymmetric aspect in the mean is generally known as the

reverting property of return dynamics. The findings from the research argued that

negative returns usually reverted faster than positive returns.

Koutmos (1997) examined that the emerging Asian equity markets of Philippines, Korea,

Malaysia, Taiwan Singapore, and Thailand act likewise to the developed equity markets

21

regarding to their stochastic property phenomena, volatility clustering and leverage effect.

These outcomes are astonishingly confirmatory.

Nam, Pyun and Avard (2001) investigated the mean reverting behavior and pattern of

monthly stock returns regarding to the indices of AMEX, NYSE, and NASDAQ, by

applying ANST-GARCH models. Study analyses the time varying volatility in return

series and supports to overreaction hypothesis of the study. This particular model shows

the asymmetric patterns of mean reversion as well as risk decimation. The time period is

taken from 1926:01 to l997:12, and results indicated that not only the negative returns

reverts to the positive returns but also faster than positive returns reverse to the negative

returns. However negative returns are actually causing to reduce risk premiums from the

higher predictable volatility. The results support the hypothesis regarding to the market

overreaction. The results indicate that asymmetry is due to the mispricing behavior on the

part of investors who are overreacting to the certain market Good or Bad news. The

results confirms about the arguments for the contrarian strategy of portfolio.

Kulp-Tag (2007) examined the asymmetric behavior regarding to the conditional mean

and variance. Particularly this study modeled short-horizon mean reversion pattern in

mean with an asymmetric nonlinear autoregressive model, along with this phenomenon

the variance is modeled with an (E-GARCH) in the Mean model. In a study of Nordic

stock markets the results indicates that negative returns revert more rapidly to the positive

returns when positive returns generally keep on longer. It further concluded that

asymmetry in both mean and variance can be visualized in all these equity markets and

hence are reasonably alike. Increase in volatility following positive returns after negative

returns is an indication of overreactions in the equity market. So the study revealed that

negative returns cause to increase in variance and positive returns leads towards fall in

variance.

22

Zhang and Li (2008) examined asymmetric aspects in the Stock Market of China. They

anticipated that the stock returns in this equity market show asymmetric dynamics with

negative returns frequently foremost to overreactions. Supplementary it is concluded that

there exist leverage effect in the behavior of volatility and it is true for six ASEAN stock

markets of Philippines, Singapore, Indonesia, Malaysia, Thailand, and Vietnam.

Liau and Yang (2008) evaluated seven equity markets of Asian regarding to the mean and

volatility asymmetric patterns by using daily observation for the January 3, 1994 to

March 31, 2005 and give evidence for asymmetry in mean reversion behavior in these

equity markets. This study is a little attempt to complements the relevant studies by

seeming at the asymmetric mean-reverting behavior by using data following the Asian

crisis.

Ibrahim (2010) examine six ASEAN emerging markets return patterns (Philippines,

Singapore, Indonesia, Malaysia, Thailand, and Vietnam) by applying E-GARCH in mean

model, and famous as AR-EGARCH (1, 1) Model. This study used data from the period

August 2000 to May 2010 and report that these equity markets generally have fast mean

reversion velocity but relatively different dynamics of return patterns. It reveals that there

exist no evidence regarding to the serial correlation in the markets of Thailand and

Singapore. Therefore only the technical trading strategies are relevant for the markets of

Vietnam and Indonesia. By this behavior it may be hypothesized that the emerging

markets follow asymmetric patterns not only in the variance but also in mean.

To meet another challenge for the evaluation of linear versus nonlinear models in terms

of variance forecasting performance. The interest in finding a volatility model that may

elaborate the data series most appropriately lies in the ability to make best possible

forecasting of future risk. The ARCH model of (Engle, 1982) and the GARCH model of

23

(Bollerslev, 1986) have got lots of support, and are used in the development of these

models for the conditional variance.

Engle and Ng (1993) used sign and size bias tests to identify nonlinear or asymmetric

patterns. In different studies the variance is modeled with the linear GARCH as

(Bollerslev, 1986) modeled the variance, and the nonlinear models Quadratic GARCH

(QGARCH) is applied by (Sentana, 1995) the EGARCH by (Nelson, 1991) the GJR-

GARCH by (Glosten, et al., 1993) the TGARCH by (Zakoian, 1994) and Volatility

Switching GARCH (VS-GARCH) is used by (FornariÃ and Mele, 1997). Engle (1982)

investigated linear and nonlinear ARCH effects to evaluate the models. Moreover the

Lagrange Multiplier (LM) test and a modified LM test are used by (Lundbergh and

Teräsvirta, 1999) to evaluate the performance. The results indicate that even though the

tests carried out to provide suggestions that whether asymmetric, nonlinear models

should be applied or not and it cannot closely be decided that which nonlinear model

should be used.

Taylor (2004) concluded that the benefit of such models is that they permit the

characteristics in the variance model to take into consideration the sign and size effects

of historical returns or shocks.

Kulp-Tag (2008) examined how volatility in financial markets is modeled in different

ways. Study investigated empirically that how good these models are for volatility,

including both linear and nonlinear, in absorbing third and fourth moments. Study

investigated the Nordic stock markets, including, Sweden, Finland, Denmark and

Norway regarding to the diverse linear and nonlinear models. Nordic markets results

reveal that a linear model have the ability to be used for modeling the financial series,

However many times nonlinear models perform to some extent better in some

situations and cases. Study concluded that the Nordic markets show asymmetric

24

patterns merely to a certain extent. Generally negative shocks have a major effect on

these equity markets; however these effects are not so strong in terms of absorbing

third and fourth moment. Therefore non-linear models outperform linear ones. These

studies justify that it may also be hypothesized that Non linear models perform better

than linear models in term of volatility forecasting for the markets which is studied in

this research. Further justifications also exist in upcoming literature as well.

The concept of asymmetry is considered in two dimensions: First, asymmetry is

associated to the relationship between the conditional variance and the lagged squared

error term and hence (G)ARCH type modeling and extensions are there. Secondly,

asymmetry is considered in the distribution that is applied in the modeling of the

variance and this is accomplished by introducing asymmetric distributions. Bollerslevs

traditional GARCH model is used as starting point or benchmark to estimate the

variance. Two asymmetric extensions are applied first one is the (Nelson, 1991)

EGARCH and secondly Asymmetric Power ARCH (AP-ARCH) by (Ding, Engle and

Granger, 1993). There are three distributions are combined with these models: the

Normal (Gaussian) distribution, the GED (asymmetric), the Student’s t-distribution

(symmetric).

Kim, Mollick and Nam (2008) employed annual returns producing from overlapping

monthly price indices for the Great Seven equity markets. They used LSTAR, ESTAR

models and identified asymmetry and common nonlinearities in the long run horizon of

stock returns. They concluded that asymmetric nonlinear dynamics creates attraction to a

considerable segment of the expected variations in the long-horizon stock returns.

Moreover it is clear that nonlinear models outperform linear models in sample and as well

as in the out of sample forecasting exercises mostly. It is important to analyze linear and

nonlinear model comparison and performance. This study proposes strong permanence of

25

return vibrancy with nonlinear impulse responses. The results of the study provide

purposeful information for the investors in global stock markets regarding to the design

and investment strategies and justify for inferring the hypothesis that volatility influences

return in a non-linear fashion. To meet the next objective the past studies justify that the

risk-return-information relation have investigated in different domains. Both the

contemporaneous and the dynamic relationship are the matter of interest. The concept

of asymmetry is introduced both in the conditional mean as well as in the variance.

Kumar (2006) analyzed the comparative performance of various econometric forecasting

volatility models in connection with stock and Forex markets of India. He ranked

EWMA on the basis of out of sample predications as superior to the other methods

and concluded that EWMA escort to improve in the volatility forecasting of equity

markets and the GARCH (5,1) is leading method in the Forex market.

Banerjee and Sarkar (2006) studied and examine the existence of long memory in the

Indian equity market returns. Study concluded that even though each day stock returns

are not mostly correlated but still there exist good support of long memory existence

evidence in the conditional variance. Further they found that FIGARCH is superior to all

other GARCH family models in the performance and best-fit volatility model is declared.

Moreover the study concludes that in SENSEX returns the presence of leverage effect is

insignificant therefore symmetric volatility models outperforms as they are expected.

Cheong, Nor and Isa (2007) investigated the volatility behavior in asymmetry and long-

memory sense regarding to the daily returns of the Malaysian equity market for a period

of 1991–2005. Clustering volatility, leverage effect and long-memory behavior of the

volatility are captured through asymmetric GARCH models and by including the realized

volatility in GARCH for the final period. Crosswise the periods, the results indicate that

mixture of symmetric and asymmetric GARCH modeling are fitted for volatility

26

capturing. It is concluded that the existence of long-memory volatility permits us to rank

the degree of market inefficiency, which may also allows to the reject the efficient market

hypothesis in Malaysian equity market. In CGARCH and FIGARCH models the

diagnostic tests indicates better specification with no significance of iid except in the

whole period. Lastly, the GARCH models by including the RV indicate better log-

likelihood but due to the penalty of additional parameter it does not evaluate BIC

criterion.

Leeves (2007) investigated the conditional volatility in Indonesian equity market covering

the Asian crises period. Asymmetric volatility models recommend that all parameters

were found time-varying, along with the inclusion of those which capture asymmetric

response. The study used GJR, NGARCH and AGARCH known as asymmetric models

of conditional volatility on daily stock returns of Indonesian equity market for the period

1990-1999. Results shows that estimations for this period are significant for ARCH and

GARCH effects, however no such significant asymmetric effects exist. Further the results

reveal that the Asian crisis proposes significant asymmetric effects from the shock of

conditional volatility, hence negative shocks create higher volatility than positive shocks.

Ederington and Guan (2010) investigated differentiation regarding to the impact of

likewise large positive and negative return shocks in the equity market of US. EGARCH,

GJR models were applied and results revealed that following large positive shocks of the

returns, models predict larger increase and smaller increase in volatility and how a

negative shock of the same scale falls more speedily. On the other hand asymmetric

econometric models forecast a decrease in volatility preceded by near to zero returns and

prior to the stable market both implied and realized volatility have little change from the

observed levels. By these studies it may also hypothesized that there exist asymmetry in

the variance for emerging markets and negative reactions increases volatility more than

27

positive reactions in emerging markets.

Alagidede (2011) studied return predictability in African emerging stock markets. The

study focused on the behavior of mean, variance and mean reversion patterns. It is

therefore concluded that the single time varying returns can be predicted. Moreover, study

found that volatility clustering, leverage effect and leptokurtosis exist in the data. Further

study found that all African markets provide an evidence of long memory and hence it is

vital indicator of less than perfect arbitrage.

Goudarzi and Ramanarayan (2011) studied BSE- 500 index during the global financial

crisis of 2008-09 by using asymmetric ARCH models regarding to the effects of good

and bad news on volatility. EGARCH and TGARCH models were used to identify the

effects of volatility. The returns behavior of BSE-500 was identified the reaction of

good and bad news asymmetrically. Existence of the leverage effect shows that the

bad news has a greater impact on volatility than good news. Particularly this stylized

fact shows that the innovation sign has a significant impact on the return’s volatility

and the influx of negative news in the equity market created increase in volatility more

than good news and hence it is concluded that bad news in the BSE-500 increases

volatility higher than good news. Such behaviors motivates to hypothesized that

information asymmetries may have impact on volatility and returns

Tripathy and Garg (2013) studied stock market volatility prediction of six emerging

economies by using daily data from January 1999 to May 2010. The used ARCH,

GARCH, TGARCH GARCH-M and EGARCH models. However it is concluded

that there prevails positive association between risk and stock return in Brazilian

stock market only. Results reveal that volatility shocks are significantly consistent

in all economies. Moreover asymmetric GARCH models shows sound existence of

asymmetric effects in stock returns in all equity markets. Results indicate the presence of

28

leverage effect in the stock returns series and shows that bad news creates higher impact

on volatility. Further this study reveals that the rise in volatility is excessively with

negative shocks in the return series. From such behaviors in markets it may also

hypothesized for the underlying study the returns of the emerging markets follow

asymmetric pattern in mean in which positive returns are followed by more positive

returns but negative returns revert to positive returns faster that positive reverts to

negative returns.

Gyesen, Huang, Kruger (2013) evaluated the predictive ability of the returns of

Johannesburg Stock Exchange by taking macroeconomic variables through linear and

nonlinear models. They used, ARMA, Markov switching, Dynamic Regression models,

asymmetric models of GARCH and EGARCH to capture the conditional heteroskedasity.

They found that the most significant model is Markov switching model in sample fit and

further EGARCH and Two-state model Dynamic Regression is the most significant for

out of sample fit. They found that the predictive performance of nonlinear models is not

better than linear models due to crises period which indicates the advantage of nonlinear

considerations of conditional volatility lessens for the sample period. Mutunga, Islam and

Orawo (2014) investigated the existence of leverage effects in the daily returns series of

NIKKEI-225 and S&P-500 for the period 2008 to 2011 by EGARCH and GJR-GARCH)

with Maximum Likelihood Estimate and EF approaches. Recently (Raza, Arshad, Ali

and Munawar, 2015) estimated and predicted the volatility of KSE-100 returns through a

number of GARCH family models by taking a period from June, 2002 to May, 2013.

They used ARMA specifications and tried to identify the best fitted GARCH model for

volatility forecast. They applied GARCH, GJR-GARCH, EGARCH and APARCH

models and concluded that GARCH (1,1) is fitted model with student’s t- distribution

and with GED, GARCH(1,1) is best prediction model and same with EGARCH(1,1).

29

Sharma and Vipul (2015) compared the forecasting ability of the recently proposed

GARCH model by using daily returns with standard GARCH model and predicted the

conditional volatility of sixteen equity markets indices for the period of fourteen years.

They found that relative predicting performance of the GARCH and EGARCH models

based on realized volatility is more sensitive to the selection of the loss criterion. EWM

with realized measures generally outperforms the Realized GARCH model in out-of-

sample forecast and results provide robustness. In a recent study (Albuquerque,

Eichenbaum, Luo and Rebelo, 2016) proposed a simple theoretical framework for asset

pricing in which the central role is played by demand shocks. Such shock increases

valuation risk but permit the model to take into consideration the key asset pricing

element such as equity premium, Moreover express weak correlations between the

fundamentals and stock returns. Mubarik and Javid (2016) investigated the volatility

forecasting by using GARCH family models for KSE market from the period July 1998 to

June 2011. They used error statistics of these forecasts to measure the performance of the

model. They identified that only EGARCH model has negative leverage effect from all

asymmetric models and bad news cause to decrease the volatility and good news cause to

increase in the volatility. The asymmetric models perform better in Pakistan than the

symmetric model in out-of sample forecasting context. Huynh (2017) tested the

conditional asset pricing models in the international equity markets and concluded that by

taking new instruments to capture the time variation in risk exposure can significantly

decrease the bias element in unconditional alpha. To explore the explanatory variables

this study has further reviewed the literature to identify the gaps in the previous literature.

Asteriou and Price (2001) studied the impact of politically instability on economic growth

of United Kingdom during the period 1961 to 1997. They used GARCH-M model along

with six variables that quantify political instability and analyzed its impact on growth.

30

The outcomes revealed that negative effects of instability exist on growth and have

positive impact on growth volatility.

Salman (2002) applied GARCH-in-mean model for the risk-return relationship, and

concluded that both risk and return are integrated with information given to the market

participants. This relationship has been investigated with various methods; employing

traditional regression analysis, GARCH applications and co-integration analysis. It is

inferred from such studies that it may also hypothesized there exist a significant positive

relationship between risk and return.

Lee and Rui (2002) investigated the dynamic relationship between trading volume and

stock returns on a daily basis. Mestel, Gurgul & Majdosz (2003) explored the

association among stock returns, volatility of the returns and trade volume. The results

indicate weak support for both the contemporary and the dynamic relationship between

stock returns and trading volume on the Austrian market. However in a similar paper

(Gurgul, Majdosz and Mestel, 2005) provided no evidence regarding to the relationship

of stock returns and trading volume for the Polish market. Glosten et al. (1993) argue

that an interest rate variable in the variance equation reduces the persistence, and

interest rates are of helpful in modeling and predicting volatility. There has not been

any proof of interest rates providing better predictions; adding interest rates into the

mean equation does not give better models, but benefits in the volatility modeling

seems to be present. Glosten et al. (1993) further stated that with the help of nominal

interest rates, it could be possible to forecast periods of relatively large excess returns

and significantly less volatility. This alone supports the use of interest rates as an

information variable for the variance. Batra (2004) examined the volatility pattern in

Indian stock markets regarding to the asymmetric behavior and time varying volatility of

stock returns by using GARCH model. The study analyzed impulsive shifts and the

31

likelihood of chance of these rapid shifts in volatility along with the important political

and economic events even in inside and outside of the country. Moreover the study

evaluated various cycles in the equity market regarding to the variation in duration,

amplitude and volatility in terms of bear and bull stages for the concerned period.

Results show that the stock market liberalization has no any direct inference on the

volatility of stock returns. Further results indicate that there exist no structural changes

around liberalization event and the time period regarding to the volatility breaks in

trading transactions. It is found that the bull phase is longer and the bull amplitude

phase is greater. Moreover in these phases the magnitude of volatility is also higher.

Finally, the results reveal that that volatility has been decreased after the liberalization

stage for bear and bull phases of the equity market cycles. Hence it is concluded that

the equity market cycles influence the volatility in different dynamics regarding to the

bull phase phenomenon.

Leduc and Sill (2007) used an equilibrium model to evaluate the significance of monetary

policy for the time prior to 1984 as a result of decline in United State inflation and output

volatility. Results indicate that monetary policy is playing an important role in decreasing

the inflation volatility, however it played a little role in decreasing real output volatility.

Econometric model indicate that the decrease in real output volatility due to smaller TFP

shocks. Under an optimal monetary policy they further investigated the pattern of output

and inflation volatility and found that real output volatility would have been somewhat

lower, and inflation volatility substantially lower.

Kulp-Tag (2008) investigated the relation between return, volatility with volume and

interest rates as impulse variables. The concept of asymmetry in returns and volatility

in this study contributes to the current literature. The risk-return-information

relationship is investigated on the S&P 500 index for daily data. Hence the results

32

suggest that interest rate is not an important information parameter for modeling the

volatility. Asymmetry in mean is modeled with a piecewise regression to take into

consideration the asymmetric autocorrelation in the mean. Asymmetry in mean appears

to be of some significance in modeling conditional mean and variance.

Engle and Rangel (2008) used macroeconomic factors to found volatility their study

concluded that inflation, GDP growth, and short term interest rate are significant

expounding variables that cause to an increase in the volatility. They concluded that

inflation and growth of output are significant positive determinants of volatility.

Engle, Ghysels and Sohn (2013) studied the relationship between macroeconomic forces

and stock market volatility by applying latest set of component models that differentiate

short-run movements from secular actions. They formulated industrial production growth

and inflation to drive the long- term component of model. Hence, it is concluded that

adding economic factors into volatility models performs well in terms of long run

predictions. Further they found that at every day level, industrial production growth and

inflation take into consideration between ten percent and thirty five percent of one day

forward volatility projection. Consequently, the study inferred that macroeconomic

fundamentals play an important even at short horizons in capturing the volatility. It is

concluded that the macroeconomic forces have the ability to capture the volatility in long

run as well as in short run dynamics. These studies justify for hypothesizing that

macroeconomic variables are significant information parameter for modeling the

volatility.

Sangmi and Hassan (2013) evaluated the macroeconomic variables impact on the stock

price behavior and volatility of the Indian equity market. Their study concluded that

there exist a significant relationship between equity market fluctuations and

macroeconomic variables of inflation, interest rate, exchange rate, gold price, money

33

supply and industrial production. Attari and Safdar (2013) used EGARCH model to

generate volatility from KSE return series and identified GDP, Inflation and interest rate

as the key determinants of volatility in Pakistan.

Omorokunwa and Ikponmwosa (2014) examined the relationship between volatility of the

stock market and macroeconomic variables such as GDP, exchange rate, interest rate and

inflation. Empirical evidence is taken for the period of 1980 to 2011 by applying GARCH

model. GARCH model capture the non-linear effects because volatility influence return in

a non-linear fashion. It is the point that we may too hypothesize that the same behavior of

volatility is reflected by KSE returns. They concluded that price behavior in Nigeria is

volatile and the historical information has impact on stock market volatility in Nigerian

equity market. Hence they concluded that exchange rate and interest rate have effect on

stock price volatility in a weak manner and inflation is the major determinant in Nigerian

stock price volatility. They suggested that inflation element should be taken into

consideration in the proper design of targeted monetary policy by taking into the stock

market perception of policies. In finance arbitrage pricing theory guides the relationship

between macroeconomic variables and stock return.

d’Addona and Giannikos (2014) modeled asset pricing with business cycles in regimes

switching in mean and variance equation. They identified model has predictability power

and reports significant results. Further they realized and identified the modeling of

macroeconomic risk in such kind of models. It is evidence that Macroeconomic

variables are significant information parameter for modeling the volatility and this

hypothesis can be established for asset pricing in the emerging economies.

Herskovic, Kelly, Lustig, and Van-Nieuwerburgh (2016) identified that idiosyncratic

volatility leads a strong factor structure for pricing the common factors in idiosyncratic

34

volatility for shocks. Lowest idiosyncratic volatility beta (Systemic Risk) has greater

earning capacity than the highest idiosyncratic volatility beta. Therefore this particular

element of idiosyncratic volatility assists to express the anomalies of asset pricing

modeling as well. Epstein and Ji (2013) volatility and drift is modeled with a utility

approach in a continuous time frame of reference and extension is made in asset pricing

theory with arbitrage free rule, based upon arguments of hedging approach and sharp

predictions can be attained by assuming preference maximization and equilibrium.

Demir, Fung, and Lu (2016) elaborated the performance of CAPM under a general

equilibrium model, can be enhanced significantly by applying conditional consumption

and market return volatilities as modeling factors. Indian market is tested through

portfolios selected by size and book-to-market equity ratio point of view. Conditional

volatility has very low effect on companies having large capitalization than small-growth

and small-value based firms.

Kim and Kim (2016) modeled asset pricing and found strong evidence of Inter-linkages

among the volatilities of 6 equity markets of United States and rejected the null

hypothesis of constant volatility for the capital asset pricing model in the period of

financial crises. The (Brooks and Persand, 2003; Yu, 2006; McMillan and Speight, 2007)

used VaR techniques in the computation of stock return volatility in the Asian emerging

markets. They identified that VaR is significant parameter for volatility modeling.

However, there seem a lot of gap in existing literature with respect to VaR measurement

in various equity markets. Thupayagale (2010) analyzed the prediction performance by

using GARCH model in context with Value-at-Risk estimation by using stock return data.

The results reveals that models with asymmetric effects and having long memory are

important in considering the provision of improved VaR estimates and can escape from

losses in trade. Moreover the results indicate that it can be used to forecast for out-of-

35

sample. It is an important parameter in the computation of Value-at-Risk for derivation of

exact asset-return volatility estimations. It is inferred from the study that Value at Risk is

significant information parameter for modeling the volatility. It may hypothesize that the

same behavior prevails in the equity market dynamics of KSE. Volatility and asset pricing

remained always a hot cake in financial modeling in various context and testified

volatility in the domain of various risk anomalies and firm factors as (Grootveld and

Hallerbach, 1999) indicated that semi-variance is same like to variance but it considers

only values below the average value. This element refines the problems of asymmetry and

known as downside risk. This element can be used to eliminate the probability of loss for

the portfolio. More over this approach considers the element of lower partial moment that

can be tested for empirical financial time series. It is inferred from the study that

downside risk is significant information parameter for modeling the volatility.

Critically the literature review observed the past and recent studies based upon the

dynamics of conditional volatility studies and their relevance to asset pricing in

emerging and developed economies. It is seen that their exist nonlinearities in returns of

the financial time series and volatility can be predicted and the performance of related

efficiency can be measured. Further it is evident that GARCH family models can

capture the asymmetric behavior in the variance Moreover a stochastic property

phenomenon prevails regarding to the volatility clustering and leverage effect in

emerging markets. Empirical analyses also supports to the overreaction hypothesis of the

study and negative returns are actually causing to reduce risk premiums from the higher

predictable volatility. In short it is seen that asymmetry is due to the mispricing behavior

on the part of investors who are overreacting to the certain market Good or Bad news and

this fact confirms the arguments for the contrarian strategy of portfolio. Numerous

studies argue for asymmetric, nonlinear models and majorly results supports that non-

36

linear models outperform linear ones. It is also inferred from the past studies that there

exist a significant positive relationship between risk and return. Further studies also

support with some evidences that information variables suck like macroeconomic

variable, business cycles, trade volume, market liberalization can be helpful in

modeling and predicating the volatility but there exist a sound gap regarding to the

modeling of conditional volatility with reference to the macroeconomic forces, market

conditions asymmetries, value at risk, and downside risk for asset pricing

determination.

37

HYPOTHESES OF THE STUDY

H1. The emerging stock markets follow asymmetric patterns not only in the variance,

but also in the mean.

H2 There exist asymmetry in the variance for emerging markets and negative

reactions increases volatility more than positive reactions in emerging markets.

H3. The returns of emerging markets follow asymmetric pattern in mean in which

positive returns are followed by more positive returns but negative returns revert

to positive returns faster than positive reverts to the negative returns.

H4: Non linear models perform better than linear models in terms of volatility

forecasting.

H5. Information asymmetries have impact on volatility and returns.

H6: Macroeconomic variables are significant information parameter for modeling the

volatility.

H7: There exist a significant positive relationship between risk and return

H8: Volatility influence return in a non-linear fashion.

38

CHAPTER 3

DATA AND METHODOLOGY

39

3. DATA AND METHODOLOGY

Banco Bilbao Vizcaya Argentaria (BBVA) held research in November 2010 and

identified key emerging markets in the world with a new economic concept. Banco

Bilbao Vizcaya Argentaria classified emerging markets into two sets of developing

economies which are as under.

3.1 Emerging and Growth-Leading Economies (EAGLEs)

EAGLEs are defined as emerging economies where the expected incremental GDP would

be larger than the average of the Great Seven (G7) economies in subsequent 10 years, but

not including the USA. Three Asian markets from this group i.e China, India, and

Indonesia are taken for this study.

3.2 NEST: Next to EAGLEs

NEST are defined as emerging economies where the expected incremental GDP is lower

than the mean value of the Great Six economies (G7 excluding the USA’s) but higher

than the average value of the Italy. Three Asian markets from this group i.e Bangladesh,

Malaysia, and Pakistan are taken for this study.

So this study includes six emerging markets from above these two groups which include

China, India, Indonesia, Bangladesh, Malaysia and Pakistan, The data is comprised of

daily prices for the period Jan 4, 2000 to Dec 30, 2010. Stock indices data is taken from

Yahoo Finance and the relevant websites of the equity markets. Eviews-8 and Ox

Metrix-6 Software has been used to test the data.

3.3 Stock Market Returns

Stock market returns are computed by using the following equation.

𝑆𝑟𝑡 = 𝑙𝑛(

𝑝𝑡𝑝𝑡−1

⁄ ) (1)

Srt = Stock Returns

Pt = Closing Price of Stock indices at time t

http://en.wikipedia.org/wiki/Emerging_and_Growth-Leading_Economies

40

Pt-1 = Closing Price of Stock Indices at 1 time before.

3.4 METHODOLOGY

To meet the objectives of the study, methodology section covers the core dimensions of

the study area and introduces a new econometric approach to model the volatility for

asset pricing in an emerging market scenario.

The volatility in equity markets has been studied from various domains. Some are

focused on linear relationship and some focus on nonlinear relationship.

Malkiel and Fama (1970), and Fama (1991) developed approach to focus on the

Random Walk Hypothesis. When using linear setups for modeling time series data as

for example stock market indices, it is assumed that the series are normally distributed,

or that the logarithmic series, are normally distributed.

The return rt should behave as a random variable with variance δ2 and mean equal to µ

or; rt ~ N µ, δ2. Prices should follow a random, and tomorrow’s price should be

possible to predict from the price today and the information available today;

3.4.1 Sign and Size Bias Test

To study the behavior of volatility in stable and unstable market sign and size bias test is

used. Sign bias test is used to test the either historical positive and negative shocks have a

diverse impact on volatility. In first instance we get the residuals value from the

symmetric GARCH model and then we go for sign bias test in the given below regression

of the squared residuals.

휀�̂�2 = 𝜆0 + 𝜆1 𝑆𝑔𝑛−

𝑡−1+ 𝜇𝑡 (2)

𝑊ℎ𝑒𝑟𝑒 𝑎𝑠 𝑆𝑔𝑛−𝑡−1

= 1 𝑖𝑓 휀�̂�−1 < 0 𝑎𝑛𝑑 𝑆𝑔𝑛−𝑡−1

= 0 otherwise

Sign bias testing involves t-test for coefficient 𝜆1 : However if positive and negative

shocks have diverse effect on volatility then the coefficient 𝜆1 will be statistically

significant.

41

Whether volatility depends upon sign and size of past shocks a sign and size bias test is

used based upon the following regression.

휀�̂�2 = 𝜆0 + 𝜆1 𝑆𝑔𝑛𝑧−

𝑡−1+ 𝜆2 𝑆𝑔𝑛𝑧−

𝑡−1휀�̂�−1 + 𝜆3𝑆𝑔𝑛𝑧+

𝑡−1휀�̂�−1 + 𝜇𝑡 (3)

𝑆𝑔𝑛𝑧+𝑡−1

= 1 − 𝑆𝑔𝑛𝑧−𝑡−1

Null hypothesis for presence of no sign and size bias corresponds to: Ho: 𝜆1 = 𝜆2 = 𝜆3 =

0 . Lagrange Multiplier (LM) test is used to test this element.

3.4.2 The Mean Equation

When we are going to model a variance equation, specifications for the mean equation are

required to be made. While estimating a mean equation model, residuals are mandatory to

model the variance equation for its repossession.

Therefore in this study returns are narrated by the following econometric process:

𝑟𝑡 = Ψ0 + ∑ Ψ𝑖𝑝𝑖=1 𝑟𝑡−𝑖 + ∑ λ𝑖

𝑞𝑖=1 휀𝑡−𝑖 + 휀𝑡 , (4)

Where as Ψ0 is a constant, Ψ𝑖 and λ𝑖are the parameters, 𝑟𝑡is the return at time t and 휀𝑡is

the white noise at time t. Equation (2) is an ARMA (p,q) model that explains returns as

dependent on the previous values of returns and shocks. To select the order of an ARMA

model for each indices and to determine which values of p and q narrates the financial

time series to its best, various combinations of ARMA (p,q) models in different manner is

being estimated. OLS regression is used to estimate. Then estimated variations of Auto

Regressive Moving Average (ARMA) models are matched to one another by visualizing

values of some selected information based criterion. As the Schwarz information criterion

(SIC) deems to provide steady and consistent results, therefore model selection provides

choice and performance by reducing this information criterion.

42

To meet the first objective the study compares the Linear and Nonlinear Volatility

Models for equity market returns and propose an appropriate model for volatility in

emerging equity markets.

3.4.3 Linear Models

The fundamental methods concerned in the estimation of the different financial models,

parameters for an initial period and the usage of these parameters to prior data, thus

composing out of sample prediction. Following are the linear models that may be applied

to test the performance and forecasting: (1) Random walk model, (2) Autoregressive

Model, (3) Moving Average Model, (4) Exponential Smoothing Model.

3.4.3.1 Random Walk Model

The random walk model explains the mechanism of forecasting of stock return volatility

of today is based upon on the observed volatility of yesterday. Rashid and Ahamd (2008)

applied random walk model to test the volatility. The mechanism of Random walk model

can be explained as follows:

𝜎𝑡(𝑅𝑊) = 𝜎𝑡−1 (5 )

Whereas “σt” elaborates the daily volatility as measured in the equation.

3.4.3.2 Autoregressive Model – (AR)

Poterba and Summers (1986) employed investigated the linear model and specified a

stationary AR(1) process for volatility testing of the S&P 500 Index. The simplest and

extensively applied version of the autoregressive model is the first-order autoregressive,

or AR (1), model. The AR (1) process is represented by using following equation.

(1 − ω1𝜆)𝜎𝑡(𝐴𝑅) = 𝜐 + 휀𝑡 (6)

𝜎𝑡 = ω1 𝜎𝑡−1 (7)

Whereas ω1 is the autoregressive parameter, λ is backward shift operator, where as “υ” is

constant term, and εt is the error term at time t.

43

3.4.3.3 Moving Average Model – (MA)

Moving average prediction model takes into consideration the lagged values of the

forecast error to get better the present forecast. Rashid and Ahmad (2008) used MA

model to forecast the volatility. The fist order moving average, or MA (1), takes into

consideration the most recent forecast error and it can be explained as follows:

𝜎𝜏(𝑀𝐴) = 𝑢 + (1 − 𝜓1 𝜎𝑡−1) + 𝜎𝑡−1(𝑀𝐴) + 𝜉𝑡 (8)

Whereas Ψ1 is the moving average parameter whereas “u” is the constant term, and ξt is

the error term at time t.

3.4.3.4 Exponential Smoothing Model (ESM)

Dimson and Marsh (1990) introduced the model of exponential smoothing for the

prediction of return’s volatility:

𝜎𝑡(𝐸𝑆) = 𝜋𝜎𝑡−1(𝐸𝑆) + (1 − 𝜋)𝜎𝑡−1 (9)

As this model indicates that the forecast of volatility is based upon the assumption that it

is a function of the instant past forecast and hence reflect the immediate historical

observed volatility. Therefore smoothing parameter, π, is constrained to be lounge

between 0 and 1. The optimal value of π should be determined empirically. Further to

meet the objective of long-run and short-run behavior of volatility in emerging equity

markets, the below non-linear and asymmetric models is used. Moreover correlation test

is used among the stock returns and further correlation test is used among the conditional

volatilities of the respective markets to identify the degree of relationship.

3.5 Non-Linear Models:

3.5.1 The ARCH Model

Engle (1982) discuss a model known as Autoregressive Conditional Heteroskedasticity

(ARCH) for modeling the variation in the stock prices. This model captures the tendency

for volatility clustering in the given financial time series. The major reason at the back of

44

ARCH model is to grasp time varying variance, and to provide benefits from immediate

past information. Simple first order auto regression can assist to understand the ARCH

model in a dynamic way.

𝑟𝑡 = 𝛼𝑟𝑡−1 + 휀𝑡 (10)

The conditional variance 𝜎2 value can be specified as a function of the historic residuals

of the conditional mean. In simple words if the historical information absorbed in

variance equation then there is a chance that it may improve the prediction. Engle (1982)

proposes the following model,

𝜎𝑡2 = 𝛼0 + 𝛼1𝑟𝑡−1

2 + 휀𝑡 (11)

Where 휀𝑡 ~ 𝑖𝑖𝑑(0,1), Engle (1982) proposes another possible method to parameterize 𝜎𝑡2

in order to grasp the heteroskedastic behavior as,

𝜎𝑡2 = 𝛼0 + ∑ 𝛼𝑖

𝑞𝑖=1 휀𝑡−1

2 , (12)

where 𝛼0 > 0 and 𝛼0 ≥ 0.

3.5.2 The GARCH Model

Bollerslev (1986) introduce GARCH model. The generalized form of ARCH model is

called GARCH model as expressed by (Engle, 1982). GARCH model presents a superior

fit because it considers in a better way with non-negativity constraints. In an econometric

model it requires some numbers of lags to be included. GARCH model is different from

ARCH model because it allows the conditional variance to be modeled by lagged values

along with the historic shocks. Generally GARCH (p, q) model is expressed as following

equation:

𝜎𝑡2 = 𝜓0 + ∑ 𝜓𝑖

𝑞𝑖=1 휀𝑡−𝑖

2 + ∑ 𝜙𝑗𝑝𝑗=1 𝜎𝑡−𝑗

2 (13)

Whereas (p,q) represent the order of the GARCH term as well as ARCH term

respectively. The specified variance term 𝜎𝑡2 is known as conditional variance at time “t”

and 𝜓0 specifies constant element, whereas 𝜓𝑖and 𝜙𝑗 are the parameters and 𝜺𝒕−𝒊𝟐 is the

45

indicator of preceding squared shocks and 𝜎𝑡−𝑗2 indicates previous variances. Brooks

(2008) point out that in most cases GARCH (1, 1), is enough to grab the volatility

clustering. Moreover it is pointed out that higher order is very exceptional used in the

finance studies. GARCH model successfully grasp diverse number of features of

financial time series like volatility clustering and thick tailed returns. The GARCH model

is said to be stationary when the following condition (𝛼 + 𝛽 < 1) is fulfilled. Even if

(α + β = 1) still the process is to be said stationary because the variance is infinite. The

εt is considered to be normally distributed approximately if it is along with an average

value of zero and the time-varying variance is expressed as (εt ~ N (o,𝜎𝑡2)).

3.5.3 Asymmetric GARCH Models

No doubt GARCH models performance is excellent in explaining the volatility, but the

squared residuals behavior is still problematic and an unaddressed issue. So the models

anticipate that the positive and negative shocks of same magnitude have the same effects

on variance. It is seen that due to squaring the prior values of shocks and by performing

this computation the sign of the shocks got lost. Therefore, Asymmetric non-linear

models are introduced to resolve this problem

3.5.4 Threshold GARCH (TGARCH)

To meet the objective of impact of information asymmetries of returns and volatility

TGARCH model is used. Zakoian (1994) proposes the Threshold GARCH (TGARCH)

model which is same like to the GJR-GARCH model. The only differentiating point that

in the model of TGARCH, that the conditional standard deviation is modeled as a

replacement for the conditional variance. So the TGARCH (1,1) model can be describes

in this manner.

𝜎𝑡 = 𝜙 + 𝛹|휀𝑡−1| + 𝛾휀𝑡−1𝐷𝑡−1 + 𝛽𝜎𝑡−1 (14)

Whereas Dt−1 is equal to one if εt−1 < 0 and zero if εt−1 ≥0

46

3.5.5 The EGARCH Model

Nelson (1991) presented the Exponential GARCH model famously known as EGARCH

model. This model is relatively more purposeful and useful than the GARCH model

because it allows different impact on the volatility regarding to good news and bad news

phenomena. Moreover it also allows having higher impact on volatility regarding to big

news. Specifically EGRACH model works in two steps. In first step it takes into

consideration the mean and in second step it takes into consideration the variance

component. EGARCH (p, q) model can be expressed in the following manner:

𝑙𝑜𝑔(𝜎𝑡2) = 𝜙 + ∑ 𝜙𝑗

𝑝𝑖=1 |

𝜀𝑡−𝑗

𝜎𝑡−𝑗| + ∑ 𝜆𝑖

𝑞𝑖=1 𝑙𝑜𝑔(𝜎𝑡−𝑖

2 ) + ∑ 𝜔𝑘𝑘𝑖=𝑖

𝜀𝑡−𝑘

𝜎𝑡−𝑘 , (15)

Whereas 𝜙, 𝜆, and 𝜔 shows parameters for conditional variance estimation and 𝜆𝑖

indicates the effect of the prior period measures on the conditional variance. Positive

value of 𝜆𝑖 indicates that a positive change in the equity price is associated with more

positive change and vise versa. The Coefficient 𝜙𝑗 measures the impact of last period

information and expresses the preceding standardized residuals impact on existing

volatility. The term 𝜔𝑘 shows asymmetric effect in the variance and the negativity in 𝜔𝑘

indicate that bad news has higher impact on stock return volatility rather than good news.

EGARCH model show logarithmic time varying conditional variance in which concerned

parameters are allowed to be negative. Therefore this element indicates that the EGARCH

model does not need any non-negativity limits in the stated parameters. This feature

makes the model more attractive than general GARCH model. If (λ < 1), then it indicates

the stationary constraint for an EGARCH (1, 1) model. In the state of symmetry, where

the quantity of positive and negative shocks has equal impacting on the variance, then (ω

= 0). On the other way if ω < 0 the power of a negative or positive shock rationales the

variance to increase or decrease, and if ω > 0 positive and negative shocks rationale the

47

variance to increase or decrease respectively. The natural logarithm of conditional

variance is modeled in the EGARCH (1,1), and can be calculated as,

ln(𝜎𝑡2) = 𝑎 + 𝜔

𝜀𝑡−1

𝜎𝑡−1+ λ |

𝜀𝑡−1

𝜎𝑡−1 − √

2

𝜋 | + 𝛽ln (𝜎𝑡−1

2 ) (16)

Whereas the parameters a, ω, λ and β are constant parameters,

3.5.6 GJR-GARCH Model

Glosten, Jagnathan and Robinston (1993) propose the GJR-GARCH, which one is

generally seen as the simplest model for modeling the asymmetries in the conditional

variance. This model has some resemblance with the TGARCH model and contains the

standard GARCH model, However an additional term that can handle and control the

asymmetry in the variance as follows,

𝜎𝑡2 = 𝜙 + 𝛹휀𝑡−1

2 + 𝜔𝐷⁻𝑡−1 휀𝑡−12 + 𝛽𝜎𝑡−1

2 (17)

Where D-t−1 indicates a dummy variable.

D-t−1 is equal to one if εt−1 < 0 and zero if εt−1 ≥0

3.5.7 Volatility Switching Model

Conditions indicate that the parameters included should be non-negative. Moreover, that

the sum of α and β should be less than (α + β < 1). Fornaria and Mele (1997) developed

Volatility Switching (VS) model and it may take benefit of the mean-reversion behavior

in the conditional variance, and can be computed in this manner,

𝜎𝑡2 = 𝜙 + 𝛼𝛹휀𝑡−1

2 + 𝜆𝛾𝐷𝑡−1 𝑣𝑡−1 + 𝛽1𝜎𝑡−12 (18)

Here the Dt−1 parameter has a value of one if εt−1 > 0 and zero if εt−1 = 0, and with a value

of minus one then εt−1 < 0. Determination and asymmetry in the variance is measured

through the parameter εt2/νt−1 = 𝜎𝑡

2 . Poon and Granger (2003) indicated that regime

switching models have fascinated interest recently from the financial markets and reacted

divergently to large and small shocks. The traditional (ARCH) models cannot handle

such facts.

48

3.5.8 Quadratic ARCH (QARCH)

Sentana (1995) discuss Quadratic ARCH (QARCH) model for the volatility. Commonly

the Q-GARCH(1,1) model can be defined as,

𝜎𝑡2 = 𝜙 + 𝜓휀𝑡−1

2 + 𝜆휀𝑡−1 + 𝛽1𝜎𝑡−12 (19)

The quadratic parameter in this model makes it possible to apply second-order Taylor

approximation to analyze the anonymous conditional variance function of the said model.

The parameters ϕ, ψ, λ and β are constants, and to hold the condition of covariance

stationarity to should be there for the model, so ( ψ + β < 1). The individual parameters

ψ and β should be greater or equal to 0, and λ < 4, ϕ ψ is the positivity requirement to

hold the in the variance.

3.6 Econometric Models

3.6.1 Volatility and Return:

To develop a non-linear volatility based asset pricing model, the below econometric

methodology is proposed for modeling the volatility from various perspective as narrated

below in the given methodology to meet the core objective of the study.

This study explores asset pricing on the basis of volatility. The process is explained as

following. GARCH-Mean model permits the conditional mean to depend on its own

conditional variance. If the risk is captured by the volatility or by the conditional

variance then the conditional variance may enter the conditional mean of X t. To

examine the objective of impact of macro-economic variables on stock return volatility.

The following methodology is used.

3.6.1.1 Model 1: Return, Volatility and Macroeconomic Factors

The macroeconomics variable includes CPI, Term Structure of interest rate, industrial

production and oil prices. Data for Macroeconomic variable is on monthly basis for the

period Jan 2000 to Dec 2010 from the Econstats web resources and other available

49

internet resources. The role of macroeconomic variable in determining volatility is

modeled as under.

𝑋𝑡 = 𝑎0 + 𝛽𝑋𝑡−1 + 𝛾𝜎𝑡2 + 𝜋1 (𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛)𝑡 + 𝜋2 (𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑟𝑎𝑡𝑒 𝑐ℎ𝑎𝑛𝑔𝑒)𝑡

+ 𝜋3 (𝐺𝑟𝑜𝑤𝑡ℎ 𝑟𝑎𝑡𝑒 𝑖𝑛 𝑟𝑒𝑎𝑙 𝑠𝑒𝑐𝑡𝑜𝑟)𝑡 + 𝜋4 (𝑂𝑖𝑙 𝑃𝑟𝑖𝑐𝑒 𝐶ℎ𝑎𝑛𝑔𝑒)𝑡

+ 휀𝑡 (20)

Whereas Xt is return for t periods and α0 is constant and β,γ and π are slopes and

coefficient. Whereas Xt , dependent variable σ2t is variance and 휀𝑡 is error term.

ℎ𝑡 = 𝛾0 + ∑ 𝛿𝑖

𝑝

𝑖=1

ℎ𝑡−𝑖 + ∑ 𝛾𝑗

𝑞

𝑗=1

𝜇𝑡−𝑗2 + ∑ 𝜇𝑘𝑀𝑘

𝑚

𝑘=1

(21)

Whereas ℎ𝑡 is variance and Mk is a set of macroeconomic explanatory variables that

might help to explain the variance.

Inflation Rate

The consumer price index (CPI) is used as a proxy for inflation because CPI is used as a

broad-based parameter for computing the average change in prices of goods and services

throughout a specific period.

𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒 = 𝐼𝑟𝑡 = 𝑙𝑛(

𝐶𝑃𝐼𝑡𝐶𝑃𝐼𝑡−1

⁄ ) (22)

Irt= Inflation Rate

CPIt = Closing Value of CPI at time t

CPIt-1 = Closing Value CPI at 1 time before

Interest Rate Change

Treasury bill rates are used as a proxy for the interest rate. Change is computed by log

difference to T- bill rates.

𝐿𝑜𝑔 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑜𝑓 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑅𝑎𝑡𝑒 = 𝐼𝑁𝑟𝑡 = 𝑙𝑛(

𝑇𝐵𝑡𝑇𝐵𝑡−1

⁄ ) (23)

INrt= Interest Rate Change

TBt = Closing T-Bill Price at time t

50

TBt-1 = Closing T-Bill Price at 1 time before

Industrial production Index

Industrial production index is used as a proxy to measure the growth rate in the real

sector. Here the Industrial production is an indicator of overall economic activity in the

economy and can affects on stock return volatility through its impact on expected future

cash flows.

𝐿𝑜𝑔 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑖𝑛 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑎𝑙 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝐼𝑛𝑑𝑒𝑥 = 𝐺𝑟𝑜𝑤𝑡h rate in real sector

= 𝐺𝑅𝑟𝑡 = 𝑙𝑛(

𝐼𝑃𝐼𝑡𝐼𝑃𝐼𝑡−1

⁄ ) (24)

GRrt= Growth Rate in Real Sector = Change in Industrial production Index

IPIt = Closing IPI value at time t

IPIt-1 = Closing IPI Price at 1 time before

Oil Prices

Brent oil prices are used as proxy for oil prices. Rise in oil prices causes to an increase in

the cost of production and hence reduce the earnings of the corporate sector due to

decrease in profit margins or reduction in demand of product. Therefore oil prices may

effect on stock returns volatility.

𝐿𝑜𝑔 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑖𝑛 𝑂𝑖𝑙 𝑃𝑟𝑖𝑐𝑒𝑠

= 𝐿𝑛𝑂𝑃𝑟𝑡 = 𝑙𝑛(

𝑂𝑃𝑡𝑂𝑃𝑡−1

⁄ ) (25)

LnOPr= Log difference in Oil Prices

OPt = Closing Oil Price at time t

OPt-1 = Closing Oil Price at 1 time before

3.6.1.2 Model 2(a) Returns, Volatility and Market Conditions Asymmetries

To model the impact of information asymmetries on returns and volatility, the following

methodologies is used. Further this study explains the dynamics of asset pricing and

51

volatility in the presence of asymmetric market conditions. The econometric model for

said phenomena is provided below.

𝑋𝑡 = 𝑎0 + 𝛽𝑋𝑡−1 + 𝛾𝜎𝑡2 + 𝜃1 (𝐷)𝑡 + 휀𝑡 (26)

Whereas Xt is return for t periods and α0 is constant and β, γ and θ1 are slopes and

coefficient. Whereas Xt , dependent variable σ2t is variance and 휀𝑡 is error term.

ℎ𝑡 = 𝛾0 + ∑ 𝛿𝑖

𝑝

𝑖=1


𝑞

𝑗=1

𝜇𝑡−𝑗2 + ∑ 𝜇𝑘𝐷𝑘

𝑚

𝑘=1

(27)

Dummy 1 Bullish

Dummy 0 Other wise

3.6.1.3 Model 2(b) Returns, Volatility and Market Asymmetries Good

News and Bad News Effect

The specification of the conditional variance equation for TARCH is given by

ℎ𝑡 = 𝛾0 + 𝛾𝜇𝑡−12 + 𝜃𝜇𝑡−1

2 𝐷𝑡−1 + 𝛿ℎ𝑡−1 (28)

Where Dt take the value of 1 for 휀𝑡<0 and 0 otherwise. So it is very clear that good news

and bad news have a diverse impact. Good news has an impact γ, while bad news has an

impact of γ+θ, if θ>0 it indicates that there is asymmetry. On the other hand if θ=0 the

news impact is symmetric.

Dummy 1 Good News

Dummy 0 Other Wise

3.6.1.4 Model 3 Returns, Volatility, and Value at Risk

This study explains the dynamics of asset pricing and volatility in the presence of value at

risk. The econometric model for said phenomena is provided below.

𝑋𝑡 = 𝑎0 + 𝛽𝑋𝑡−1 + 𝛾 (𝑉𝑎𝑙𝑢𝑒 𝑎𝑡 𝑟𝑖𝑠𝑘)𝑡 + 𝜇𝑡 (29)

Whereas Xt is return for t periods and α0 is constant and β and γ are slopes and

coefficient. Whereas Xt , dependent variable and µt is error term.

52

ℎ𝑡 = 𝛾0 + ∑ 𝛿𝑖

𝑝

𝑖=1


𝑞

𝑗=1

𝜇𝑡−𝑗2 (30)

Value at Risk (VaR) is a widely applied risk measure for the risk of loss against specific

portfolio of financial assets, probability and time horizon. Value at risk (VaR) measures

the worst expected loss under normal market conditions for a specific time interval at a

given confidence level. Value at risk answer to the question that how much can I lose

with x% probability over a pre-set horizon Jorion (1996).

3.6.1.5 Model 4: Returns, Volatility, Semi-variance

Above stated model are related to total risk as a measure of risk. The total risk is captured

by using S.D which demonstrates above means and below mean value. Investor

appreciates above mean market risk but concerned about downside risk deviation. So the

downside risk is captured by using the following relationship.

𝑋𝑡 = 𝑎0 + 𝛽𝑋𝑡−1 + 𝛾 (𝑆𝑒𝑚𝑖 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒)𝑡 + 𝜇𝑡 (31)

Whereas Xt is return for t periods and α0 is constant and β, and γ are slopes and

coefficient. Whereas Xt, dependent variable ht is variance and µt is error term.

ℎ𝑡 = 𝛾0 + ∑ 𝛿𝑖

𝑝

𝑖=1


𝑞

𝑗=1

𝜇𝑡−𝑗2 (32)

Semivarance is a measure of the dispersion of all observations that fall below the average

or target value of a particular data set. The method for semi-variance computations is as

follows:

𝑠𝑒𝑚𝑖𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 =1

𝑛∑ (𝐴𝑣𝑒𝑟𝑎𝑔𝑒 − 𝑟𝑡)2

𝑛

𝑟𝑡<𝑎𝑣𝑒𝑟𝑎𝑔𝑒

(33)

Whereas,

n = Total number of observations below the mean, rt is observed value and average is the

mean or target value of the data set. It is a useful tool in portfolio or assets analysis, semi-

53

variance provides a measure for downside risk. Whereas standard deviation and variance

are the measures of volatility but semi-variance only looks at the negative fluctuations of

an asset. For risk averse investors, the optimal portfolio allocations can be achieved by

minimizing the semi-variance that would limit the likelihood of a large loss.

54

CHAPTER 4

RESULTS AND DISCUSSION

55

4. Results and Discussion

Models expressed in Chapter No. 3 are adopted in this empirical study to explain the

dynamics of returns and volatility. Six emerging market including China, India,

Indonesia, Bangladesh, Malaysia and Pakistan are taken for study as classified in

Chapter No 3. The data is comprised of daily prices for the period 4 Jan 2000 to 30 Dec

2010. In first instance the descriptive statistics explains the behavior of the data as

expressed below in Table 1.

Table 1: Descriptive Statistics for Daily Market Returns;

Period Jan 2000 to Dec 2010

SS BSE JCI DSE KLSE KSE

Mean 0.000274 0.000529 0.000694 0.001056 0.000248 0.000876

Median 0.000154 0.001399 0.001283 7.30E-06 0.000439 0.001332

Maximum 0.090343 0.1599 0.120873 0.282336 0.198605 0.110642

Minimum -0.11304 -0.13794 -0.14726 -0.26907 -0.19246 -0.10097

Std. Dev. 0.017875 0.018715 0.016672 0.015049 0.011773 0.016936

Skewness -0.19992 -0.30136 -0.76478 1.80489 -0.64134 -0.0668

Kurtosis 7.259541 9.662934 12.56576 119.6496 73.08964 7.39603

Jarque-Bera 1810.527 4427.309 9282.658 1347260 486096.5 1913.338

Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Results reported in Table 1 indicate the descriptive statistics for daily logarithmic

returns regarding to the SS (China). BSE index (India), JCI index (Indonesia), DSE

index (Bangladesh), KLSE (Malaysia) and KSE index (Pakistan). DSE and KSE are

generating higher average return but BSE and SS remain is more risky. KSE, BSE,

JCI, KLSE, SS are negatively skewed but DSE is positively skewed. Jarque-Bera

normality test also ensures departure from normality for all market returns.

56

Figure 1: Daily Mean Returns of China, India, Indonesia, Bangladesh,

Malaysia and Pakistan from January 2000 to December 2010

The above returns series are evaluated for heteroscedasticity. However Jarque-Bera

test rejects the null hypothesis of normality and indicates that all the equity market

return series show non-normality. This element exhibits that the series have

tendency of volatility clustering.

Table 2 indicates the number of observations regarding to the negative and positive

returns and summary statistics of returns following each other.

-.3

-.2

-.1

.0

.1

.2

.3

250 500 750 1000 1250 1500 1750 2000 2250

SS BSE JCI

DSE KLSE KSE

57

Table 2: Positive and Negative Returns Summary


Total observations 2376 2376 2376 2376 2376 2376

Negative Observations 1170 1080 1076 1099 1115 1074

Positive observations 1206 1296 1300 1277 1261 1302

Two Negative Observations 123 142 142 116 125 131

Two Positive Observations 161 134 159 120 130 160

Three Negative Observations 69 74 74 69 78 64

Three Positive Observations 73 80 74 64 54 73

Four Negative Observations 82 63 63 75 70 62

Four Positive Observations 77 105 103 110 103 108

The above table indicates that the numbers of positive returns are more than

negative returns for all the equity markets; It concludes that that positive return are

more often backed up by other positive returns, rather than in the case for negative

returns. It can be predicted that positive returns are more persistent and negative

returns tend to be revert towards positive returns faster. It summarizes that returns

are asymmetric, and positive returns are more continual when negative returns

reverts in fast manner. For these markets, the difference between the number of

positive and negative observations is clear. The smallest difference between

positive and negative returns can be seen for the Chinese equity market. It indicates

most significant asymmetries in mean-reversion are to be observed other than the

Chinese equity market where four negative observations are greater than four

positive consecutive returns. It might be inferred that BSE, KSE, JCI are more

effective than KLSE, SS and DSE equity markets because the pattern indicates for

KLSE and DSE that the number of three consecutive negative observations are

more than that of three consecutive positive returns. Overall all equity markets have

58

mean reverting behavior. Figure 2 indicates the stock returns of KLSE, BSE, DSE

KSE, SS and JCI individually.

F igu re 2 : S tock Returns o f Equ i ty Marke t s

59

This study requires getting a first instance regarding to the use of linear versus

nonlinear models and how these perform, firstly sign and size bias test is

conducted.

Table 3 below indicates the results of estimations for sign-bias test, a negative size-

bias test, and a positive size-bias test.

Below results of the sign-bias test for the six markets provides significant results

for NSB and PSB as well. Results indicates that asymmetry exist and can be

observed properly in the return series.

Table 3: Sign and Size Bias Tests

Test/Index SS BSE JCI DSE KLSE KSE

SB 0.00001

-0.00009

-0.0000715 0.00009 -0.00044

-0.00002

p-value 0.89890

0.10650 0.1684 0.45180

<0.0001 0.58000

NSB -0.00910

-0.02237

-0.019481 -0.00203

-0.06625

-0.01971

p-value <0.0001

<0.0001 <0.0001 0.72980

<0.0001 <0.0001

PSB 0.00708

0.00803

0.00487 0.01539

0.001815

0.00864

p-value <0.0001

<0.00030

<0.04190

<0.00380

0.6061

<0.0001

5% is significance level

Table 3 indicates the results of estimations for sign-bias test (SB), negative size-

bias test (NSB) and positive size-bias test (PSB) for SS, BSE, JCI, DSE, KLSE, and

KSE. The results are significant overall for negative sign bias (NSB) and for

positive sign bias (PSB) as well except KLSE. This initial element indicates that

nonlinear models gain some support. Results indicate that asymmetry exist and can

be observed in the returns series. Coefficients and p-values for sign-bias test,

negative size-bias test and positive size bias test for the equity returns are reported

at P<0.05. The estimation of the negative size bias test indicates that negative

asymmetry can be seen in the returns series. The result of the positive size-bias test

on the other hand, generating significant estimates for the KSE, BSE, DSE SS and

60

JCI and indicates towards positive asymmetry regarding to these equity markets.

However, for the KLSE market, the hypothesis of positive asymmetries is rejected.

In the same way the results of the negative size-bias test, generates significant

estimates for the KSE, BSE, KLSE SS and JCI and indicates towards negative

asymmetry regarding to these equity markets.

However, for the DSE, the hypothesis of negative asymmetries is rejected.

According to the sign and size bias test above, non linear patterns are expected and

being observed because the return series indicates asymmetric patterns. Lagrange

Multiple test for ARCH effects are conducted to further investigate this matter.

Table 4: Lagrange-Multiplier Test of ARCH Effects for GARCH model

Index SS BSE JCI DSE KLSE KSE

F-STAT 0.492191 0.90406 0.154165 0.029158 0.144049 0.174527

P-value 0.6877 0.4383 0.927 0.9933 0.9335 0.9137

Observed R2 1.478146 2.713655 0.463187 0.08762 0.432798 0.524352

P-value 0.6873 0.4379 0.9269 0.9933 0.9334 0.9135

ARCH (lags)-P-

value

1 0.4719 0.3591 0.9037 0.8897 0.5242 0.7495

2 0.3732 0.7049 0.5033 0.8536 0.9339 0.6399

3 0.6822 0.183 0.9939 0.8522 0.8867 0.6555

Table 4 reports the p-values estimated with Lagrange Multiplier (LM) test for GARCH

model. For each return series the lag length for this model 1, 2, 3 is used. The Lagrange

multiplier test is used to test the ARCH effects. The test examines whether

heteroscedasticity or homoscedasticity can be observed in returns series. It is inferred

from above results that no non linear and asymmetric ARCH effect can be directly seen

from the estimation of the L-M test for GARCH model.

61

Table 5: Lagrange-Multiplier Test of ARCH Effects FOR GJR-GARCH


F-STAT 0.347386 0.309062 0.259157 0.020492 0.075447 0.183947

P-value 0.7911 0.8189 0.8548 0.996 0.9732 0.9074

Observed Rs 1.04346 0.928391 0.778529 0.061578 0.226703 0.552645

P-value 0.7907 0.8186 0.8546 0.996 0.9732 0.9072

ARCH (lags)-P-value

1 0.6134 0.5767 0.8949 0.9901 0.661 0.9138

2 0.4102 0.6833 0.4402 0.8838 0.957 0.5725

3 0.7393 0.5064 0.6819 0.8415 0.8598 0.6395

Table 5 reports the p-values estimated with Lagrange Multiplier (LM) test for GJR-

GARCH model. For each return series the lag length for this model 1, 2, 3 is used. The

Lagrange multiplier test is used to test the ARCH effects. The test examines whether



from the estimation of the L-M test for GJR-GARCH model.

Table 6: Lagrange-Multiplier Test of ARCH Effects FOR EGARCH


F-STAT 1.001568 0.350866 0.02673 0.015085 0.044691 0.147896

P-value 0.3911 0.7885 0.9941 0.9975 0.9875 0.9311

Observed Rs 3.005967 1.053908 0.080322 0.04533 0.134291 0.444355

P-value 0.3907 0.7882 0.9941 0.9975 0.9874 0.9309

ARCH (lags)-P-value

1 0.1529 0.8373 0.8025 0.9272 0.7308 0.5323

2 0.3487 0.9939 0.904 0.9295 0.9953 0.8273

3 0.964 0.3149 0.9548 0.8643 0.9004 0.9275

62

Table 6 reports the p-values estimated with Lagrange Multiplier (LM) test for EGARCH





from the estimation of the L-M test for EGARCH model.

Table 7: Lagrange-Multiplier Test of ARCH Effects FOR VS-GARCH


F-STAT 1.129183 0.240991 0.156607 0.025284 1.251892 0.429145

P-value 0.3359 0.8678 0.9255 0.9946 0.2893 0.7321

Observed Rs 3.388425 0.723974 0.470523 0.075976 3.756063 1.288909

P-value 0.3355 0.8676 0.9253 0.9946 0.289 0.7318

ARCH (lags)-P-value

1 0.9306 0.7238 0.9093 0.9462 0.3392 0.5829

2 0.0911 0.6313 0.499 0.8496 0.6958 0.4701

3 0.4665 0.5476 0.9982 0.8504 0.1036 0.5022

Table 7 reports the p-values estimated with Lagrange Multiplier (LM) test for VS-

GARCH model. For each return series the lag length for this model 1, 2, 3 is used. The

Lagrange multiplier test is used to test the ARCH effects. The test examines whether



from the estimation of the L-M test for VS-GARCH model.

63

Table 8: Lagrange-Multiplier Test of ARCH Effects FOR QARCH


F-STAT 0.378177 0.226075 0.077633 0.046179 0.158372 0.294874

P-value 0.7688 0.8783 0.9721 0.9868 0.9243 0.8291

Observed Rs 1.135905 0.679177 0.23327 0.138763 0.475823 0.885786

P-value 0.7684 0.8781 0.972 0.9868 0.9242 0.8289

ARCH(lags)-P-value

1 0.5774 0.6842 0.9961 0.8225 0.4955 0.7692

2 0.4165 0.6367 0.6554 0.9688 0.9271 0.5206

3 0.6817 0.5939 0.8545 0.7689 0.9878 0.5386

Table 8 reports the p-values estimated with Lagrange Multiplier (LM) test for QARCH





from the estimation of the L-M test for QARCH model.

The LM-test is performed for all six markets, and for different model specifications. It is

inferred from above results that no nonlinear and asymmetric ARCH effects can be

directly seen from the estimation of the L-M Test as reported from Table 4 to Table 8.

This initial inspiration indicates that nonlinear models may not perform better than linear

ones.

From the above statistical results, it is quite difficult to make concluding remarks about

what type of model for the volatility modeling should be used; either linear model provide

close enough predictions, or nonlinear models produce better estimates? To further

analyze the performance of various volatility models, the estimation results of the models

are presented in Table 9 to 13 regarding to the estimates for GARCH, GJR-GARCH,

EGARCH, VS-GARCH and QGARCH.

64

Table 9: Estimates of GARCH (1,1) Model

Statistics Parameters SS BSE JCI DSE KLSE KSE

Mean Equation

α 0.000412 0.001265 0.001277 0.001482 0.000243 0.001337

p-value [0.1466] <0.0001 <0.0001 <0.0001 [0.3234] <0.0001

β 0.032674 0.084549 0.107033 0.101329 0.166872 0.07255

p-value [0.1153] [0.0002] <0.0001 <0.0001 <0.0001 [0.0009]

Variance

Equation

ψ0 0.00001 0.00001 0.00002 0.000000272 0.00004 0.00002

p-value <0.0001 <0.0000 <0.0001 <0.0001 <0.0001 <0.0001

ψ1 0.06041 0.12469 0.13698 0.57618 0.18238 0.16836

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

φ1 0.92403 0.86410 0.81281 0.68657 0.53976 0.77399

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

Diagnostic

Test

AIC- statistics -5.39048 -5.42986 -5.55107 -5.82976 -6.28377 -5.39048

SIC- statistics -5.37833 -5.4177 -5.53892 -5.81761 -6.27161 -5.56946

log likelihood 6406.196 6452.953 6596.896 6927.839 7466.972 6633.161

Table 9 reports results for GARCH model that the coefficient of the conditional mean

equation is significant at p<0.0001 with the exception of SS. ARCH term is significant at

95% confidence interval indicating that past price behavior influence current volatility in

all markets. The GARCH term is also significant at 95% confidence interval which

reports the presence of persistence in volatility. Moreover the coefficient for lagged stock

returns show significance at p<0.05, it indicates that the lagged volatility impact on

current volatility significantly. The coefficient of ψ1 and φ1 are statistically significant at

p<0.0001 representing that the hypothesis regarding to the constant variance model is

rejected. The prominent AIC, Schwarz and the Log Likelihood methods is used for

selecting model.

65

Table 10: Estimates of EGARCH (1,1) Model


Mean Equation α 0.000571 0.000691 0.000718 0.000686 0.0000938 0.001163

p-value [0.027] [0.0104] [0.0141] <0.0001 [0.7117] <0.0001

β 0.032194 0.10651 0.123734 0.0265 0.159429 0.073503

p-value [0.0864] <0.0001 <0.0001 [0.1854] <0.0001 [0.0006]

Variance

Equation

φ -0.27835 -0.50987 -0.79985 -1.20756 -1.3693 -0.99154

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

φ1 0.137566 0.246907 0.215094 0.506732 0.109477 0.285137

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

ω1 -0.0169 -0.09939 -0.1176 -0.22111 -0.12507 -0.08131

p-value [0.0033] <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

λ1 0.978043 0.961016 0.923807 0.890609 0.857828 0.906776

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

Diagnostic

Test

AIC- statistics -5.39321 -5.44409 -5.57206 -5.89131 -6.29023 -5.59198

SIC- statistics -5.37863 -5.42951 -5.55747 -5.87673 -6.27564 -5.5774

log likelihood 6410.436 6470.861 6622.815 7001.931 7475.642 6646.477

Table 10 reports the parameter estimates of the EGARCH (1,1). EGARCH model is used

to study the asymmetric behavior of the prices. The results indicate that there exists

persistence of volatility as coefficient λ1 is significant. The significant value of φ1

indicates that asymmetric behavior exists in the markets. The response of volatility is

adjusting for good and bad news. Bad news creates more volatility in compare to good

news. Similarly size effect is visible from significant value of φ1. It means big change in

price creates more volatility in compare to small change in price. The appropriate model

is selected on the basis of diagnostic test i.e. the model with minimum AIC, SIC and Log

Likelihood value.

66

Table 11: Estimates of GJR-GARCH (1,1) Model

Table 11 reports the parameter estimates of the GJR-GARCH (1,1).

GJR-GARCH Model is used to study the asymmetric behavior of the market. Ψ is

significant and positive which indicates that past price behavior influences current price

volatility. The significant value of β indicates that the volatility once created persistence

and contributes in the volatility of next period. The ω is found significant and persistent

which shows that asymmetric behavior exist in market. It means bad news has more

affect than good news. Market response is higher for bad news in compare to good news.


Mean

Equation

α 0.000305 0.000889 0.000865 0.000966 0.0000705 0.001071

p-value [0.294] [0.0016] [0.0037] <0.0001 [0.7717] [0.0003]

β 0.036869 0.096046 0.128802 -0.00476 0.155966 0.091184

p-value [0.078] <0.0001 <0.0001 [0.8346] <0.0001 <0.0001

Variance

Equation

φ 0.0000055 0.000008 0.00002 0.0000104 0.0000265 0.000019

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

ψ 0.044324 0.064633 0.038942 0.248242 0.010893 0.083506

p-value <0.0001 <0.0001 [0.0015] <0.0001 [0.1645] <0.0001

ω 0.029219 0.122388 0.170652 0.700513 0.234707 0.142353

p-value [0.0002] <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

β 0.923483 0.854324 0.799465 0.691922 0.657546 0.77912

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

Diagnostic

Test

AIC- statistics -5.3923 -5.44164 -5.5687 -5.86963 -6.30102 -5.5934

SIC- statistics -5.37771 -5.42706 -5.55411 -5.85505 -6.28644 -5.57881

log likelihood 6409.353 6467.951 6618.827 6976.186 7488.465 6648.157

67

Table 12: Estimates of VS-GARCH (1,1) Model


Mean

Equation

α 0.00036 0.000742 0.000185 0.001341 0.000666 0.001273

p-value [0.1552] [0.3914] [0.8861] <0.0001 <0.0001 <0.0001

β 0.02661 0.094407 0.106763 0.103877 0.139257 0.075048

p-value [0.2023] <0.0001 <0.0001 <0.0001 <0.0001 [0.0003]

Variance

Equation

φ 0.000004 0.000013 0.000016 0.0000081 0.0000037 0.000012

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

α 0.081748 0.135531 0.139562 0.763083 0.161784 0.170738

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

β 0.908572 0.831729 0.809099 0.652861 0.813071 0.794783

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

λ 0.000002 0.000019 0.00000019 0.00000007 0.0000021 0.000001

p-value <0.0001 <0.0001 [0.3308] <0.0001 <0.0001 <0.0001

Diagnostic

Test

AIC-statistics -5.41556 -5.44161 -5.55017 -5.84487 -6.58894 -5.61231

SIC- statistics -5.40096 -5.42458 -5.53557 -5.83026 -6.57434 -5.59772

log likelihood 6428.857 6460.749 6588.502 6935.088 7820.477 6665.005

Table 12 reports the parameter estimates of the VS-GARCH (1,1). Variance equation

indicates that α is asymmetric and p-value is indicating that past previous behavior

influence current volatility. β is significant and reports persistence of volatility in the

market. It means volatility created in one period is continued in subsequent periods.

Coefficient of λ indicates asymmetric behavior in the market. Poon and Granger (2003)

indicated that regime switching models have fascinated interest recently from the

financial markets and reacted divergently to large and small shocks. The traditional

(ARCH) models cannot handle such facts.

68

Table 13: Estimates of QARCH (1,1) Model


Mean

Equation

α 0.00047 0.000731 0.001289 0.000117 0.000276 0.001543

p-value [0.1118] [0.013] <0.0001 [0.3022] [0.1746] <0.0001

β 0.023449 0.091634 0.106138 0.145004 0.206231 0.071

p-value [0.275] <0.0001 <0.0001 <0.0001 <0.0001 [0.0007]

Variance

Equation

φ 0.0000059 0.000012 0.000015 0.0000013 0.0000602 0.000015

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

ψ 0.064579 0.131899 0.136304 0.597915 0.357793 0.156598

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

β1 0.917371 0.83431 0.813995 0.716089 0.170982 0.793356

p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

λ -0.00012 -0.00198 0.0000002 0.000434 0.000378 -0.00055

p-value <0.0001 <0.0001 [0.3308] <0.0001 <0.0001 <0.0001

Diagnostic

Test

AIC- statistics -5.38764 -5.44724 -5.55017 -6.27862 -6.35666 -5.59183

SIC- statistics -5.37305 -5.43264 -5.53557 -6.26403 -6.34208 -5.57724

log likelihood 6395.746 6466.423 6588.502 7455.584 7551.36 6640.71

Table 13 reports the estimates of QARCH model the significant negative values of the

parameter λ for all markets except JCI. This indicates that nonlinearity exist with

reference to past price behavior. It can be observed that the parameter ψ is larger than

the parameter λ for all series. When this situation holds (ψ>λ), negative reactions

contributes a greater effect on the conditional variance, instead of positive shocks of the

same size. The parameters estimates of the GJR-GARCH reported in Table 10, indicate

that nonlinear model better than the linear ones. The results for the Volatility-Switching

GARCH model shows similar results. The co-efficient parameter ψ is positive for all

return series and is larger than the parameter λ. These findings indicate that small positive

shocks have a larger impact on the conditional volatility than small negative shocks;

however when the reactions are greater in size, then the effect on volatility is in opposite

direction. This element elaborates that large positive shocks contributes to a smaller

69

increase in volatility rather than large shock is negative.

Conditional standard deviation and conditional volatility graphs are shown in Figure 3 to

Figure 14 as depicted below indicates that the variance is not constant element and it is

time varying aspect. Variance can be modeled to predict the stock returns if suitable

parameters are incorporated in variance equation.

70

Conditional Standard Deviation Conditional Variance

Figure 3 : Conditional Standard Deviation of

BSE

Figure 4 : Conditional Variance of BSE

Figure 5: Conditional Standard Deviation of

DSE

Figure 6 : Conditional Variance of DSE


JCI

Figure 8 : Conditional Variance of JCI

.00

.01

.02

.03

.04

.05

.06

.07

00 02 04 06 08 10

Conditional standard deviation

.0000

.0005

.0010

.0015

.0020

.0025

.0030

.0035

.0040

00 02 04 06 08 10

Conditional variance

.00

.04

.08

.12

.16

.20

.24

00 02 04 06 08 10


.00

.01

.02

.03

.04

.05

00 02 04 06 08 10


.00

.01

.02

.03

.04

.05

.06

.07

00 02 04 06 08 10


.000

.001

.002

.003

.004

00 02 04 06 08 10


71

Conditional Standard Deviation Conditional Variance


KSE

Figure 10 : Conditional Variance of KSE


KLSE

Figure 12 : Conditional Variance of

KLSE


SS

Figure 14 : Conditional Variance of SS

.00

.01

.02

.03

.04

.05

00 02 04 06 08 10


.0000

.0004

.0008

.0012

.0016

.0020

.0024

00 02 04 06 08 10


.00

.02

.04

.06

.08

.10

.12

00 02 04 06 08 10


.000

.002

.004

.006

.008

.010

.012

.014

00 02 04 06 08 10


.005

.010

.015

.020

.025

.030

.035

.040

00 02 04 06 08 10


.0000

.0002

.0004

.0006

.0008

.0010

.0012

.0014

00 02 04 06 08 10


72

Figure 3 to Figure 14 indicates the graphical snap of conditional standard deviations and

conditional variance. Here conditional S.D and Conditional variance different behavior

patterns regarding to the volatility shocks over different time periods. To check the

further performance of linear versus nonlinear models in describing stock return

volatility, the study used the out-of-sample forecasts with Random Walk model. The test

is performed on 2376 observation for forecasting comparison. The decision for the

forecast performance of the models is decided on the root mean squared error (RMSE)

approach: The table below describes the actual error computations for linear and

nonlinear models for the described forecasting period. The performance of the

forecasting is judged by using Root Mean Square Error approach.

Table 14: Forecasting Performance of Linear and Nonlinear Models of the

Volatility of Stock Returns

Model Root Mean Error Square

Linear Models SS BSE JCI DSE KLSE KSE

Random Walk 0.017853 0.018710 0.016662 0.015049 0.011763 0.016927

AR(1) 0.017853 0.018710 0.016662 0.015049 0.011763 0.016927

MA(1) 0.017853 0.018708 0.016661 0.015042 0.011763 0.016926

Exponential

Smoothing

0.018153 0.018830 0.016730 0.015111 0.011959 0.016969

Non-Linear Models

ARCH(1,1) 0.017854 0.018715 0.016399 0.015048 .0117740 0.016945

GARCH(1,1) 0.017854 0.018729 0.016678 0.015055 0.011771 0.016936

EGARCH (1,1) 0.017856 0.018711 0.016662 0.015047 0.011771 0.016931

GJR-GARCH(1,1) 0.017853 0.018715 0.016664 0.015043 0.011771 0.016929

VS-GARCH(1,1) 0.017860 0.018722 0.016685 0.015045 0.011781 0.016940

QARCH(1,1) 0.017861 0.018723 0.016670 0.015077 0.011773 0.016951

These models are ranked on the basis of minimum value of RMSE value for first, second

to onward. In linear models the MA (1) model out performs all the others in an out-of-

73

sample forecasting exercise for all stock returns on the basis of RMSE criterion. The AR

(1) and Random Walk Model appear as second best model and the exponential

smoothing model is ranked last.

Within nonlinear models, the GJR-GARCH model is ranked top for KSE, DSE and SS.

No doubt GJR-GARCH model is dominated over EGARCH (1,1) and GARCH Model

for this time period on the basis of RMSE criteria. It is interesting to note that the ARCH

model is ranked top for JCI and KLSE. GJR-GARCH(1,1) model is ranked second for

these markets. For BSE, EGARCH (1,1) model beating all other model in ranking in an

out-of-sample forecasting when the forecasting for whole period. After comparison of

linear and nonlinear models, it is found that that the GARCH, GJR-GARCH are

outperforming among all the models during the whole volatility periods. Even though

non-linear models dominate the linear models because the nonlinear models superiority

is due to the ability to capture nonlinear patterns that can be expected because the return

series shows asymmetric patterns as it is proved in the Table 9 to 13 estimates. It is

concluded that overall GARCH model outperforms among all the other models due to the

best ability to explain the conditional volatility. The below stated table indicates that the

degree of relationship among the volatilities of these equity markets.

Table 15: Correlation Matrix of Stock Returns


SS 1

BSE 0.15 1

JCI 0.13 0.36* 1

DSE 0.02 0.05 0.02 1

KLSE 0.17 0.24* 0.33* 0.03 1

KSE 0.05 0.10 0.08 0.04 0.06 1 *Significant at 0.05 level

Stock return correlations indicate that how the returns are associated among these equity

markets. SS has highest degree of positive correlation with KLSE, BSE and JCI but not

74

quite significant. BSE has highest degree of correlation with JCI and KLSE, significant

at p<0.05. Whereas JCI has highest correlation with KLSE significant at p<0.05 and KSE

has positive correlation with BSE but not quite significant however it indicates that the

returns are moving these two economies in one direction.

Table 16: Conditional Volatility Correlation Matrix

σ2SS σ2BSE σ2JCI σ2DSE σ2KLSE σ2KSE

σ2SS 1

σ2BSE 0.41* 1

σ2JCI 0.32* 0.62* 1

σ2DSE -0.01 -0.01 -0.003 1

σ2KLSE 0.03 0.03 0.06 0.002 1

σ2KSE 0.08 0.09 0.004 0.09 0.004 1 *Significant at 0.05 level

Conditional volatility based upon GARCH Model, correlations indicate that how the

conditional volatility is associated among the equity markets. The volatility of SS is

positively correlated with the volatility of BSE and JCI at p<0.05 but negatively

correlated with DSE. However there is high degree of positive association among the

volatility of BSE and JCI significant at p<0.05 but negative correlation exist between

BSE and DSE. Even JCI and DSE has also negative correlations but not significant.

Where there is high degree of relationship among the conditional volatilities of equity

markets that indicates that shock behave in same direction in these equity markets except

the behavior of DSE with SS, BSE and JCI. It is therefore concluded that the movement

of volatility in EAGLEs markets have positive degree of relationship that determines that

how the positive and negative news or shocks behave in these markets, however the

volatility movements in NEST markets are minor correlated. It is clear evidence that the

returns association does not mean the volatility associations. However if volatilities are

75

associated it can be inferred that returns are associated. So volatility modeling has its own

unique attribution.

After a thorough analysis it is identified on the grounds that GARCH model can be tested

for risk return relationship along with macroeconomic models to have a superior look.

Investors who are risk averse and therefore they require an additional premium as

compensation in order to hold a risky asset. Such premium is undoubtedly a positive

function of the risk which means that the higher the risk then higher the premium should

be, if the risk is captured by the volatility or by the conditional variance, and then the

conditional variance may the conditional mean function as well. GARCH model is linked

here with the macroeconomic variables to capture the effect of risk not by the variance

series but also using the standard deviation of the series which have for the mean and

variance equations because the GARCH models allows us to add explanatory variables in

the specification of the conditional variance equation that can have ability to explain the

variance though macroeconomic explanatory variables.

76

4.1 Econometric Models for China

Table 17(a): Estimates of GARCH in Mean (1,1) Model 1: Return, Volatility and

Macroeconomic Model for SS, Impact of macroeconomic variables on

Return

Statistics Parameters SS

Mean Equation 𝑎𝑜 -0.18445

p-value <0.00001

β -0.05183

p-value <0.00001

γ 3.377293

p-value <0.00001

π1 0.074832

p-value <0.00001

π2 -0.48612

p-value 0.2586

π3 0.003622

p-value 0.1264

π4 -0.05254

p-value 0.022

Variance Equation 𝛄𝟎 -0.00029

p-value <0.00001

δ 0.162614

p-value <0.00001

γ1 0.640814

p-value <0.00001

Diagnostic

Test

AIC- Statistics 30.31179

SIC- Statistics 30.53237

Log- Likelihood -1960.27

Table 17(a) indicates that GARCH in mean model is extended with the macroeconomic

variables in the equation for SS. The conditional mean is significant at p < 0.10. So far

as macroeconomic variables are concerned, inflation is significantly related to return

indicating the presence of short term liquidity effect. Similarly, Oil prices change has

significant negative effect on return and increase in oil price decreases returns of stocks.

Model is selected on the basis of AIC, SIC, and Log Likelihood values.

77

Table 17(b): Estimates of GARCH in Mean (1,1) Model 1: Return, Volatility and

Macroeconomic Model for SS, Impact of macroeconomic variables on

Volatility

Table 17(b) indicates the impact of macroeconomic variables on volatility of market has

also been exercised. The results indicate that inflation is significant positively related to

volatility. In high periods of inflation, volatility is on high side, change in oil prices has


Mean Equation γ0 0.002981

p-value 0.741

δ 0.167467

p-value 0.0643

γ1 -0.102361

p-value 0.9555

Variance Equation 𝑎0 0.045503

p-value 0.1474

β 0.040354

p-value 0.3943

γ 0.844603

p-value <0.00001

π1 0.000439

p-value 0.0035

π2 -0.02885

p-value 0.2038

π3 -0.0096

p-value 0.1469

π4 -0.0007

p-value 0.8221

Diagnostic Test AIC- Statistics -2.13429

SIC- Statistics -1.91371

Log -Likelihood 148.7286

78

also significant impact on volatility. In the period of rising prices volatility is lower it may

be due to anchoring.

Table 18(a): Estimates of GARCH in Mean (1,1) Model 2(a): Return, Volatility and

Market Conditions Asymmetries for SS. Impact of Market conditions

asymmetries on returns.


Mean Equation 𝜶𝟎 -0.01031

p-value <.0.00001

β 0.014674

p-value 0.3441

γ 1.83814

p-value 0.4307

θ1 0.020294

p-value <0.00001

Variance Equation γ0 4.68E-06

p-value <0.00001

δ 0.079233

p-value <0.00001

γ1 0.899044

p-value <0.00001

Diagnostic

Test

AIC- statistics -5.97694

SIC- statistics -5.95991

log likelihood 7095.648

Table 18(a) reports the role of bullish and bearish market.

θ1 value is quite significant and positive which shows that returns are higher in bullish

period. Model is used to study the asymmetric behavior of the market.

θ1 is significant and positive which indicates that past price behavior influences current

price volatility. The significant value of δ indicates that the volatility once created

79

persistence and contributes in the volatility of next period. The γ1 found significant and

persistent which shows that asymmetric behavior exist in market.

Table 18 (b): Estimates of GARCH in Mean (1,1) Model 2(a): Return, Volatility and

Market Conditions Asymmetries for SS. Impact of Market conditions

asymmetries on volatility.


Mean Equation 𝜶𝟎 0.002669

p-value <0.00001

β 0.03673

p-value .1154

γ -1.6252

p-value 0.3876

Variance Equation γ0 0.000109

p-value <0.00001

δ 0.68259

p-value <0.00001

γ1 0.16316

p-value <0.00001

μ1 -0.00011

p-value <0.00001

Diagnostic

Test




Results in Table 18(b) indicate that the negativity of μ1 in bullish effect indicates that

volatility in the bullish market is less than the volatility in bearish market. It means that

in bullish market return are high and volatility is low which offer better risk return

relationship.

80

Table 19: Estimates of TGARCH Model 2(b): Return, Volatility and Market

Asymmetric Conditions, Good News and Bad News Effect.


Mean Equation α 0.00029

p-value 0.3369

β 0.028497

p-value 0.1795


p-value <0.0001

γ1 0.048128

p-value <0.0001

δ 0.029422

p-value 0.0003

θ 0.916989

p-value <0.0001

Diagnostic

Test

AIC- Statistics -5.38909


Log-Likelihood 6397.458

TGARCH (1,1) model is estimated for SS returns series by using Gaussian standard

normal distribution AIC, SIC and maximum Log Likelihood values, and ARCH- LM test

are performed to select volatility model that can best model the conditional variance of

the SS returns series. The estimation result of TGARCH (1, 1) models are shown in Table

19. The significant θ indicates persistence in volatility for long run and hence stable

indicator of an integrated process. Variance equation indicates that ARCH term has

coefficient 0.048128 significant at p<0.0001 and the GARCH term coefficient is

0.029422 significant at p<0.001.

The asymmetric effect captured by the parameter estimate θ is positive and significant in

81

the TGARCH (1, 1) that indicates the existence of leverage effect. After finding the

presence of leverage effects in the series by using TGARCH (1,1). However results

indicated that TGARCH(1,1) model can be a potential representative of the asymmetric

conditional volatility process for the daily return series of SS.

From the estimated TGARCH model, it is apparent that good news has an impact of

0.04128 magnitudes for SS and bad news has an impact of (0.048128+0.916989 =

0.965117). Because the leverage effect is significant and hence it is concluded that the

bad news increases higher volatility in SS more than good news.

Table 20: Estimates of GARCH (1,1)Model 3: Return and Value at Risk for SS


Mean equation α 0.000263

p-value <0.00001

β -3.99e-18

p-value 1

γ -1443.769

p-value 0.2054

Variance equation γ0 1.36e-35

p-value <0.00001

δ 0.6

p-value <0.00001

γ1 0.15

p-value <0.00001

Diagnostic

Test



Log-likelihood 90250.56

Table 20 indicates the relationship of return and the value at risk. GARCH Model is

extended with the Value at Risk in mean equation. The results indicate that the γ1 is

insignificantly related to return. It is inferred that VaR is not significantly related to the

82

returns of SS market. ARCH term is significant at 95% confidence interval indicating that

past price behavior influence current volatility in the market. The GARCH term is

significant at 95% confidence interval which reports the presence of persistence in the

volatility. It indicates that the value at risk is positive but not significant and has no effect

on the price behavior.

Table 21: Estimates of GARCH (1,1) Model 4: Return, Volatility and Semi Variance

for SS


Mean Equation α -0.00083

p-value 0.072

𝛽 0.069171

p-value 0.1021

γ 2.448605

p-value <0.00001


p-value <0.00001

δ 0.63867

p-value <0.00001

γ1 0.159283

p-value <0.00001

Diagnostic

Test




Table 21 indicates the relationship of return, and the Semi-variance. GARCH Model is

extended with the Semi-variance. Semi-variance is downside risk and added into

variance equation. Here semi-variance is significant at p<0.0001 and indicates that results

indicate that as down side risk increases, return also increases. The results indicate that

ARCH term and GARCH term are significant at p<0.0001. Here all the variables for

83

variance equation are statistically significant and the value of the semi-variance is

positive which means if the semi-variance increases it causes to an increase in the return.

4.2 Econometric Models for India


Macroeconomic Model for BSE: Impact of macroeconomic variables

on Return

Statistics Parameters BSE

Mean Equation 𝒂𝟎 0.45

p-value 0.8351

β 0.019134

p-value 0.2465

γ -78.4828

p-value 0.3684

π1 -0.01006

p-value 0.0909

π2 -0.01001

p-value 0.9081

π3 0.001354

p-value 0.9917

π4 0.089325

p-value 0.1947

Variance Equation γ 0.000102

p-value 0.481

δ 0.983127

p-value <0.00001

γ1 -0.005582

p-value 0.3991

Diagnostic

Test



Log Likelihood 151.4774


variables in the variance equation for BSE. The conditional mean is not significant. So

far as macroeconomic variables are concerned, inflation has significant negative effect on

return and increase in inflation decrease returns of the stocks. Model is selected on the

basis of AIC, SIC, and Log Likelihood values.

84


Macroeconomic Model for BSE: Impact Of Macroeconomic Variables

On Volatility



volatility. In high periods of inflation, volatility is on high side, change in interest rate



p-value 0.0008

δ 0.052035

p-value 0.5144

γ1 -6.825557

p-value 0.0034

Variance Equation 𝑎0 -0.02543

p-value 0.0457

β 0.767876

p-value <0.00001

γ 0.000922

p-value 0.4141

π1 0.0000830

p-value 0.0038

π2 -0.02248

p-value <0.00001

π3 -0.01878

p-value 0.0023

π4 0.006992

p-value 0.0005

Diagnostic Test AIC- statistics -2.197952



85

has negative significant impact on volatility. Similarly change in industrial production has

also negative significant impact on volatility. Therefore in the period of rising prices

volatility is lower it may be due to anchoring. However oil price change has positive

significant impact on volatility. Due to high positive change in oil prices volatility is on

high side.


Market Conditions Asymmetries for BSE: Impact of Market Conditions

Asymmetries On Return



p-value <0.00001

β 0.032477

p-value 0.0245

γ -0.154851

p-value 0.939

θ1 0.019659

p-value <0.00001


p-value <0.00001

δ 0.882865

p-value <0.00001

γ1 0.115029

p-value <0.00001

Diagnostic

Test




Table 23(a) reports the role of bullish and bearish market behavior in BSE.

θ1 value is significant and positive which shows that returns are higher in bullish period.

Model is used to study the asymmetric behavior of the market.

86






Market Conditions Asymmetries for BSE: Impact of Market Conditions

Asymmetries On Volatility.



p-value <0.00001

β 0.011061

p-value 0.2685

γ -430.1017

p-value <0.00001


p-value <0.00001

δ 0.008118

p-value 0.6617

γ1 -0.000742

p-value 0.1237

μ1 -0.0000608

p-value <0.00001

Diagnostic

Test







relationship.

87




Mean equation 𝜶𝟎 0.000882

p-value 0.0017

β 0.097318

p-value <0.0001

Variance equation γ0 0.00000795

p-value <0.0001

γ1 0.064636

p-value <0.0001

δ 0.123009

p-value <0.0001

θ 0.854005

p-value <0.0001

Diagnostic

Test



log-likelihood 6458.865

TGARCH (1,1) model is estimated for BSE returns series by using Gaussian

standard normal distribution AIC, SIC and maximum Log Likelihood values, and

ARCH- LM test are performed to select volatility model that can best model the

conditional variance of the BSE returns series. The estimation result of TGARCH (1,1)

models are shown in Table 24. The conditional mean is significant for TGARCH(1,1)

at p<0.10 that indicates persistence in volatility for long run and hence stable indicator

88

of an integrated process. ARCH and GARCH terms are significant at p<0.0001.

The asymmetric effect captured by the parameter estimate θ is positive and significant

in the TGARCH (1, 1) that indicate the existence of leverage effect. After finding the

presence of leverage effects in the series by using TGARCH (1,1). Diagnostic test identifies

the model performance in comparison to other equity markets. However results indicated

that TGARCH(1,1) model can be a potential representative of the asymmetric

conditional volatility process for the daily return series of BSE.


0.064636 magnitudes for BSE and bad news has an impact of (0.064636+0.854005 =


bad news increases higher volatility in BSE more than good news.

89

Table 25: Estimates of GARCH (1,1) Model 3: Return, Volatility and Value at Risk

for BSE



p-value <0.00001

β -0.000235

p-value 0.0019

γ -1444.603

p-value <0.00001


p-value 0.1063

δ 0.677972

p-value <0.00001

γ1 0.309051

p-value <0.00001

Diagnostic

Test





extended with the Value at Risk in mean equation. The results indicate that the γ is

negatively related to return significantly. It is inferred that VaR is significantly negatively

related to the returns of BSE market. ARCH term is significant at 95% confidence

interval indicating that past price behavior influence current volatility in the market. The

GARCH term is significant at 95% confidence interval which reports the presence of

persistence in the volatility. It indicates that the value at risk is negative and has effect on

the price behavior.

90

Table 26: Estimates of GARCH (1,1) Model 4: Return, Volatility and Semi variance

for BSE



p-value 0.0302

𝛽 0.095278

p-value 0.0035

γ -0.328351

p-value 0.4079


p-value 0.0002

δ 0.837336

p-value <0.00001

γ1 0.130067

p-value <0.00001

Diagnostic

Test






variance equation. Here semi-variance is insignificant which indicates no such effect. The

results indicate that ARCH term and GARCH term are significant at p<0.00001. Here all

the variables for variance equation are statistically significant. The value of the semi-

variance is negative but insignificant.

91

4.3 Econometric Models for Indonesia


Macroeconomic Model for JCI: Impact of macroeconomic variables

on Return

Statistics Parameters JCI

Mean Equation 𝑎𝑜 0.053923

p-value 0.2456

β 0.069918

p-value 0.5526

γ -0.591043

p-value 0.3926

π1 0.300407

p-value 0.7789

π2 -0.55417

p-value 0.0009

π3 0.007209

p-value 0.9306

π4 -0.01217

p-value 0.8477

Variance Equation 𝛄𝟎 0.001803

p-value 0.1589

δ 0.313233

p-value 0.2616

γ1 0.334955

p-value 0.0036

Diagnostic

Test





variables in the variance equation for JCI. The conditional mean is not significant. So far

as macroeconomic variables are concerned, change in interest rate has significant

negative effect on return and increase in interest rate decrease returns of stocks. Model is

selected on the basis of AIC, SIC, and Log Likelihood values.

92


Macroeconomic Model for JCI: Impact of macroeconomic variables

on Volatility

Table 27(b) indicates the impact of macroeconomic variables on volatility of JCI market.

The results indicate that GARCH term is significant but no macroeconomic variable have

significant impact on volatility.



p-value 0.0004

δ 0.122628

p-value 0.2156

γ1 -9.534224

p-value 0.028


p-value 0.4675

β 1.051728

p-value <0.00001

γ -0.123546

p-value 0.0018

π1 -0.000451

p-value 0.9897

π2 -0.000156

p-value 0.9647

π3 0.004257

p-value 0.6281

π4 0.000331

p-value 0.9116




93


Market Conditions Asymmetries for JCI: Impact of market conditions

asymmetries on Return.



p-value <0.00001

β 0.058389

p-value <0.00001

γ 4.205766

p-value 0.1239

θ1 0.019622

p-value <0.00001


p-value <0.00001

δ 0.804274

p-value <0.00001

γ1 0.159444

p-value <0.00001

Diagnostic

Test




Table 28(a) reports the role of bullish and bearish market behavior in JCI .







94

Table 28(b): Estimates of GARCH in Mean (1,1) Model 2(a): Return, Volatility and

Market Conditions Asymmetries for JCI: Impact of market conditions

asymmetries on Volatility.



p-value <0.00001

β 0.049836

p-value <0.00001

γ -401.5648

p-value <0.00001


p-value <0.00001

δ 0.001248

p-value 0.901

γ1 0.001248

p-value <0.00001

μ1 -0.0000574

p-value <0.00001

Diagnostic

Test



Log likelihood 7147.294




relationship.

95





p-value 0.0034

β 0.12872

p-value <0.0001


p-value <0.0001

γ1 0.038811

p-value 0.0014

δ 0.16792

p-value <0.0001

θ 0.8019

p-value <0.0001

Diagnostic

Test




TGARCH (1,1) model is estimated for JCI returns series by using Gaussian standard

normal distribution AIC, SIC and maximum Log Likelihood values, and ARCH- LM

test are performed to select volatility model that can best model the conditional variance

of the BSE returns series. The estimation result of TGARCH (1, 1) models are shown in

Table 29. The conditional mean is significant for TGARCH(1,1) that indicates

persistence in volatility for long run and hence stable indicator of an integrated

process. ARCH and GARCH terms are significant at p<0.01 and p<0.0001

respectively.


96


presence of leverage effects in the series by using TGARCH (1,1). Diagnostic test identify



conditional volatility process for the daily return series of JCI.


0.038811 magnitudes for JCI and bad news has an impact of (0.038811+0.8019 =


bad news increases higher volatility in JCI more than good news.

Table 30: Estimates of GARCH(1,1)Model 3: Return, Volatility and Value at Risk for

JCI



p-value <0.00001

β -0.00023

p-value 0.0029

γ -1445.39

p-value <0.00001


p-value <0.00001

δ 0.616544

p-value <0.00001

γ1 0.192066

p-value <0.00001

Diagnostic

Test






97


related to the returns of JCI market. ARCH term is significant at 95% confidence interval

indicating that past price behavior influence current volatility in the market. The ARCH

and GARCH term is significant at 95% confidence interval which reports the presence of


the price behavior.


for JCI



p-value 0.0062

𝛽 0.127384

p-value 0.0002

γ -2.242415

p-value 0.0003


p-value <0.00001

δ 0.73389

p-value <0.00001

γ1 0.115699

p-value <0.00001

Diagnostic

Test








98




4.4 Econometric Models for Bangladesh


Macroeconomic Model for DSE: Impact of macroeconomic variables

on Return

Statistics Parameters DSE

Mean Equation 𝑎𝑜 0.236073

p-value 0.7643

β 0.225684

p-value 0.0441

γ -51.18882

p-value 0.7833

π1 -0.075689

p-value 0.9227

π2 0.124938

p-value 0.9839

π3 0.030717

p-value 0.8098

π4 -0.022235

p-value 0.6752


p-value 0.0378

δ 0.096005

p-value 0.8043

γ1 0.031218

p-value 0.7807

Diagnostic

Test





variables in the variance equation for DSE. The conditional mean is not significant. So

far as macroeconomic variables are concerned, No variable has significant effect on

return. Model is selected on the basis of AIC, SIC, and Log Likelihood values.

99



On Volatility


also been exercised. The conditional mean is significant at p < 0.10. The results indicate

that change in industrial production is significant positively related to volatility. In high



p-value 0.8344

δ 0.18549

p-value 0.281

γ1 0.630617

p-value 0.0809


p-value 0.0255

β 0.281912

p-value 0.3439

γ 0.050296

p-value 0.5335

π1 -0.007829

p-value 0.8639

π2 0.019059

p-value 0.5356

π3 0.02559

p-value 0.0066

π4 0.001387

p-value 0.6658




100

growth period of industrial production, volatility is on high side, Therefore in the period

of high industrial production volatility is higher it may be due to anchoring.

Table 33(a)`: Estimates of GARCH in Mean (1,1) Model 2(a): Return, Volatility and

Market Conditions Asymmetries for DSE: Impact of market conditions




p-value <0.00001

β 0.110589

p-value <0.00001

γ 0.072776

p-value 0.0003

θ1 0.00571

p-value <0.00001

Variance Equation γ0 -0.000000397

p-value 0.413

δ 0.679755

p-value <0.00001

γ1 1.100461

p-value <0.00001

Diagnostic

Test




Table 33(a) reports the role of bullish and bearish market behavior in DSE.


Model is used to study the asymmetric behavior of the market. θ1 is significant and

positive which indicates that past price behavior influences current price volatility. The

significant value of δ indicates that the volatility once created persistence and contributes

101

in the volatility of next period. The γ1 found significant and persistent which shows that

asymmetric behavior exist in market.


Market Conditions Asymmetries for DSE: Impact of market conditions




p-value 0.6576

β 0.254173

p-value <0.00001

γ -0.28069

p-value 0.6848

Variance Equation γ0 -0.00000114

p-value <0.00001

δ 0.638496

p-value <0.00001

γ1 0.760665

p-value <0.00001

μ1 0.0000242

p-value <0.00001

Diagnostic

Test







relationship.

102

Table 34: Estimates of TGARCH Model 4: Return, Volatility and Market




p-value <0.0001

β -0.0044

p-value 0.8468


p-value <0.0001

γ1 0.248296

p-value <0.0001

δ 0.700479

p-value <0.0001

θ 0.691989

p-value <0.0001

Diagnostic

Test




TGARCH (1,1) model is estimated for DSE returns series by using Gaussian



conditional variance of the DSE returns series. The estimation result of TGARCH (1, 1)

models is shown in Table 34. The conditional mean is significant for TGARCH(1,1)

that indicates persistence in volatility for long run and hence stable indicator of an

integrated process.



presence of leverage effects in the series by using TGARCH (1,1). Diagnostic test identifies

103



conditional volatility process for the daily return series of DSE.


0.248296 magnitudes for DSE and bad news has an impact of (0.248296+0.691989 =


bad news increases higher volatility in DSE more than good news.


for DSE



p-value <0.00001

𝛽 -0.000319

p-value 0.1624

γ -1444.617

p-value <0.00001


p-value 0.1435

δ 0.623584

p-value <0.00001

γ1 0.203615

p-value <0.00001

Diagnostic

Test







104

related to the returns of DSE market. ARCH term is significant at 95% confidence




the price behavior.

Table 36: Estimates of GARCH (1,1) Model 4: Return, Volatility and Semi -Variance

for DSE



p-value 0.0612

𝛽 0.041587

p-value 0.5749

γ 0.132842

p-value 0.8353


p-value <0.00001

δ 0.685167

p-value <0.00001

γ1 0.870101

p-value <0.00001

Diagnostic

Test










105

4.5 Econometric Models for Malaysia


Macroeconomic Model for KLSE: Impact of macroeconomic variables

on Return

Statistics Parameters KLSE


p-value 0.0644

β 0.052962

p-value 0.0151

γ 0.086962

p-value 0.3243

π1 -1.40529

p-value 0.3356

π2 -0.064

p-value 0.2548

π3 0.043531

p-value 0.6056

π4 0.067772

p-value <0.00001


p-value 0.3955

δ 0.870101

p-value <0.00001

γ1 0.685167

p-value <0.00001

Diagnostic

Test





variables in the variance equation for KLSE. The conditional mean is not significant. So

far as macroeconomic variables are concerned, oil price has significant positive effect on

return and increase in change in oil price increase returns of stocks. Model is selected on

the basis of AIC, SIC, and Log Likelihood values.

106



Variables On Volatility


also been exercised. Change in industrial production has also positive significant impact

on volatility. Therefore in the period of high industrial growth volatility is on high side.


Mean Equation γ0 -2.653122

p-value <0.00001

δ 0.123042

p-value 0.0018

γ1 0.144553

p-value <0.00001

Variance Equation 𝑎0 -0.000407

p-value <0.00001

β 0.955296

p-value <0.00001

γ 0.147641

p-value <0.00001

π1 0.004064

p-value 0.735

π2 -0.00016

p-value 0.9627

π3 0.013666

p-value <0.00001

π4 0.000542

p-value 0.4442




107


Market Conditions Asymmetries for KLSE: Impact of market conditions




p-value <0.00001

β 0.09896

p-value <0.00001

γ 3.320108

p-value 0.3083

θ1 0.013051

p-value <0.00001


p-value <0.00001

δ 0.447227

p-value <0.00001

γ1 0.392705

p-value <0.00001

Diagnostic

Test




Table 38(a) reports the role of bullish and bearish market behavior in KLSE.







108


Market Conditions Asymmetries for KLSE: Impact of market conditions




p-value <0.00001

β 0.052288

p-value <0.00001

γ -123.455

p-value <0.00001


p-value <0.00001

δ 0.018836

p-value 0.0734

γ1 0.017636

p-value <0.00001

μ1 -0.0000992

p-value <0.00001

Diagnostic

Test







relationship.

109




Mean Equation 𝒂𝟎 7.05E-05

p-value 0.7717

β 0.155966

p-value <0.0001


p-value <0.0001

γ1 0.015143

p-value 0.1645

δ 0.234707

p-value <0.0001

θ 0.657546

p-value <0.0001

Diagnostic

Test




TGARCH (1,1) model is estimated for KLSE returns series by using Gaussian



conditional variance of the KLSE returns series.

The estimation result of TGARCH (1, 1) models is shown in Table 39. The conditional

mean is significant for TGARCH(1,1) that indicates persistence in volatility for long

run and hence stable indicator of an integrated process.



presence of leverage effects in the series by using TGARCH (1,1) the SIC were used to

110

select the best model for returns series. The model with lowest value of SIC fits the

data best. The results are presented in table 39. TGARCH (1,1) were the lowest

respectively as compare to the other equity markets and significant, therefore the study

concludes that TGARCH(1,1) model can be a potential representative of the

asymmetric conditional volatility process for the daily return series of KLSE.


0.015143 magnitudes for KLSE and bad news has an impact of (0.015143+0.657546 =


bad news increases higher volatility in KLSE more than good news.


for KLSE


Mean equation 𝒂𝟎 0.000253

p-value <0.00001

𝛽 0.000000000000000423

p-value <0.00001

γ -1443.769

p-value <0.00001

Variance equation γ0 2.27E-34

p-value 1

δ 0.6

p-value <0.00001

γ1 0.15

p-value <0.00001

Diagnostic

Test






111


related to the returns of KLSE market. ARCH term is significant at 95% confidence




the price behavior.

Table 41: Estimates of GARCH (1,1) Model 4: Return, Volatility and Semi -Variance

for KLSE



p-value 0.6745

𝛽 0.167394

p-value <0.00001

γ 4.192136

p-value <0.00001


p-value 0.0031

δ 0.993485

p-value 0.0085

γ1 0.002947

p-value <0.00001

Diagnostic

Test








112




4.6 Econometric Models for Pakistan


Macroeconomic Model for KSE: Impact of macroeconomic variables on

Return

Statistics Parameters KSE


p-value 0.5558

β 0.130553

p-value 0.1364

γ 4.710384

p-value 0.3995

π1 -0.26877

p-value 0.7967

π2 -0.20189

p-value 0.0629

π3 0.028855

p-value 0.799

π4 0.051474

p-value 0.5839


p-value 0.5562

δ 0.506381

p-value 0.543

γ1 -0.039771

p-value 0.6934

Diagnostic

Test





variables in the variance equation for KSE. The conditional mean is not significant. So

far as macroeconomic variables are concerned, change in interest rate has significant

negative effect on return and increase in change in interest rate decreases returns of

stocks. Model is selected on the basis of AIC, SIC, and Log Likelihood values.

113


Macroeconomic Model for KSE: Impact of macroeconomic variables on

Volatility

Table 42(b) indicates the impact of macroeconomic variables on volatility of the market

has also been exercised. The results indicate that inflation is significant positively related

to volatility. In high periods of inflation, volatility is on high side. Therefore in the period

of rising prices volatility is lower it may be due to anchoring. However oil prices change

has negative significant impact on volatility.


Mean Equation γ0 -0.007216

p-value 0.2205

δ 0.158823

p-value 0.6197

γ1 2.671622

p-value 0.0002


p-value 0.0407

β 0.568143

p-value 0.0019

γ -0.051422

p-value 0.1251

π1 0.107572

p-value 0.0413

π2 -0.00577

p-value 0.3292

π3 0.00836

p-value 0.2609

π4 -0.02372

p-value 0.0027




114


Market Conditions Asymmetries for KSE: Impact of market conditions




p-value <0.00001

β 0.046784

p-value 0.0032

γ 0.018471

p-value 0.2375

θ1 2.835369

p-value <0.00001


p-value <0.00001

δ 0.824566

p-value <0.00001

γ1 0.131085

p-value <0.00001

Diagnostic

Test



Log- Likelihood 7323.241

Table 43(a) reports the role of bullish and bearish market behavior in KSE .







115


Market Conditions Asymmetries for KSE: Impact of market conditions




p-value <0.00001

β 0.036694

p-value 0.0004

γ -513.4456

p-value <0.00001


p-value <0.00001

δ -0.002288

p-value 0.2645

γ1 0.000737

p-value 0.266

μ1 -0.0000445

p-value <0.00001

Diagnostic

Test



Log- Likelihood 7065.103




relationship.

116





p-value 0.0003

β 0.091333

p-value <0.0001


p-value <0.0001

γ1 0.083396

p-value <0.0001

δ 0.142722

p-value <0.0001

θ 0.778899

p-value <0.0001

Diagnostic

Test




TGARCH (1,1) model is estimated for KSE returns series by using Gaussian



conditional variance of the KLSE returns series.

The estimation result of TGARCH (1, 1) models is shown in Table 44. The conditional

mean is significant for TGARCH(1,1) that indicates persistence in volatility for long

run and hence stable indicator of an integrated process.



presence of leverage effects in the series by using TGARCH (1,1).

Diagnostic test identify the model performance in comparison to other equity markets.

However results indicated that TGARCH(1,1) model can be a potential

representative of the asymmetric conditional volatility process for the daily return

117

series of KSE.


0.083396 magnitudes for KSE and bad news has an impact of (0.083396+0.778899 =


bad news increases higher volatility in KSE more than good news.

Table 45: Estimates of GARCH (1,1)Model 3: Return, Volatility and Value at Risk

for KSE


Mean equation 𝒂𝟎 0.00876

p-value <0.00001

𝛽 -2.86E-16

p-value .998

γ -1443.16

p-value <0.00001

Variance equation γ0 6.09E-34

p-value 1

δ 0.6

p-value <0.00001

γ1 0.15

p-value 0.0006

Diagnostic

Test







related to the returns of KSE market. ARCH term is significant at 95% confidence


118



the price behavior.


for KSE



p-value 0.0068

𝛽 0.02814

p-value 0.3677

γ -1.085048

p-value 0.2422


p-value <0.00001

δ 0.842199

p-value <0.00001

γ1 0.120431

p-value <0.00001

Diagnostic

Test










119

Table 47: Diagnostic -Test

Diagnostic –

Test


Model 1

a. Mean

Equation

AIC- statistics 30.312 -2.1765 -2.1751 -2.47035 -6.3191 -1.9070 SIC- statistics 30.532 -1.9559 -2.1751 -2.24977 -6.2882 -1.6864 Log Likelihood -1960.2 151.47 165.72 170.5725 2972.85 133.958

b.

Variance

Equation

AIC- statistics -2.1342 -2.1979 -2.6113 -2.47089 -0.7430 -2.0141

SIC- statistics -1.9137 -1.9773 -2.3492 -2.25031 -0.5224 -1.7936

Log Likelihood 148.728 152.866 139.26 170.6076 58.2991 140.92

Model

2(a)

a.Mean

Equation

AIC- statistics -5.9769 -6.130 -6.0179 -6.21952 -6.7942 -6.1662 SIC- statistics -5.9599 -6.1136 -6.0009 -6.19413 -6.777 -6.1492 Log Likelihood

7095.64 7277.9 7147.2

4537.917 8075.1 7323.24 b.

Variance

Equation

AIC- statistics -5.3812 -5.7924 -5.5688 -6.15358 -6.7075 -5.9486

SIC- statistics -5.3641 -5.7754 -5.5542 -6.12819 -6.6905 -5.9316


6876.85 6613.39 4489.881 7972.26 7065.10

Model

2(b)

AIC- statistics -5.3890 -5.4408 -5.5688 -5.8688 -6.3010 -5.5924

SIC- statistics -5.3744 -5.4262 -5.5542 -5.8542 -6.28644 -5.5778

Log Likelihood 6397.45 6458.86 6613.39 6969.329 7488.46 6641.44

Model 3 AIC- statistics -76.091 -14.576 -14.570 -13.0943 -74.110 -73.06 SIC- statistics -76.076 -14.561 -14.555 -13.0797 -74.09 -73.054 Log Likelihood

90250.5 17293.7 17293.3 15542.42 88012.3 86702.

Model 4 AIC- statistics -5.7870 -5.5049 -5.6238 -6.31919 -6.427 -5.5835 SIC- statistics -5.7589 -5.4771 -5.5957 -6.28823 -6.400 -5.554 Log Likelihood

3067.36 2970.40 2995.07 2972.859 3570.21 2889.92

Table 47 indicates the summary of diagnostic test for all equity markets. AIC, SIC and

Log likelihood values are used to select the model that may best model the conditional

mean and conditional variance for these equity markets in a best way. First of all for

model 1(a), KSE, JCI and BSE market has lower values for AIC, SIC, Log Likelihood

and it indicates that the conditional mean can be modeled in these economies for asset

pricing in a best way along with the extension of macroeconomic variables. For Model

1(b) AIC, SIC and Log likelihood values are used to select the model that may best model

the conditional variance for these equity markets in a best way. SS,BSE, KLSE and KSE

market has lower absolute values for AIC, SIC, Log Likelihood and it indicates that the

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conditional variance can be modeled in these economies for asset pricing in a best way

along with the extension of macroeconomic variables. However the performance of the

model 1(b) cannot be rejected for other economies as well. The diagnostic test for model

2(a) mean equation indicate that the conditional variance can be modeled in a preeminent

way for SS, BSE, KSE and JCI markets respectively along with asymmetric market

conditions. However the performance of this model cannot be rejected for other

economies as well. The conditional variance equation of Model 2(a) variance indicates

that SS, BSE, JCI and KSE have lower absolute values and performing well in capturing

the variance. Moreover Model 2(b) also ensures that the Good News and Bad News effect

can be modeled for SS, BSE, JCI and KSE in an excellent way based upon diagnostic test

value. Further Model 3 ensures that BSE JCI and DSE can be modeled along with VaR to

explain the risk return relationship in these economies. Finally Model 4 is performing the

best for KSE, BSE, JCI and SS to capture the return in these markets along with the semi-

variance. However the performance of the model cannot be rejected for other equity

markets as well.

4.7: Summary of the Results

This segment presents the summarized results of the analysis performed in previous

section of the study. Table 1 indicates that DSE and KSE are generating higher

average return but BSE and SS is more risky. KSE, BSE, JCI, KLSE, SS are

negatively skewed but DSE is positively skewed. Jarque-Bera normality test also

ensures departure from normality for all market returns. The returns series are

evaluated for heteroscedasticity. However Jarque-Bera test rejects the null

hypothesis of normality and indicates that all the equity market return series show

non-normality. Table 2 indicates that the numbers of positive returns are more than

negative returns for all the equity markets. The smallest difference between positive

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and negative returns can be seen for the Chinese equity market. It indicates most

significant asymmetries in mean-reversion are to be observed other than the

Chinese equity market where four negative observations are greater than four

positive consecutive returns. It might be inferred that BSE, KSE, JCI are more

effective than KLSE, SS and DSE equity markets because the pattern indicates for

KLSE and DSE that the number of three consecutive negative observations are

more than that of three consecutive positive returns. Overall all equity markets have

mean reverting behavior. Table 3 indicates that results are significant overall for

negative sign bias (NSB) and for positive sign bias (PSB) as well except KLSE.

Results indicate that asymmetry exist and can be observed in the returns series.

Coefficients and p-values for sign-bias test, negative size-bias test and positive size

bias test for the equity returns are reported at P<0.05. The estimation of the

negative size bias test indicates that negative asymmetry can be seen in the returns

series. The result of the positive size-bias test on the other hand, generating

significant estimates for the KSE, BSE, DSE SS and JCI and indicates towards

positive asymmetry regarding to these equity markets. However, for the KLSE

market, the hypothesis of positive asymmetries is rejected. In the same way the

results of the negative size-bias test, generates significant estimates for the KSE,

BSE, KLSE SS and JCI and indicates towards negative asymmetry regarding to

these equity markets. However, for the DSE, the hypothesis of negative

asymmetries is rejected. According to the sign and size bias test above, non linear

patterns are expected and being observed because the return series indicates

asymmetric patterns. Lagrange Multiple test for ARCH effects are conducted to

further investigate this matter. Table 4 to 8 reported the p-values estimated with

Lagrange Multiplier (LM) test for GARCH, EGARCH, GJR-GARCH,VS-GARCH

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and QARCH model respectively. The Lagrange multiplier test is used to test the ARCH

effects. It is inferred from above results that no non linear and asymmetric ARCH effect

can be directly seen from the estimation of the L-M test for GARCH,EGARCH, GJR-

GARCH,VS-GARCH and QARCH model respectively. This initial inspiration indicates

that nonlinear models may not perform better than linear ones. Table 9 to 13 regarding to

the estimates for GARCH, GJR-GARCH, EGARCH, VS-GARCH and QGARCH. Table

9 reports results for GARCH model that the coefficient of the conditional mean equation

is significant at p<0.0001 with the exception of SS. ARCH term is significant at 95%

confidence interval indicating that past price behavior influence current volatility in all

markets. The GARCH term is also significant at 95% confidence interval which reports

the presence of persistence in volatility. Moreover the coefficient for lagged stock returns

show significance at p<0.05,. The coefficient of ψ1 and φ1 are statistically significant at

p<0.0001 representing that the hypothesis regarding to the constant variance model is

rejected. Table 10 reports the parameter estimates of the EGARCH (1,1). EGARCH

model is used to study the asymmetric behavior of the prices. The results indicate that

there exists persistence of volatility as coefficient λ1 is significant. The significant value

of φ1 indicates that asymmetric behavior exists in the markets. Similarly size effect is

visible from significant value of φ1. It means big change in price creates more volatility

in compare to small change in price. Table 11 reports the parameter estimates of the GJR-

GARCH (1,1). GJR-GARCH Model is used to study the asymmetric behavior of the

market. Ψ is significant and positive which indicates that past price behavior influences

current price volatility. The significant value of β indicates that the volatility once

created persistence and contributes in the volatility of next period. The ω is found

significant and persistent which shows that asymmetric behavior exist in market. It

means bad news has more affect than good news. Market response is higher for bad news

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in compare to good news. Table 12 reports the parameter estimates of the VS-GARCH

(1,1). Variance equation indicates that α is asymmetric and p-value is indicating that past

previous behavior influence current volatility. β is significant and reports persistence of

volatility in the market. It means volatility created in one period is continued in

subsequent periods. Coefficient of λ indicates asymmetric behavior in the market. The

traditional (ARCH) models cannot handle such facts. Table 13 reports the estimates of

QARCH model the significant negative values of the parameter λ for all markets

except JCI. This indicates that nonlinearity exist with reference to past price

behavior. The performance of the forecasting is judged by using Root Mean Square

Error approach. Table 14 indicates the Forecasting Performance of Linear and Nonlinear

Models of the Volatility of Stock Returns. These models are ranked on the basis of

minimum value of RMSE value for first, second to onward. In linear models the

MA (1) model out performs all the others in an out-of-sample forecasting exercise

for all stock returns on the basis of RMSE criterion. The AR (1) and Random Walk

Model appear as second best model and the exponential smoothing model is ranked

last. Within nonlinear models, the GJR-GARCH model is ranked top for KSE, DSE

and SS. No doubt GJR-GARCH model is dominated over EGARCH (1,1) and

GARCH Model for this time period on the basis of RMSE criteria. It is interesting

to note that the ARCH model is ranked top for JCI and KLSE. GJR-GARCH(1,1)

model is ranked second for these markets. For BSE, EGARCH (1,1) model beating

all other model in ranking in an out-of-sample forecasting when the forecasting for

whole period. Table 15 provides results of Correlation among Stock Returns. Stock

return correlations indicate that how the returns are associated among these equity

markets. SS has highest degree of positive correlation with KLSE, BSE and JCI but not

quite significant. BSE has highest degree of correlation with JCI and KLSE, significant at

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p<0.05. Whereas JCI has highest correlation with KLSE significant at p<0.05 and KSE

has positive correlation with BSE but not quite significant however it indicates that the

returns are moving these two economies in one direction. Table 16 presents Conditional

Volatility Correlations among equity markets. Conditional volatility based upon GARCH

Model, correlations indicate that how the conditional volatility is associated among the

equity markets. The volatility of SS is positively correlated with the volatility of BSE and

JCI at p<0.05 but negatively correlated with DSE. However there is high degree of

positive association among the volatility of BSE and JCI significant at p<0.05 but

negative correlation exist between BSE and DSE. Even JCI and DSE has also negative

correlations but not significant. Where there is high degree of relationship among the

conditional volatilities of equity markets that indicates that shock behave in same

direction in these equity markets except the behavior of DSE with SS, BSE and JCI.


variables in the equation for SS. The conditional mean is significant at p < 0.10. So far

as macroeconomic variables are concerned, inflation is significantly related to return

indicating the presence of short term liquidity effect. Similarly, Oil prices change has

significant negative effect on return and increase in oil price decreases returns of stocks.

Table 17(b) results indicate that inflation is significant positively related to volatility. In

high periods of inflation, volatility is on high side, change in oil prices has also significant

impact on volatility. Table 18(a) reports the role of bullish and bearish market.

θ1 value is quite significant and positive which shows that returns are higher in bullish

period. Model is used to study the asymmetric behavior of the market.




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persistent which shows that asymmetric behavior exist in market. Results in Table 18(b)

indicate that the negativity of μ1 in bullish effect indicates that volatility in the bullish

market is less than the volatility in bearish market. Table 19 indicates that the

asymmetric effect is captured by the parameter estimate θ which is positive and

significant in the TGARCH (1, 1) that Indicates the existence of leverage effect and it is

apparent that good news has an impact of 0.04128 magnitudes for SS and bad news has

an impact of (0.048128+0.916989 = 0.965117). Because the leverage effect is

significant and hence it is concluded that the bad news increases higher volatility in SS

more than good news. Table 20 results indicate that the γ is insignificantly related to

return. It is inferred that VaR is not significantly related to the returns of SS market.

ARCH term is significant at 95% confidence interval indicating that past price behavior

influence current volatility in the market. The GARCH term is significant at 95%

confidence interval which reports the presence of persistence in the volatility. It indicates

that the value at risk is positive but not significant and has no effect on the price behavior.

Table 21 indicates that semi-variance is significant at p<0.0001 and indicates that results






variables in the variance equation for BSE. The conditional mean is not significant. So

far as macroeconomic variables are concerned, inflation has significant negative effect on

return and increase in inflation decreases returns of stocks. Table 22(b) indicates the

impact of macroeconomic variables on volatility of market has also been exercised. The

results indicate that inflation is significant positively related to volatility. In high periods

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of inflation, volatility is on high side, change in interest rate has negative significant

impact on volatility. Similarly change in industrial production has also negative

significant impact on volatility. Therefore in the period of rising prices volatility is lower

it may be due to anchoring. However oil price change has positive significant impact on

volatility. Due to high positive change in oil prices volatility is on high side. Table 23(a)

reports the role of bullish and bearish market behavior in BSE .








market is less than the volatility in bearish market. It means that in bullish market return

are high and volatility is low which offer better risk return relationship. The estimation

result of TGARCH (1,1) models are shown in Table 24 for BSE. The conditional

mean is significant for TGARCH(1,1) at p<0.10 that indicates persistence in volatility

for long run and hence stable indicator of an integrated process. ARCH and GARCH

terms are significant at p<0.0001. The asymmetric effect captured by the parameter

estimate θ is positive and significant in the TGARCH (1, 1) that indicate the existence

of leverage effect. After finding the presence of leverage effects in the series by using

TGARCH (1,1). From the estimated TGARCH model, it is apparent that good news has

an impact of 0.064636 magnitudes for BSE and bad news has an impact of

(0.064636+0.854005 = 0.918641). Because the leverage effect is significant and hence

it is concluded that the bad news increases higher volatility in BSE more than good

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news. Table 25 results indicate that the γ is negatively related to return significantly. It is

inferred that VaR is significantly negatively related to the returns of BSE market. ARCH

term is significant at 95% confidence interval indicating that past price behavior influence

current volatility in the market. The GARCH term is significant at 95% confidence

interval which reports the presence of persistence in the volatility. It indicates that the

value at risk is negative and has effect on the price behavior. Table 26 indicates the

relationship of return, and the Semi-variance. GARCH Model is extended with the Semi-

variance. Semi-variance is downside risk and added into variance equation. Here semi-

variance is insignificant which indicates no such effect. The results indicate that ARCH

term and GARCH term are significant at p<0.00001. Here all the variables for variance

equation are statistically significant. The value of the semi-variance is negative but

insignificant. Table 27(a) indicates that GARCH in mean model is extended with the

macroeconomic variables in the variance equation for JCI. The conditional mean is not

significant. So far as macroeconomic variables are concerned, change in interest rate has

significant negative effect on return and increase in interest rate decreases returns of

stocks. Model is selected on the basis of AIC, SIC, and Log Likelihood values. Table

27(b) indicates the impact of macroeconomic variables on volatility of JCI market. The

results indicate that GARCH term is significant but no macroeconomic variable have

significant impact on volatility. Table 28(a) reports the role of bullish and bearish market

behavior in JCI.

θ1value is significant and positive which shows that returns are higher in bullish period.





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result of TGARCH (1, 1) models for JCI are shown in Table 29. The conditional mean

is significant for TGARCH(1,1) that indicates persistence in volatility for long run

and hence stable indicator of an integrated process. ARCH and GARCH terms are

significant at p<0.01 and p<0.0001 respectively. The asymmetric effect captured by

the parameter estimate θ is positive and significant in the TGARCH (1, 1) that indicate

the existence of leverage effect. From the estimated TGARCH model, it is apparent that

good news has an impact of 0.038811 magnitudes for JCI and bad news has an impact

of (0.038811+0.8019 = 0.840711). Because the leverage effect is significant and hence

it is concluded that the bad news increases higher volatility in JCI more than good news.




related to the returns of JCI market. ARCH term is significant at 95% confidence interval

indicating that past price behavior influence current volatility in the market. The ARCH

and GARCH term is significant at 95% confidence interval which reports the presence of


the price behavior. Table 31 indicates semi-variance is significant at p<0.0001 and

indicates that results indicate that as down side risk increases, return also increases. The

results for JCI indicate that ARCH term and GARCH term are significant at p<0.0001.

Here all the variables for variance equation are statistically significant and the value of

the semi-variance is positive which means if the semi-variance increases it causes to an

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increase in the return. Table 32(a) indicates that GARCH in mean model is extended with

the macroeconomic variables in the variance equation for DSE. The conditional mean is

not significant. So far as macroeconomic variables are concerned, No variable has

significant effect on return. Table 32(b) indicates that the conditional mean is significant

at p < 0.10 for DSE. The results indicate that change in industrial production is

significant positively related to volatility. In high growth period of industrial production,

volatility is on high side, Therefore in the period of high industrial production volatility is

higher it may be due to anchoring. Table 33(a) reports the role of bullish and bearish

market behavior in DSE.










result of TGARCH (1, 1) models is shown in Table 34. The conditional mean is

significant for TGARCH(1,1) that indicates persistence in volatility for long run and

hence stable indicator of an integrated process. The asymmetric effect captured by the

parameter estimate θ is positive and significant in the TGARCH (1, 1) that indicate the

existence of leverage effect. After finding the presence of leverage effects in the series by

using TGARCH (1,1). However results indicated that TGARCH(1,1) model can be a

potential representative of the asymmetric conditional volatility process for the daily

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return series of DSE. From the estimated TGARCH model, it is apparent that good news

has an impact of 0.248296 magnitudes for DSE and bad news has an impact of

(0.248296+0.691989 = 0.940285). Because the leverage effect is significant and hence

it is concluded that the bad news increases higher volatility in DSE more than good

news. Table 35 indicates the relationship of return and the value at risk. GARCH Model

is extended with the Value at Risk in mean equation. The results indicate that the γ is


related to the returns of DSE market. ARCH term is significant at 95% confidence




the price behavior. Table 36 indicates the relationship of return, and the Semi-variance.

GARCH Model is extended with the Semi-variance. Semi-variance is downside risk and

added into variance equation. Here semi-variance is insignificant which indicates no such

effect. The results indicate that ARCH term and GARCH term are significant at

p<0.00001. Here all the variables for variance equation are statistically significant. The

value of the semi-variance is negative but insignificant. Table 37(a) indicates that

GARCH in mean model is extended with the macroeconomic variables in the variance

equation for KLSE. The conditional mean is not significant. So far as macroeconomic

variables are concerned, oil price has significant positive effect on return and increase in

change in oil price increases returns of stocks. Table 38(a) reports the role of bullish and

bearish market behavior in KLSE. θ value is significant and positive which shows that

returns are higher in bullish period. θ is significant and positive which indicates that past

price behavior influences current price volatility. The significant value of δ indicates that

the volatility once created persistence and contributes in the volatility of next period. The

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γ1 found significant and persistent which shows that asymmetric behavior exist in market.


volatility in the bullish market is less than the volatility in bearish market. The

estimation result of TGARCH (1, 1) models for KLSE is shown in Table 39. The

conditional mean is significant for TGARCH(1,1) that indicates persistence in

volatility for long run and hence stable indicator of an integrated process. The

asymmetric effect captured by the parameter estimate θ is positive and significant in

the TGARCH (1, 1) that indicate the existence of leverage effect. After finding the

presence of leverage effects in the series by using TGARCH (1,1) the SIC were used to

select the best model for returns series. From the estimated TGARCH model, it is

apparent that good news has an impact of 0.015143 magnitudes for KLSE and bad news

has an impact of (0.015143+0.657546 = 0.672689). Because the leverage effect is

significant and hence it is concluded that the bad news increases higher volatility in

KLSE more than good news. Table 40 indicates that the γ is negatively related to return

significantly. It is inferred that VaR is significantly negatively related to the returns of

KLSE market. ARCH term is significant at 95% confidence interval indicating that past

price behavior influence current volatility in the market. The GARCH term is significant

at 95% confidence interval which reports the presence of persistence in the volatility. It

indicates that the value at risk is negative and has effect on the price behavior. Table 41

indicates the relationship of return, and the Semi-variance. GARCH Model is extended

with the Semi-variance. Semi-variance is downside risk and added into variance

equation. Here semi-variance is significant at p<0.0001 and indicates that results indicate

that as down side risk increases, return also increases. The results indicate that ARCH

term and GARCH term are significant at p<0.0001. Here all the variables for variance

equation are statistically significant and the value of the semi-variance is positive which

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means if the semi-variance increases it causes to an increase in the return. Table 42(a)

indicates that GARCH in mean model is extended with the macroeconomic variables in

the variance equation for KSE. The conditional mean is not significant. So far as

macroeconomic variables are concerned, change in interest rate has significant negative

effect on return and increase in change in interest rate decreases returns of stocks. Table

42(b) indicates the impact of macroeconomic variables on volatility of the market has


volatility. In high periods of inflation, volatility is on high side. Therefore in the period

of rising prices volatility is lower it may be due to anchoring. However oil prices change

has negative significant impact on volatility. Table 43(a) reports the role of bullish and

bearish market behavior in KSE .










result of TGARCH (1, 1) models is shown in Table 44. The conditional mean is

significant for TGARCH(1,1) that indicates persistence in volatility for long run and

hence stable indicator of an integrated process. The asymmetric effect captured by the

parameter estimate θ is positive and significant in the TGARCH (1, 1) that indicate the

existence of leverage effect and it is apparent that good news has an impact of 0.083396

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magnitudes for KSE and bad news has an impact of (0.083396+0.778899 = 0.862295).

Because the leverage effect is significant and hence it is concluded that the bad news

increases higher volatility in KSE more than good news. Table 45 indicates that the γ is


related to the returns of KSE market. ARCH term is significant at 95% confidence




the price behavior. Table 46 indicates the relationship of return, and the Semi-variance.

GARCH Model is extended with the Semi-variance. Semi-variance is downside risk and

added into variance equation. Here semi-variance is insignificant which indicates no such

effect. The results indicate that ARCH term and GARCH term are significant at

p<0.00001. Here all the variables for variance equation are statistically significant. The

value of the semi-variance is negative but insignificant. Table 47 indicates the summary

of diagnostic test for all equity markets. AIC, SIC and Log likelihood values are used to

select the model that may best model the conditional mean and conditional variance for

these equity markets in a best way. First of all for model 1(a), KSE, JCI and BSE market

has lower values for AIC, SIC, Log Likelihood and it indicates that the conditional mean

can be modeled in these economies for asset pricing in a best way along with the

extension of macroeconomic variables. For Model 1(b) AIC, SIC and Log likelihood

values are used to select the model that may best model the conditional variance for these

equity markets in a best way. SS,BSE, KLSE and KSE market has lower absolute values

for AIC, SIC, Log Likelihood and it indicates that the conditional variance can be

modeled in these economies for asset pricing in a best way along with the extension of

macroeconomic variables. However the performance of the model 1(b) cannot be

134

rejected for other economies as well. The diagnostic test for model 2(a) mean equation

indicate that the conditional variance can be modeled in a preeminent way for SS, BSE,

KSE and JCI markets respectively along with asymmetric market conditions. However

the performance of this model cannot be rejected for other economies as well. The

conditional variance equation of Model 2(a) variance indicates that SS, BSE, JCI and

KSE have lower absolute values and performing well in capturing the variance. Moreover

Model 2(b) also ensures that the Good News and Bad News effect can be modeled for SS,

BSE, JCI and KSE in an excellent way based upon diagnostic test value. Further Model 3

ensures that BSE JCI and DSE can be modeled along with VaR to explain the risk return

relationship in these economies. Finally Model 4 is performing the best for KSE, BSE,

JCI and SS to capture the return in these markets along with the semi-variance.

The summarized results of the study elaborate the empirical evidences regarding to the

models for EAGLEs and NEST markets.

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CHAPTER 5

CONCLUSION

136

5. CONCLUSION

This study investigates different linear and nonlinear models for the conditional variance

in first instance. It compares linear and non-linear models and identified the model that

best performs in forecasting the return and volatility dynamics in EAGLEs and NEST

markets. The study also tests the robustness of model for out-of-sample volatility forecast.

The study investigates the comparative capability of various linear and nonlinear models

so that a model can be identified for the asset pricing which can perform better to absorb

the volatility effect. The study covers the period from 4th January 2000 to 30th December

2010. The results of estimations for sign-bias test (SB), negative size-bias test

(NSB) and positive size-bias test (PSB) for SS, BSE, JCI, DSE, KLSE, and KSE

indicates that there exists negative sign bias (NSB) and positive sign bias (PSB) for

all the equity markets except KLSE. This initial element indicates that nonlinear

models have some support. Results indicate that asymmetry exist and can be

observed in the returns series. Coefficients and p-values for sign-bias test, negative

size-bias test and positive size bias test for the equity returns are reported at

P<0.05. The estimation of the negative size bias test indicates that negative

asymmetry can be seen in the returns series. The result of the positive size-bias test

on the other hand, generating significant estimates for the KSE, BSE, DSE SS and

JCI and indicates towards positive asymmetry regarding to these equity markets.

However, for the KLSE market, the hypothesis of positive asymmetries is rejected.

In the same way the results of the negative size-bias test, generates significant

estimates for the KSE, BSE, KLSE SS and JCI and indicates towards negative

asymmetry regarding to these equity markets.

However, for the DSE, the hypothesis of negative asymmetries is rejected. It is

hence concluded that due to sign and size bias test, non linear patterns are expected

137

and being observed because the return series indicates asymmetric patterns. Further

Lagrange Multiple test for ARCH effects is conducted to further investigate this

matter. The LM-test is performed for all six markets, and for different model

specifications. It is inferred from above results that no nonlinear and asymmetric

ARCH effects is proved.

The forecasting models includes random walk, autoregressive, moving average,

exponential smoothing and nonlinear ARCH-class models including GARCH,

EGARCH, GJR-GARCH, VS-GARCH and QGARCH models. The estimates of

GARCH model indicates that ARCH term is significant at 95% confidence interval

indicating that past price behavior influence current volatility in all markets. The

GARCH term is also significant at 95% confidence interval which reports the presence of

persistence in volatility. Moreover the coefficient for lagged stock returns show

significance at p<0.05, it indicates that the lagged volatility has impact on current

volatility representing that the hypothesis regarding to the constant variance is rejected.

EGARCH model is used to study the asymmetric behavior of the prices. The results

indicate that there exists persistence of volatility as coefficient λ1 is significant. The

significant value of φ1 indicates that asymmetric behavior exists in the markets. The

response of volatility is adjusting for good and bad news. Bad news creates more

volatility in compare to good news. Similarly size effect is visible from significant value

of φ1. It means big change in price creates more volatility in compare to small change in

price. GJR-GARCH Model is used to study the asymmetric behavior of the market. Ψ is

significant and positive which indicates that past price behavior influences current price

volatility. The significant value of β indicates that the volatility once created persistence

and contributes in the volatility of next period. The ω is found significant and persistent

which shows that asymmetric behavior exist in market. It means bad news has more

138

affect than good news. Market response is higher for bad news in compare to good news.

In VS-GARCH (1,1) variance equation indicates that α is asymmetric and p-value is

indicating that past previous behavior influence current volatility. β is significant and

reports persistence of volatility in the market. It means volatility created in one period is

continued in subsequent periods. Coefficient of λ indicates asymmetric behavior in the

market. Poon and Granger (2003) indicated that regime switching models have fascinated

interest recently from the financial markets and reacted divergently to large and small

shocks. The traditional (ARCH) models cannot handle such facts. The estimates of

QARCH model indicates that nonlinearity exist with reference to past price

behavior. It can be observed that the parameter ψ is larger than the parameter λ for

all series. When this situation holds (ψ>λ), negative reactions contributes a greater

effect on the conditional variance, instead of positive shocks of the same size. The

parameters estimates of the GJR-GARCH indicate that nonlinear model better than

the linear ones. The results for the Volatility-Switching GARCH model show

similar results. The co-efficient parameter ψ is positive for all return series and is

larger than the parameter λ. These findings indicate that small positive shocks have

a larger impact on the conditional volatility than small negative shocks; however

when the reactions are greater in size, then the effect on volatility is in opposite

direction. This element elaborates that large positive shocks contributes to a

smaller increase in volatility rather than large shock is negative and confirms H2

The results of Asymmetric GARCH model indicate that there exists leverage

effect in these economies and the impact of news is asymmetric. It shows that

equity market volatility increases with bad news and leads towards lowering the

stock returns. It suggests that negative innovations found in returns lead towards

positive innovations in the volatility level. It is concluded for all the equity

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markets that the conditional volatility increases in a higher proportions immediate

after the negative innovations. All theses above discussed facts confirms the H1,

H2 and H3 for this study. This element is also found in this study that it is

consistent with the volatility feedback proposition as reported by Tripathy and

Garg (2013). It is observed that among linear models of stock return volatility, the

MA(1) i s r an ked first using the RMSE criterion and in nonlinear models, the

ARCH ,GJR-GARCH, GARCH(1, 1) model and EGARCH (1, 1) model perform

well and results are closer to each other. AIC, Schwarz and Log Likelihood

method conclude that GARCH model outperforms all other model. So it is

concluded that GARCH specification is best in performance to capture the

volatility. The study confirms H4 that that non-linear models outperforms than the

linear models in volatility forecasting. Within nonlinear models, the GJR-GARCH

model is ranked top for KSE, DSE and SS. No doubt GJR-GARCH model is

dominated over EGARCH (1,1) and GARCH(1,1) Model for this time period on the

basis of RMSE criteria. It is interesting to note that the ARCH model is ranked top

for JCI and KLSE. GJR-GARCH(1,1) model is ranked second for these markets.

For BSE, EGARCH (1,1) model beating all other model in ranking in an out-of-

sample forecasting when the forecasting for whole period. After comparison of

linear and nonlinear models, it is found that that the GJR-GARCH-model clearly

stands first and outperforms all the models during the whole volatility periods.

Even though non-linear models dominate the linear models because the nonlinear

models superiority is due to the ability to capture nonlinear patterns that can be

expected because the return series shows asymmetric patterns. It is concluded that

overall GARCH model outperforms among all the other models due to the best

ability to explain the conditional volatility where there is high degree of relationship

140

among the conditional volatilities of equity markets that indicates that shock behave in

same direction in these equity markets except the behavior of DSE with SS, BSE and JCI.

It is therefore concluded that the movement of volatility in EAGLEs markets have

positive degree of relationship that determines that how the positive and negative news or

shocks behave in these markets, however the volatility movements in NEST markets is

less correlated. It is clear that the returns association does not mean the volatility

associations. However if volatilities are associated it can be inferred that returns are

associated. So volatility modeling has its own unique attribution.

Further GARCH in mean model is extended with i.e. macroeconomic variables

for SS, BSE, JCI, DSE, KLSE and KSE. The macroeconomics variable includes CPI,

Term Structure of interest rate, industrial production and oil prices. Data for

Macroeconomic variable is on monthly basis for the period Jan 2000 to Dec 2010.

Impact of macroeconomic variable on return and volatility is tested in mean and variance

equation simultaneously. Impact of macroeconomic variable on return equation indicates

that for SS market, the conditional mean is significant. So far as macroeconomic variables

are concerned, inflation is significantly related to return indicating the presence of short

term liquidity effect. Similarly, Oil prices change has significant negative effect on return

and increase in oil price decreases returns of stocks. Impact of macroeconomic variable

on volatility equation for SS market indicates that inflation is significant positively related

to volatility. In high periods of inflation, volatility is on high side, change in oil prices has

also significant impact on volatility. In the period of rising prices volatility is lower it may

be due to anchoring. Impact of macroeconomic variable on return equation indicates that

for BSE the conditional mean is not significant. So far as macroeconomic variables are

concerned, inflation has significant negative effect on return and increase in inflation

decreases returns of stocks.

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The impact of macroeconomic variables on volatility of market has also been observed

for BSE. The results indicate that inflation is significantly positively related to volatility.

In high periods of inflation, volatility is on high side, change in interest rate has negative

significant impact on volatility. Similarly change in industrial production has also

negative significant impact on volatility. Therefore in the period of rising prices volatility

is lower it may be due to anchoring. However oil price change has positive significant

impact on volatility. Due to high positive change in oil prices volatility is on high side.


equation for JCI. The conditional mean is not significant. So far as macroeconomic

variables are concerned, change in interest rate has significant negative effect on return

and increase in interest rate decreases returns of stocks. The impact of macroeconomic

variables on volatility of JCI market is exercised and the results indicate that GARCH

term is significant but no macroeconomic variable have significant impact on volatility.


equation for DSE. The conditional mean is not significant. So far as macroeconomic

variables are concerned, No variable has significant effect on return. The impact of

macroeconomic variables on volatility of market has also been exercised. The conditional

mean is significant at p < 0.10. The results indicate that change in industrial production is

significant positively related to volatility. In high growth period of industrial production,

volatility is on high side, Therefore in the period of high industrial production volatility is

higher it may be due to anchoring. GARCH in mean model is extended with the

macroeconomic variables in the variance equation for KLSE. The conditional mean is

not significant. So far as macroeconomic variables are concerned, oil price has significant

positive effect on return and increase in change in oil price increases returns of stocks.

142

The impact of macroeconomic variables on volatility of KLSE has also been observed.

Change in industrial production has also positive significant impact on volatility.

Therefore in the period of high industrial growth volatility is on high side.


equation for KSE. The conditional mean is not significant. So far as macroeconomic

variables are concerned, change in interest rate has significant negative effect on return

and increase in change in interest rate decreases returns of stocks. The impact of

macroeconomic variables on volatility of KSE market has also been exercised. The

results indicate that inflation is significantly positively related to volatility. In high

periods of inflation, volatility is on high side. Therefore in the period of rising prices

volatility is lower it may be due to anchoring. However oil prices change has negative

significant impact on volatility. It is concluded that macroeconomic variables are

significant parameters for explaining the returns of stock as well as volatility in these

markets. Hence it is proved that Macroeconomic variables are significant information

parameter for modeling the volatility in these economies and confirms to H6

Further the role of bullish and bearish market is tested in all the equity market. θ value is

quite significant and positive which shows that returns are higher in bullish period.

Model is used to study the asymmetric behavior of the market. θ is significant and

positive which indicates that past price behavior influences current price volatility. The

significant value of δ indicates that the volatility once created persistence and contributes

in the volatility of next period. The γ1 found significant and persistent which shows that

asymmetric behavior exist in market for EAGLE’S and NEST markets.

Moreover results indicate that the negativity of μ1 in bullish effect indicates that volatility

in the bullish market is less than the volatility in bearish market. It means that in bullish

market return are high and volatility is low which offer better risk return relationship in

143

the equity markets of SS, BSE, JCI, DSE, KLSE and KSE. This study accepts H5 of

study that information asymmetries have impact on return and volatility.

TGARCH (1,1) model is estimated for SS, BSE, JCI, DSE, KLSE and KSE returns

series by using Gaussian standard normal distribution. The significant θ indicates

persistence in volatility for long run and hence stable indicator of an integrated

process for SS, BSE, JCI, DSE, KLSE and JCI market. The asymmetric effect

captured by the parameter estimate θ is positive and significant in the TGARCH (1, 1)

that indicates the existence of leverage effect. The leverage effect is significant and hence

it is concluded that the bad news increases higher volatility in all these markets more

than good news. However results indicated that TGARCH (1,1) model can be a

potential representative of the asymmetric conditional volatility process for the daily

return series of SS, BSE, JCI, DSE, KLSE and JCI and confirms H2 and H3

Further the relationship of return and the value at risk is explored for all the equity

markets. GARCH Model is extended with the Value at Risk in mean equation. It is

concluded that that the γ is insignificantly related to return and it is inferred that VaR is

not significantly related to the returns of SS market. Whereas the results for BSE market

indicate that the γ is negatively related to return significantly and it is inferred that VaR is

significantly negatively related to the returns of BSE market. Moreover the results

indicate that the γ is negatively related to return significantly and hence it is inferred that

VaR is significantly negatively related to the returns of JCI market. Further GARCH

Model is extended with the Value at Risk in mean equation for DSE market. The results

indicate that the γ is negatively related to return significantly. It is inferred that VaR is

significantly negatively related to the returns of DSE market. GARCH Model is extended

with the Value at Risk in mean equation. The results indicate that the γ is negatively

related to return significantly. It is inferred that VaR is significantly negatively related to

144

the returns of KLSE market. GARCH Model is extended with the Value at Risk in mean

equation. The results indicate that the γ is negatively related to return significantly. It is

inferred that VaR is significantly negatively related to the returns of KSE market.

In last the relationship of return, and the Semi-Variance is explored for all the equity

markets. GARCH Model is extended with the Semi-variance. Semi-variance is downside

risk and added into variance equation. Here semi-variance is significant at p<0.0001 and

indicates that as down side risk increases, return also increases. The results indicate that



positive which means if the semi-variance increases it causes to an increase in the return

for SS, KLSE and confirms for H7. The coefficient value of the semi-variance is

negative but insignificant for BSE, JCI, KSE, DSE and rejects H7

AIC, SIC and Log likelihood values are used to select the model that may best model the

conditional mean and conditional variance for these equity markets in a best way. First of

all for model 1(a), KSE, JCI and BSE market has lower values for AIC, SIC, Log

Likelihood and it indicates that the conditional mean can be modeled in these economies

for asset pricing in a best way along with the extension of macroeconomic variables. For

Model 1(b) AIC, SIC and Log likelihood values are used to select the model that may

best model the conditional variance for these equity markets in a best way. SS,BSE,

KLSE and KSE market has lower absolute values for AIC, SIC, Log Likelihood and it

indicates that the conditional variance can be modeled in these economies for asset

pricing in a best way along with the extension of macroeconomic variables. However the

performance of the model 1(b) cannot be rejected for other economies as well. The

diagnostic test for model 2(a) mean equation indicate that the conditional variance can be

modeled in a preeminent way for SS, BSE, KSE and JCI markets respectively along with

145

asymmetric market conditions. However the performance of this model cannot be rejected

for other economies as well. The conditional variance equation of Model 2(a) variance

indicates that SS, BSE, JCI and KSE have lower absolute values and performing well in

capturing the variance. Moreover Model 2(b) also ensures that the Good News and Bad

News effect can be modeled for SS, BSE, JCI and KSE in an excellent way based upon

diagnostic test value. Further Model 3 ensures that BSE JCI and DSE can be modeled

along with VaR to explain the risk return relationship in these economies. Finally Model

4 is performing the best for KSE, BSE, JCI and SS to capture the return in these markets

along with the semi-variance.

Hence it is concluded that there exist a significant positive relationship between risk

and return in all the equity markets and confirms the H7. The emerging stock markets

follow asymmetric patterns not only in the variance, but also in the mean and prove the

hypothesis of the study. No doubt there exists asymmetry in the variance for emerging

markets and negative reactions increases volatility more than positive reactions in

emerging markets. Study further concludes that small positive shocks have a larger

impact on the conditional volatility than small negative shocks; however when the

reactions are greater in size, then the effect on volatility is in opposite direction.

This element elaborates that large positive shocks contributes to a smaller increase

in volatility rather than large shock is negative. The returns of emerging markets

follow asymmetric pattern in mean in which positive returns are followed by more

positive returns but negative returns revert to positive returns faster than positive

reverts to the negative returns. It provides that volatility influences returns in a non-

linear fashion and confirms to H8. Efficiency of the market is a question mark this

leads towards anomalous behavior in these economies. It is finally concluded that

volatility plays a significant role in pricing of financial assets in emerging economies.

146

Emerging market behavior for conditional volatility is modeled to estimate the riskiness

of financial assets at a certain period of time. The standard finance models can tested in

the perspective of behavioral finance to capture the conditional volatility for asset

pricing as well. Therefore it is advised to the investors that they may use investment

strategies by analyzing recent and historical news, information shocks and can forecast

the future market movements based upon these models and can use this information

for selecting optimal portfolio for efficient risk management to harvest stream of

benefits in these equity markets. It is finally concluded that the conditional

volatility modeling for asset pricing provides excellent solutions.

5.1 Implications of the Study

The equity market of each economy is performing differently regarding to their different goals

objective, risk forbearance, and their common interest in the financial market. Individual or group

of investors must have an understanding of the different cultures and political and legal status

before making international equity market investment to gain a potential level of leveraged

portfolio returns in that financial market. The major Implications of this study are that there

should be an equal level of importance to pay more consideration to the traverse effects of

political and financial policies among these emerging economies in order to support

influx of capital inflows and a safe business environment for all investment portfolios.

This study implicates that the investors in these economies should prefer non-linear

models to forecast returns. Moreover the study implicates that volatility has asymmetric

patterns so the investors must consider the market conditions asymmetries to forecast

returns. Further the study indicates that volatility is higher in unstable market than stable

one so the investors should be vigilant this dynamic. Macroeconomic variables play

significant role in explaining the volatility and return so these parameters should be part

of decision making. One of the most important implication of this study suggest that to

147

have a less impact of shocks on the stock returns and volatility, it is recommended that

there should be competent investors for the real application of financial strategy regarding

to the adjustment of the quantity for their portfolio in reaction to the shocks regarding to

the different factors. Moreover, an investor may lessen their risk while considering

investment strategy by taking those companies which have sound financial performance,

fundamentals and reasonable business models. Therefore investors may employ

investment strategies by interpreting each country economic factors, business

environment, market conditions asymmetries, past and present news and to project the

future equity market movements to harvest crop of benefits in the equity markets.

Other implications suggest that investors should also be advised to be more rational

towards satisfactory financial products and market information as well as best investment

guidance while selecting investment portfolios for the efficient and effective management

of equity market risks. Further this study implies that it is the crucial time that these

emerging economies have to reframe the equity market rules and regulations, Moreover it

is also necessary to reshape the institutional arrangement so that investors may be able to

achieve diversified portfolio returns. In future study the same model may be applied to

other emerging and developed markets so that generalizibility of the model can be

explored. Other macroeconomic variables may also be considered for modeling the asset

pricing mechanism.

148

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Web Resources:

http://en.wikipedia.org/wiki/Emerging_markets

http://www.sharegyan.com/investment-gyan/benefits-of-stock-market-volatility-714/

http://informationarbitrage.com/post/698392191/who-really-benefits-from-volatility

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https://en.wikipedia.org/wiki/Emerging_and_growth-leading_economies

https://en.wikipedia.org/wiki/Emerging_markets#BBV_Research

APPENDICES:

http://en.wikipedia.org/wiki/Emerging_markets

http://www.sharegyan.com/investment-gyan/benefits-of-stock-market-volatility-714/

http://informationarbitrage.com/post/698392191/who-really-benefits-from-volatility

http://en.wikipedia.org/wiki/Volatility_(finance)

http://www.creb.org.pk/CurrrentResearchDetails.aspx?CRID=12

http://www.investopedia.com/terms/s/semivariance.asp#ixzz1ujNGLVvW

https://en.wikipedia.org/wiki/Emerging_and_growth-leading_economies

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Figure 4: Trends of Equity Markets

Figure 2: Trend of KSE BSE and JCI

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Figure 3: Trend of KLSE SS and DSE

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Figure 4: Stock Returns of Equity Markets

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Figure 5: Conditional Volatility Plots for SS

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Figure 6: Conditional Volatility Plots for BSE

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Figure 7: Conditional Volatility Plots for JCI

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Figure 8: Conditional Volatility Plots for DSE

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Figure 9: Conditional volatility Plots for KLSE

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Figure 10: Conditional Volatility Plots for KSE

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