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THE EFFECT OF MACRO-ECONOMIC FACTORS ON STOCK RETURN VOLATILITY IN THE
NAIROBI STOCK EXCHANGE, KENYA
Tobias Olweny
Lecturer Department of Commerce and Economic Studies, JKUAT Kenya
E-mail:[email protected] and:
Kennedy Omondi
Moi University,Kenya
E-mail:[email protected]
ABSTRACT
The research study sought to investigate the effect of Macro-economic factors on the stock return volatility on
the Nairobi Securities Exchange, Kenya. The study focused on the effect of foreign exchange rate, interest rate
and inflation rate fluctuation on stock return volatility at the Nairobi Securities Exchange. It used monthly time
series data for a ten years period between January 2001 and December 2010. Empirical analysis employed was
Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) and Threshold Generalized
Conditional Heteroscedasticity (TGARCH). The main findings of the research study are as follows: the stock
returns are symmetric but leptokurtic and not normally distributed. The results showed evidence that Foreign
exchange rate, Interest rate and Inflation rate, affect stock return volatility. On foreign exchange rate,
magnitude of volatility as measured by is relatively low at 0.209138 and significant since the probability is
almost zero, 0.3191. This implies that the impact of foreign exchange on stock returns is relatively low though
significant. Volatility persistence as measured by was found low at -0.251925 and significant. This implies the
effect of shocks takes a short time to die out following a crisis irrespective of what happens to the market. There
was evidence of leverage effect as measured by λ, 0.6720. This means that volatility rise more following a large
price fall than following a price rise of the same magnitude.
Keywords: Foreign exchange rate, Inflation rate, Interest rate, Leaverage effect, Stock Market Volatility
1.0 INTRODUCTION
1.1 Background of the Study
Stock market is an important institution in a country and is of great concern to investors, stakeholders and the
government. Mobilization of resources in the economy has been a puzzle investors, stakeholders, analysts and
regulators to solve. Osinubi (2010) Effective and efficient resource mobilization in an economy foster
sustainable growth and development, therefore funds must be effectively mobilized and allocated to enable the
economy realize optimal output. The stock market is an economy promotes efficiency in capital formation and
allocation. Numerous attempts by emerging stock markets to develop the financial sector have been evident in
the recent past, as they strive towards market efficiency (Rajni and Mahendra, 2007). Stock market, an efficient
stock market, acts as a barometer to economic growth. Policy makers therefore rely on market estimates of
volatility as a barometer of the vulnerability of financial markets. However, the existence of excessive volatility,
or “noise,” in the stock market undermines the usefulness of stock prices as a “signal” about the true intrinsic
value of a firm, a concept that is core to the paradigm of the informational efficiency of markets (Karolyi, 2001).
Macro-economic variables such as inflation, interest rates and foreign exchange rates have been steadily rising
due to a combination of internal and external factors. It is highly likely that growing inflation will pressurize
interest rates upwards, a situation which may result in investors moving from the equities market to the bonds
market to benefit from the higher returns (CMA, 2011). Therefore, exchange rate, inflation rate and interest rate
play a critical role in the stock market.
NSE has been the hub of stock market, to be precise, financial markets in East and Central Africa. The bourse is
one of the best performing exchanges in Africa, researchers have in the past concentrated in other exchanges
and very few literatures has been done on factors affecting stock return volatility in NSE. This research
therefore takes advantage of the gap in the quest of measuring the feasibility of NSE. In Kenya, dealing in
shares and stocks started in the 1920's when the country was still a British colony. There was however no
formal market, no rules and no regulations to govern stock broking activities. Trading took place on a
gentleman's agreement in which standard commissions were charged with clients being obligated to honour their
contractual commitments of making good delivery, and settling relevant costs. At that time, stock broking was a
sideline business conducted by accountants, auctioneers, estate agents and lawyers who met to exchange prices
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over a cup of coffee. Because these firms were engaged in other areas of specialisation, the need for association
did not arise (www.nse.co.ke)
The NSE 20-share index recorded sharp drop to 3531 points by end of Dec 2008 (KNBS, 2009). The NSE 20
Share Index fell by 7.8% to stand at 3,247 points in December 2009 compared to 3,531 points December 2008
(KNBS, 2010). The Nairobi Stock Exchange (NSE) 20 share index rose steadily over the first three quarters of
2010 to reach a peak of 4,630 points during the third quarter. The index edged downwards slightly in the fourth
quarter but remained relatively high at 4,433 points at the end of December 2010 compared to 3,247 points in
December 2009 (KNBS, 2011). Since February 2009 there has been an increase in net foreign equity outflow at
the NSE with the highest figure of Kshs 1 billion recorded in September 2009. Total for the year to date is Kshs
4 billion showing increased confidence by foreign investors (CMA, 2009). Foreign investor participation at the
NSE as measured by average turnover figures dropped by 9%, from an average of 52% in the first quarter, 2010
to an average of 43% in the second quarter, 2010. In the first quarter, 2010, the NSE realized a Kshs. 5 billion
net foreign investor cash inflow. The second quarter, 2010, saw that foreign investor inflow contract by Kshs.
3.6 billion to Kshs. 1.4 billion. However, in June 2010 the inflows picked up (CMA, 2010). NSE has 56 listed
companies, Agriculture 4, Commercial and Services 11, Finance and Investment 16, Industrial and Allied 17
and Alternative Investment Market 8.
1.2 Statement of the Problem
Stock return volatility has been a concern in the financial sector around the world. Stock markets in emerging
market especially in African has gained prominence since the market has developed a step further to risk
diversification apart from the primary role of providing an alternative source of capital for investment. High
volatility of stock return is attributable to high risk, since most investors are risk averse, they tend to shy off
from the market due to uncertainty in expected returns. High market volatility increases unfavourable market
risk premium. Therefore, it is critical for policy makers to reduce the stock market volatility and ultimately
enhance economy stability in order to improve the effectiveness of the asset allocation decisions (Poon and
Tong, 2010).
Researchers have in the past concentrated on establishing the effects of foreign exchange rate fluctuation on
stock return volatility. Mixed results have been evident with some results indicating that exchange rate
fluctuation has an impact on stock return volatility as some contradicting. Singh et al (2011) investigated the
cause and effect relationship of foreign exchange rate volatility with stock returns in Taiwan. The findings of the
study indicated a positive relationship and that foreign exchange rate volatility has an impact of stock return
volatility. Hsing (2011) too studied the JSE using GARCH models and found a positive relationship between
exchange rate and stock return volatility.
The NSE acts as the barometer to the Kenyan economy, therefore there is need determine factors affecting stock
return volatility. Kenya being a small open economy engages in international trade and is susceptible to foreign
exchange risk that might have impact on the economy, to be precise, the stock market return. Interest rate
fluctuation in Kenya has been a concern CBK has in the recent past chipped in reduction of interest rates so as to
boost investment. According to Economic Survey, (2010), the average interest rate on 91-day treasury bills fell
to 6.82 % in December 2009 from 8.59% in December 2008.Inflation rate volatility has also been evident in the
country in the past decade. Inflation eased from 16.2% in 2008 to 9.2% in 2009 (KNBS, 2010). Inflation was
contained within the Government’s target of 5.0 per cent in 2010.The average annual inflation was 4.1 percent
in 2010 down from a high of 10.5 percent recorded in 2009 (KNBS, 2011). During this period, the stock market
experienced recovery and there is need to determine the effect of inflation rate fluctuations on stock return.
1.3 Objective of the study
1.3.1 General Objective The general objective of the study was to establish the effect of Macro-economic factors on the stock return
volatility in the Nairobi Stock Exchange.
1.3.2 Specific Objectives:
The specific objectives of the study were;
1. To establish effects of foreign exchange rate fluctuations on stock return volatility in Nairobi Stock
Exchange.
2. To establish effects of interest rate fluctuation on stock return volatility in Nairobi Stock Exchange.
3. To establish effects of inflation rate fluctuation on stock return volatility in Nairobi Stock Exchange.
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1.4 Research Hypothesis
H01: Foreign exchange rate volatility has no significant effect on stock return volatility.
H02: Interest rate volatility has no significant effect on stock return volatility.
H03: Inflation rate volatility has no significant effect on stock return volatility.
2.0 LITERATURE REVIEW
2.1 Concept of Stock Market Volatility
Research studies have in the recent past done to investigate the relationship between stock market return and
exchange rate fluctuation. Chen et al (1986) used monthly data to investigate the systematic event influence on
US stock market return by several economic variables, they found some of the variables were significant in
describing stock returns, and got a proof that the stock returns was exposed to systematic events. Stock
exchange market serves as a channel through which surplus funds are moved from lenders to savers, from those
with surplus to those with deficit. The origins of stock market volatility have long been a topic of considerable
interest and concern to policy makers and financial analysts. Policy makers have their interest vested in the main
determinants of volatility and its spillover effects on real activities. Financial analysts on other hand have their
interest focused on the effects of time-varying volatility. The financial position of an economy of a country is
determined by the capital market therefore, susceptible to its foreign exchange volatility.
Autoregressive conditional heteroskedasticity (ARCH) model by Engle (1982) as generalized (GARCH) by
Bollerslev (1986) has led to the development of various models in financial time series. Several studies
investigate the performance of GARCH models on explaining volatility of mature stock markets (e.g. Sentana
and Wadhwani, 1992; Kim and Kon, 1994; Kearney and Daly, 1998; Floros, 2007; Floros et al., 2007), but few
have tested GARCH models using daily data from Middle East stock markets. Mecagni and Sourial (1999)
examine the behaviour of stock returns as well as the market efficiency and volatility effects in the Egyptian
stock exchange using GARCH models. The results show significant departures from the EMH, tendency for
returns to exhibit volatility clustering and a significant positive link between risk and returns (Floros,
2008).Kendall and McDonald (1989) found significant estimate for a GARCH (1,1)-in-mean model using
weekly data. Among others, Chou (1988) using a GARCH-M model found a positive relation between returns
and conditional variance and also found that the GARCH-M model was more reliable than two-stage least
squares models used by Pindyck (1984), Poterba and Summers (1986) and French, Schwert, and Stambaugh
(1987). Schwert, and Stambaugh (1987) found a statistical significant positive relation between expected returns
and anticipated volatility only when using a generalized autoregressive conditional heteroscedasticity model
with mean effects (GARCH-M).
2.2 Theoretical Review
2.2.1 Fisher's theory
Irving Fisher's theory of capital and investment was introduced in his Nature of Capital and Income (1906) and
Rate of Interest (1907), although it has its clearest and most famous exposition in his Theory of Interest (1930).
In his theory, Fisher assumed that all capital was circulating capital and that capital is used up in the production
process, thus a stock of capital K did not exist. Rather, all capital is, in fact, investment. Given that Fisher's
theory output is related not to capital but rather to investment, then we can posit a production function of the
form Y =
the next period. Holding labour N constant, then the investment frontier can be drawn as the concave function
Figure 2.1: Fisher's Investment Frontier
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In Figure 2.1 above, shows Fisher's investment frontier Y2 1) where the concave nature of the curve
reflects, diminishing marginal returns to investment. Suppose we start at initial endowment of intertemporal
output E - where E1 > 0 and E2 = 0, so we only have endowment in period 1. Then the amount of "investment"
involves allocating some amount of period 1 endowment to production for period 2. The output left over for
period 1 consumption, let us call that Y1*, is effectively the amount of initial endowment that investment has not
appropriated, i.e. Y1* = E1 - I1*. The investment decision will be optimal where the investment frontier is
tangent to the interest rate line, i.e. where
becomes Y* = (Y1*, Y2*) where Y2 1*) and Y1* = E1 - I1*. It is obvious, by playing with this diagram,
that as r increases (interest rate line becomes steeper), then I1* declines; whereas as r declines (interest line
becomes flatter), then I1* increases. Thus, dI/dr < 0, so investment is negatively related to the interest rate.
2.2.2 Interest rate theory
In the classical theory, the amount of savings and investment were equated by a fluctuating interest rate.
Economists and government policy makers have found that both savings and investment are not just influenced
by changes to the interest rate. Investment is also influenced by prices and government taxes and other policies.
But even taking these variables into account, economists cannot explain all of fluctuations in investment.
Influential British economist John Maynard Keynes hypothesized that investment is dependent on the "animal
spirits" of entrepreneurs. In other words, interest rates are definitely important in savings and investment, but
they don't tell the whole story.
The theory of interest rates relates the nominal interest rate i to the rate of inflation π and the "real" interest rate
r. The real interest rate r is the interest rate after adjustment for inflation. It is the interest rate that lenders have
to have to be willing to loan out their funds. The relation Fisher postulated between these three rates is:
(1+i) = (1+r) (1+π) = 1 + r + π + r π
This is equivalent to: i = r + π(1 + r).Thus, according to this equation, if π increases by 1 percent the nominal
interest rate increases by more than 1 percent.
2.2.3 Arbitrage Pricing Theory
While the CAPM is a simple model that is based on sound reasoning, some of the assumptions that underlie the
model are unrealistic.2 Some extensions of the basic CAPM were proposed that relaxed one or more of these
assumptions (e.g., Black, 1972). Instead of simply extending an existing theory, Ross (1976a, 1976b) addresses
this concern by developing a completely different model: the Arbitrage Pricing Theory (APT). Unlike the
CAPM, which is a model of financial market equilibrium, the APT starts with the premise that arbitrage3
opportunities should not be present in efficient financial markets. This assumption is much less restrictive than
those required to derive the CAPM.
The APT starts by assuming that there are n factors which cause asset returns to systematically deviate from
their expected values. The theory does not specify how large the number n is, nor does it identify the factors. It
simply assumes that these n factors cause returns to vary together. There may be other, firm-specific reasons for
returns to differ from their expected values, but these firm-specific deviations are not related across stocks.
Since the firm specific deviations are not related to one another, all return variation not related to the n common
factors can be diversified away. Based on these assumptions, Ross shows that, in order to prevent arbitrage, an
asset's expected return must be a linear function of its sensitivity to the n common factors.
2.3 Empirical Review
2.3.1 Foreign Exchange rate volatility and stock return volatility
Exchange rate can be defined as the price at which a country’s currency can be exchanged for another country’s
currency. Exchange rate volatility has implications on a country’s financial sector, the stock market to be
precise. Benita and Lauterbach (2004) found that exchange rate volatility have real economic costs that affect
price stability, firm profitability and a country’s stability. Establishing the relationship between stock prices and
exchange rates is important for a few reasons. First, it may affect decisions about monetary and fiscal policy.
Gavin (1989) shows that a booming stock market has a positive effect on aggregate demand. Second, the link
between the two markets may be used to predict the path of the exchange rate. This will benefit multinational
corporations in managing their exposure to foreign contracts and exchange rate risk stabilizing their earnings
(Dimitrova, 2005).
Exchange rate movement affects output levels of firms and also the trade balance of an economy. Share price
movements on the stock market also affect aggregate demand through wealth, liquidity effects and indirectly the
exchange rate. Specifically a reduction in stock prices reduces wealth of local investors and further reduces
liquidity in the economy. The reduction in liquidity also reduces interest rates which in turn induce capital
outflows and in turn causes currency depreciation (Adjasi et al, 2008). Hsing (2011) found a positive
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relationship between exchange rate and the stock market in Johannesburg Stock Exchange. Cheng’ et al (2011)
conducted study on Taiwan stock market and the results indicated a positive relationship between exchange rate
and stock return. Bailey and Chung (1995) conducted a study on Exchange Rate Fluctuations, Political Risk, and
Stock Returns at the Mexican stock market and the results proved there is a positive relationship between
exchange rate fluctuation and stock market return.
Exchange rate volatility has attracted much attention in financial economics in developed and developing
economies due to its implications in the financial markets, especially the stock market. Different implications
were observed between exchange rate volatility and stock market returns – depreciation in the local currency
leads to increases in stock market prices in the long run (Adjasi et al, 2008)Choi et al(1995) and Kanas(2000)
found that exchange rate changes didn’t affect stock returns at all. However, Abugri(2008) showed that the
stock return was deeply affected by exchange rate. Griffin et al(2001) found there had a positive impact on the
electronic industries in U.S. Kandir (2008) conducted a study on Turkey with the aim of determining the
relationship between exchange rate and stock return. Using multiple regression model, the results showed that
exchange rate seem to have a positive relationship with portfolio return. Jefferis and Okeahalam (2000) study
the relationship between stock prices and selected economic variables for South Africa, Zimbabwe and
Botswana. For South Africa, they show that the stock market is positively affected by real GDP and the real
exchange rate. Other studies that found a positive relationship between exchange rate fluctuations and stock
return volatility include, Smith (1992), Solnik (1987), Aggarwal (1981), Frank and Young (1972), Phylaktis and
Ravazzolo (2000), Granger et al. (2000), Abdalla and Murinde (1997), and Apte (2001).
2.3.2 Interest rate volatility and stock return volatility
The interest rate can be defined as the annual price charged by a lender to a borrower in order for the borrower
to obtain a loan. This is usually expressed as a percentage of the total amount loaned. Traditional theories define
interest rate as the price of savings determined by demand and supply of loanable funds. Ngugi and Kabubo(
1998) states that the primary role of interest rate is to help mobilize financial resources and ensure the efficient
utilization of resources in the promotion of economic growth and development.
Chen et al(1986) indicated that interest rate had positive impact on stock return. Wongbangpo et al(2002)
observed interest rate had a negative impact on southeast Asian countries In the industrial analysis,
Nguyen(2007) found interest rate spreads had a significant effect on the riskiness of capital-intensive industries.
Chiang et al(2009) realized interest rate was negative toward Singapore hotel stock return. Specifically, Rapach
et al(2005) pointed out the interest rate was the most reliable variable. However, Chan et al(1998) thought
interest rate didn’t have any relationship with stock return. Besides, Chen et al(2005) also found the interest rate
was not significant for Taiwan hotel stock return. Kandir (2008) studied the Turkish market and found a positive
relationship between interest rates and stock return. Jefferis and Okeahalam (2000) study the relationship
between stock prices and selected economic variables for South Africa, Zimbabwe and Botswana. For South
Africa, they show that the stock market is negatively influenced by the long-term interest rate.
2.3.3 Inflation rate volatility and stock return volatility
The inflation rate is the rate of increase of a price index (for example, a consumer price index). It is the
percentage rate of change in price level over time. The rate of decrease in the purchasing power of money is
approximately equal( Mishni, 2004).Research study has also been conducted to determine the effects of inflation
on stock market. Most scholars used consumer price index (CPI) to substitute inflation. CPI was often used to
reflect the products and prices about the general public. Most studies reveals inflation had negative impaction on
stock return. Liljeblom et al (1997) also found the Finnish data of stock market was affected by inflation. In the
industries analysis, Kavussanos et al(2002) found there was a few industries have negative influence, such as
electronic sectors.etc, In predictability, the inflation is limited. (Rapach et al,2005), (Chen et al,1986). On the
contrary, They considered inflation had no ability in predicting stock return.(Chan,1998), (Chen,2005). Based
on above argument, we predict that the variable of inflation has a negative impact on stock returns. Chinzara
(2011) in his study on macroeconomic uncertainty and stock market volatility for South Africa found out stock
market volatility is significantly affected by macroeconomic uncertainty, that financial crises raise stock market
volatility, and that volatilities in exchange rates and short-term interest rates are the most influential variables in
affecting stock market volatility whereas volatilities in oil prices, gold prices and inflation play minor roles in
affecting stock market volatility.
Schwert (1989) in his classic paper studied the relationship between stock market volatility and volatility of real
and nominal macroeconomic variables and concluded that movements in inflation and real output have weak
predictive power on volatility of stock market and return.Yaya and Shittu (2010) in theirs paper found out that
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the previous inflation rates has significant effects on conditional stock market volatility. Changes in inflation
rates, as measured by changes in these rates also have greater impact in predicting the stock market volatility in
Nigeria. These results are in agreement with Fisher’s effect in international stock market.
Fisher (1930) asserted that the nominal interest rate consists of a real rate plus the expected inflation rate. Fisher
Hypothesis stated that expected real rate of the economy is determined by the real factors such as productivity of
capital and time preference of savers and is independent of the expected inflation rate. If Fisher effect holds,
there is no change in inflation and nominal stock returns since stock returns are allowed to hedge for inflation.
Some opposed to Fisher Hypothesis, and claimed that the real rates of common stock return and expected
inflation rates are independent and that nominal stock returns vary in one-to-one correspondence with expected
inflation (Pong and Tong, 2010).
2.4 Conceptual Framework
The purpose of conceptual framework is to help the reader quickly see the proposed relationship between
variables in the study (Mugenda and Mugenda, 2003). The conceptual framework of this study spells out the
relationship between foreign exchange rate, interest rate and inflation rate (independent variables) and stock
return volatility (dependent variable) as measured by NSE 20 share index. The study therefore sought to
investigate the effect of independent variable fluctuations( Interest rate, exchange rate and inflation rate) on
dependent variable (Stock return volatility).
Figure 2.2: Schematic representation on the macro-economic factors affecting stock return volatility.
Independent Variables Dependent Variable
3.0 RESEARCH METHODOLOGY
3.1 Research Design
The study undertook explanatory survey research in the quest to answer the puzzle on the effect of macro-
economic factors that on the stock return volatility in the Nairobi Stock Exchange that has been of a concern.
Explanatory survey research was chosen for robust study and deep search into the macro-economic factors to
achieve the goal of the study.
3.2 Target Population
The target population consisted of all the stocks listed at NSE as at December 2010 from which NSE share
index is derived from. This was an appropriate population and gave a clear picture of the situation in the market
with all participants included.
Foreign exchange rate
volatility
US Dollar
Interest rate volatility
90 day Treasury
bill rate
T
Inflation rate volatility
Inflation rate
Stock Return
Volatility
NSE 20
Share Index
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3.3 Sample design
The study used monthly time series data for NSE all share index that covered a period of 10 years from January
2001 to December 2010. This was aimed at achieving comprehensive coverage and a decade gave much
accurate results.
3.5 Data Collection instruments and procedure
3.5.1 Type of Data
Time series secondary data was used in the study. The data was bought from Nairobi Stock Exchange that was
used in the analysis and inferences drawn. Other sources of data were from Central Bank of Kenya and Kenya
National Bureau of Statistics. The data was obtained in soft copy and accuracy was observed.
3.5.2 Data Collection Instruments
A schedule was drawn to give direction on the particulars of data relevant for the study. This was to ensure that
the study focused on the objectives and no derailing from the objectives.
3.6 Data Analysis and Presentation
Econometrics models were used in the study to analyse the collected data so as to get accurate results. GARCH
models were of great importance in data analysis and captured the effect of macro-economic factors on stock
return volatility in the NSE. Statistical software (Eviews 7.0) was used as an aid in the analysis of the GARCH
model in determining the effect of macro-economic factors on stock return volatility. Data presentation was
done in tables and graph.
3.7 EGARCH Model
The Exponential Generalised Autoregressive Conditional Heteroskedascity (EGARCH) was used in determining
the effect of macro-economic factors on stock return volatility in NSE. This is most often preferred to the
GARCH model in studying financial markets. As identified by Koulakiotis et al (2006) the GARCH is relatively
weaker than the EGARCH in studying financial markets phenomenon.
EGARCH model takes the form:
= + ……………..…….. 1
log( )=
|
|+ λ
+ ……………………….. 2
= log
The mean and variance equations are stated in equations (1) and (2) respectively
Where
Rt represents the returns of the NSE 20 share Index
NSEt represents the prices of the NSE 20 share Index
log( )= log of conditional variance of stock market returns
- Vector of coeficient
- Error term
λ- Leverage effect
- Exchange rate volatility
Leverage effect is shown by λ< 0. If λ ≠ 0 then the impact of news is asymmetric.
The stock returns were calculated by from NSE 20 share index as, log since the index is
usually the benchmark on measuring stock market performance. The data was put in Ms excel, copied and
pasted in Eviews 7.0 software. Using the GARCH model command, the software auto generated the output
results. EGARCH model was significant in determining the volatility magnitude effect, persistence of the
volatility in the market and leverage effect. The model was also in determining the impact of news on stock
return volatility.
3.7.1 TGARCH Model
TGARCH model was developed independently by Zakoian (1994) and Glosten et al. (1993). It explains the
impact of news on volatility and its generalised version is given as:
= +
+ β + λ
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In the model, good news, 0, and bad news
0, have differential effects on the conditional variance.
represents the impact of good news while + λ represents the impact of bad news. If λ > 0, this is an
indication that bad news increases volatility in the market suggesting existence of leverage effect of the first
order. If λ ≠ 0, the indication is that the news impact is asymmetric. This model was used to prove the results of
EGARCH model on the impact of news on the stock return volatility.
4.0 DATA ANALYSIS AND PRESENTATION
4.1 Introduction
The tool used to determine the macroeconomic factors affecting stock return volatility includes descriptive
statistics, the unit root Augmented Dickey Fuller (ADF) test proposed by Dickey and Fuller (1979, 1981);
EGARCH (Nelson, 1991).
4.2 Descriptive statistics and test for variables normality
Table 4.1: Descriptive Statistics
RETURNS ERT INT INF
Mean 0.007191 0.000225 0.069092 0.102658
Median 0.005873 -0.000635 0.073050 0.090500
Maximum 0.159816 0.118515 0.153000 0.294000
Minimum -0.256647 -0.093069 0.008300 0.004000
Std. Dev. 0.066876 0.022684 0.031315 0.069591
Skewness -0.545509 1.092194 0.016472 0.976450
Kurtosis 5.012959 11.69445 3.202818 3.565811
Jarque-Bera 25.99321 398.4768 0.211102 20.66979
Probability 0.000002 0.000000 0.899829 0.000032
Observations 119 120 120 120
Statistical characteristics of all the variables are shown as Table 4.1 above. These variables are the Stock
returns, Foreign exchange rate (ERT), Interest rate (INT) and the Inflation rate (INF). Statistically, this study
employed the Jarque-Bera test to test for normality in the time series data variables used.
The Jarque-Bera (JB) test statistic was used to determine whether macro-economic factors and stock returns
follow the normal probability distribution. The JB test of normality is an asymptotic, or large-sample, test that
computes the skewness and kurtosis measures and uses the following test statistic:
JB = n [S2 /6 + (K-3)2 /24]
Where n = sample size, S = skewness coefficient, and K = kurtosis coefficient. For a normally distributed
variable, S = 0 and K = 3. Therefore, the JB test of normality is a test of the joint hypothesis that S and K are 0
and 3 respectively.
We conclude that all the variables are not normally distributed apart from interest rate whose skewness
coefficient is close to zero (0.016472) and kurtosis coefficient of 3.202818.
4.3 Tests for Stationarity
An important concern in data analysis is to know whether a series is stationary (do not contain a unit root) or not
stationary (contains a unit root). This concern is important because you want both the left hand side and right-
hand side variables of your regressions to balance. Time series data are often assumed to be non-stationary and
thus it is necessary to perform a pretest to ensure there is a stationary relationship between macroenomic factors
and stock return volatility in order to avoid the problem of spurious regression (Riman and Eyo (2008)).
Spurious regression is cited in Patterson (2000), to exist where the test statistics show a significant relationship
between variables in the regression model even though no such relationship exists between them. Therefore, in
order to address the issue of non-stationarity and avoid the problem of spurious regression, we employ a
quantitative analysis. For the testing of unit roots, the Augmented Dickey-Fuller test (ADF) was used.
Augmented Dickey-Fuller (ADF) test has been carried out which is the modified version of Dickey-Fuller (DF)
test. ADF makes a parametric correction in the original DF test for higher-order correlation by assuming that the
series follows an AR (p) process. The Augmented Dickey-Fuller test specification used here is as follows:
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ΔYt = b0 + βYt-1 + μ1Yt-1 + μ2Yt-2 + …….. + μpYt-p + εt
Where, Yt represents time series to be tested, b0 is the intercept term, β is the coefficient of interest in the unit
root test, μi is the parameter of the augmented lagged first difference of Yt to represent the pth
-order
autoregressive process, and εt is the white noise error term. In carrying out the unit root test, we seek to test the
following hypothesis:
H0: α=0 (non stationary)
If the null hypothesis is rejected, this means that the time series data is stationary. The decision criteria involve
comparing the computed Tau values with the MacKinnon critical values for the rejection of a hypothesis for a
unit root. If the computed tau (ADF) statistic is less negative (i.e. lies to the right of the MacKinnon critical
values) relative to the critical values, we do not reject the null hypothesis of non-stationarity in time series
variables.
Table 4.2: Results of Augmented Dickey Fuller (ADF) stationarity test at level
ADF Test at Level (Trend and Intercept)
Variable ADF Statistic Critical value DW Lag Inference
RETURNS -8.684240 -3.4481 1.998963 0 I(0)
ERT -9.534937 -3.4481 1.944759 0 I(0)
INT -1.967850 -3.4478 1.314754 0 I(1)
INF -1.986333 -3.4478 1.619052 0 I(1)
*MacKinnon critical values for rejection of hypothesis of a unit root at 5%.
Source: Survey Data (2011)
On application of the ADF test, at level only stock return and exchange rate are stationary whereas interest rate
and inflation are the only stationary independent variables (they all contain a unit root) as indicated by the fact
that their respective critical values are all smaller (in absolute terms) than the calculated ADF statistics and
hence we do not reject the null hypothesis: that the time series data variables are non-stationary. Therefore there
is need to test for the ADF in the first difference.
Table 4.3: Results of Augmented Dickey Fuller (ADF) stationarity test at 1st difference.
ADF Test at 1st Difference (Trend and Intercept)
At the first difference, all the variables are stationary since the critical value is greater than ADF statistic; ADF
statistic is more negative than the critical value. Therefore we reject the null hypothesis since the variables have
no unit root at the 1st difference.
4.4 Testing for Correlation
Table 4.4: Results of Correlation matrix
RETURNS ERT INT CPI
RETURNS 1.000000 -0.355411 -0.188688 -0.076586
ERT -0.355411 1.000000 -0.081018 0.154508
INT -0.188688 -0.081018 1.000000 -0.005404
CPI -0.076586 0.154508 -0.005404 1.000000
Source: Survey Data (2011)
Variable @1st
diff
ADF Statistic Critical value DW Lag Inference
D(INT) -13.39553 -3.4487 2.201829 1 I(1)
D(CPI) -11.03037 -3.4487 2.037487 1 I(1)
*MacKinnon critical values for rejection of hypothesis of a unit root at 5%.
Source: Survey Data (2011)
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A test for correlation between the variables shows negative correlation. The variables indicated weak negative
correlation between the variables. The results show that when foreign exchange rate increases by 1%, stock
return decreases by 35.54%. This implies that when exchange rate increases, investors shy away from the
market and offload stock thereby resulting in the decrease of stock return. When interest rate increase by 1%,
stock returns decreases by 18.86%. This might be prompted by investors selling off the stocks held and invest in
T-bills. The test shows that when inflation rate increase by 1%, stock return decreases by 7.65%. Rise in price
levels over time triggers investors to lose confidence in the market. From the test, foreign exchange rate
fluctuations affect stock return volatility more than interest rate and inflation. We can also see that inflation rate
fluctuation has slight impact on stock returns.
4.5 Testing for the Exponential Generalised Conditional Heteroscedasticity Model
4.5.1 Results of the EGARCH model on the effect of foreign exchange rate on stock return volatility
The results on Table 4.6 indicate that the magnitude of volatility, as denoted by , is relatively low and is
insignificant. This may be as a result of investors opting for investments dealing in local currency which are less
risky as compared to foreign exchange rate investments. Volatility persistence of foreign exchange rate is low
since is -0.251925 but significant since the probability is almost zero (0). The direction of effect λ < 1 is not
significant at the conventional levels of testing. The negative sign of -0.246829 suggests that there are leverage
effects in the returns series and that bad news has a larger impact on stock return volatility. However, being
insignificant implies that these effects are not pronounced during the sample periods. The results show that bad
news impact more on stock return volatility than good news in the market. The findings corresponds to that of
Adjasi et al, (2008).
Table 4.6: Results of the EGARCH model on the effect of foreign exchange rate on stock return volatility
Variance Equation
Coefficient Probability
-7.119628 0.0294
0.174716 0.3191
-0.251925 0.0058
λ -0.246829 0.6720
Source: Survey Data (2011)
4.5.2 Results of the EGARCH model on the effect of Interest rate on stock return volatility
Interest rate from the results in Table 4.7 show that the magnitude of volatility as measured by is low at
0.209138 and significant since the probability is almost zero. Measure persistence of volatility during the
period and is significant since the probability is zero (0). λ < 1 is significant and proves the presence of leverage
effect. The negative sign of -0.385645 suggests that there exist leverage effects in the returns series and that bad
news has a significant impact on stock return volatility. However, being significant implies that leverage effect
is pronounced during the sample periods. These findings are also consistent the findings of Rajni and Mahendra
(2007 who found out that interest rates has significant effect on stock return volatility.
Table 4.7: Results of the EGARCH model on the effect of Interest rate on stock return volatility Variance Equation
Coefficient Probability
-7.823818 0.0000
0.209138 0.1468
-0.331165 0.0011
λ -0.385645 0.1364
Source: Survey Data (2011)
4.5.3 Results of the EGARCH model on the effect of Inflation rate on stock return volatility
From the Table 4.8, volatility magnitude is low and insignificant as represented by . This may be attributable
to the fact that inflation has relatively small impact on investment at the stock market. Volatility persistence as
measured by is low and significant since the probability is close to zero (0). There exist leverage effect which
is also significant. The presence of leverage effect can be proved since effect λ < 1 and the probability is zero to
show that leverage effect is significant. This is also evidence that bad news has a significant impact on stock
return volatility than good news.
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Table 4.8: Results of the EGARCH model on the effect of Inflation rate on stock return volatility Variance Equation
Coefficient Probability
-10.32295 0.0000
-0.004819 0.9631
-0.214196 0.0153
λ -0.855409 0.0000
Source: Survey Data (2011)
4.5.4 Results of the Generalised Conditional Heteroscedasticity Model with all the three independent
variables
4.5.4.1 Results of the EGARCH model on the effect of foreign exchange rate, Interest rate and Inflation
rate on stock return volatility (Mean equation)
Foreign exchange rate from the results shown in Table 4.5 provide evidence of a negative relationship with
stock returns. This shows that when exchange rate increases, stock returns decreases since investors offload their
stock takes advantage of exchange rate parity. When exchange rate reduces, investors buy more stock in
anticipation of future increase in stock prices thereby increasing stock returns. The results also reflect that
inflation rate is significant since the probability is different almost 0 (zero). Interest rate has inverse relationship
with the stock returns. When 91 day treasury bills rate increases, it attracts more investors and this prompts them
to sell off their stock and invest in treasury bill and vice versa. Interest rate has a probability of 0.5929 and this
is not close to zero, therefore the effect is not significant.
Table 4.5: Results of the EGARCH model on the effect of foreign exchange rate, Interest rate and
Inflation rate on stock return volatility (Mean equation) Mean equation
Coefficient Probability.
ERT -0.917995 0.0000
D(INT/100) 0.506520 0.5929
D(INF/100) 0.218659 0.2347
C 0.011534 0.0552
Source: Survey Data (2011)
4.5.4.2 Results of the EGARCH model on the effect of foreign exchange rate, Interest rate and Inflation
rate on stock return volatility (Variance Equation)
In Table 4.9, is found positive, as expected, and significant at a conventional level of testing. The high
estimated parameter is an indication that the magnitude of the shocks has an insignificant impact on volatility.
The estimated parameter, of -0.169526, shows that persistence of volatility is quite low. These findings differ
from Misati and Nyamongo, (2010) whose finding was that volatility persistence at the NSE is highly persistent.
In terms of the decay process of volatility it is found that the effect of a shock in the returns is significant but
takes a short duration to lose its effect on the variance of the returns. The direction of effect λ < 1 is significant
at the conventional levels of testing. The negative sign suggests that there are leverage effects in the returns
series. However, being significant implies that these effects are pronounced during the sample periods and that
bad news has significant impact on stock return volatility than good news. Misati and Nyamongo, (2010) found
that leverage effect is not significant.
Table 4.9: Results of the EGARCH model on the effect of foreign exchange rate, Interest rate and
Inflation rate on stock return volatility (Variance Equation) Variance Equation
Coefficient Probability
-10.55920 0.0000
0.016849 0.8758
-0.169526 0.0253
λ -10.55920 0.0000
Source: Survey Data (2011)
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4.6 Testing the TGARCH Model
The test was carried out so as to determine the impact of news on stock return volatility in comparison to the
results of EGARCH model .The findings of the TGARCH model as shown in Table 4.10 provide evidence that
news impact is asymmetric since λ ≠ 0. The results is also consistent with that of EGARCH model as λ > 0,
1.043624. This is an indication that bad news increases volatility in the market suggesting existence of leverage
effect and this is the same results in the study conducted by Misati and Nyamongo, (2010). The probability for
the TGARCH test shows that leverage effect is significant. The results contradict that of Misati and Nyamongo,
(2010) who found leverage effect to be insignificant at the Nairobi Stock Exchange.
Table 4.10: Results of the TGARCH model on the effect of foreign exchange rate, Interest rate and
Inflation rate on stock return volatility
Coefficient Probability
0.000143 0.0000
-0.084618 0.0000
0.024143 0.1727
λ 1.043624 0.0000
Source: Survey Data (2011)
5.0 CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusion
From the findings of the study, it can be concluded that macro-economic factors; Foreign exchange rate, Interest
rate and Inflation rate affect stock return volatility at the Nairobi Stock Exchange.The main findings of the paper
are as follows: the equities returns are symmetric but leptokurtic and thus not normally distributed; volatility of
returns is not highly persistent; the leverage effects are significant. Misati and Nyamongo, (2010) found out in
their study: the equities returns are symmetric but leptokurtic and thus not normally distributed; volatility of
returns is not highly persistent; the leverage effects are significant.
The findings from this study are consistent with other studies as discussed earlier and although matters stock
return volatility is an important aspect in the expectations and decisions of investors in the stock market, the role
played by the Nairobi stock exchange market cannot be overlooked. The findings of the TGARCH model
suggest that the news impact is asymmetric. The results also provided evidence that bad news has a larger
impact on stock return volatility than good news. The finding is consistent with the results of the EGARCH
model.This therefore shows the immense potential that the Nairobi stock exchange may have towards fostering
the country’s economy should the Kenyan government promote a saving culture and consequently improve
investments income of the populace through appropriate policies. The Capital Markets Authority as a regulator
should work to ensure that impediment to stock market growth such as legal and other regulatory barriers.
The findings from this study emphasize on the role of the stock exchange market in directing economic growth
i.e. the Nairobi stock exchange has been found to be a leading indicator for economic growth. Therefore there is
need to identify factors that have significant effect on stock market return. This will enable investors make
rational decisions in order to maximize returns. The regulator will also ensure that measures are put in place to
ensure fair play in the market. The findings as illustrated if figure 5.1 (Appendix II) shows evidence of volatility
clustering over time.
5.2 Recommendations
Based on the findings of the study, the study presents recommendations pertinent to the policy makers,
investors, financial market regulators and future researchers. The study recommends the government through its
policy makers should come up with policies that will help stabilize Foreign exchange rate, Interest rate and
Inflation rate fluctuation thus creating investor confidence in the securities market. This will have a significant
impact on the performance of the Nairobi Securities Exchange thus foster economic growth.
The regulator should ensure that all the market players comply with the policies and regulations in a bid to
ensure efficiency and effectiveness of the bourse. The study recommends survey carried from time to time on
macro-economic factors affecting stock return. This can be facilitated by availing data for free to students and
other researcher with interest in studying the stock market, factors affecting the market returns and market
efficiency. Further studies on persistence of news on stock return will be useful to investors in making rational
investment decisions and aid the regulator in policy formulation.
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5.3 Suggestions for Further research
This study sought to investigate the macro-economic factors affecting stock return volatility in the NSE.
Volatility of returns in financial markets can be a major stumbling block for attracting investment in small
developing economies (Rajni, Mahendra, 2007). The stock market being an important institution in an economy
and for a country to experience growth, the stock market should be efficient. Future researcher may conduct
further studies and identify other macro-economic factors that significantly affect stock returns. Therefore
further should focus of macro-economic factors such as: Money supply, monetary policy, fiscal policy and
industrial production. Further studies on persistence of news on stock return will be useful to investors in
making rational investment decisions and aid the regulator in policy formulation. This will be enhanced by the
use of TGARCH model in not only determining the impact of news on stock returns, but narrow it down to good
news and bad news and the persistence of the news on stock return volatility.
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