An Empirical Analysis of Relationship Between Gold Market and Stock Market of India

A Project Report Submitted to Utkal University in the Partial A Project Report Submitted to Utkal University in the Partial A Project Report Submitted to Utkal University in the Partial A Project Report Submitted to Utkal University in the Partial

Fulfillment of Degree of Master of Finance & Control.Fulfillment of Degree of Master of Finance & Control.Fulfillment of Degree of Master of Finance & Control.Fulfillment of Degree of Master of Finance & Control.

(2009(2009(2009(2009----11)11)11)11)

Submitted by:Submitted by:Submitted by:Submitted by:

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Under the Under the Under the Under the Guidance ofGuidance ofGuidance ofGuidance of

Prof. Jayanta Kumar ParidaProf. Jayanta Kumar ParidaProf. Jayanta Kumar ParidaProf. Jayanta Kumar Parida CoCoCoCo----ordinator, ordinator, ordinator, ordinator,

Master of Finance & ControlMaster of Finance & ControlMaster of Finance & ControlMaster of Finance & Control Utkal UniversityUtkal UniversityUtkal UniversityUtkal University

Master of Finance & Control P.G. Department of Commerce

Utkal University

Prof. Jayanta Kumar ParidaProf. Jayanta Kumar ParidaProf. Jayanta Kumar ParidaProf. Jayanta Kumar Parida Co-ordinator, MFC

P.G. Department of Commerce

Utkal University

CertificateCertificateCertificateCertificate

This to certify that the project entitled ““““An An An An

Empirical Study on the Relationship between Gold Empirical Study on the Relationship between Gold Empirical Study on the Relationship between Gold Empirical Study on the Relationship between Gold

Market and Stock Market of IndiaMarket and Stock Market of IndiaMarket and Stock Market of IndiaMarket and Stock Market of India”””” is a record of bona

fide research work carried out by Dillip Khuntia Dillip Khuntia Dillip Khuntia Dillip Khuntia under

my supervision and guidance. It embodies result of his

original contribution. The project has reached the

standard of fulfilling the requirements of the regulation

relating to the degree of Master of Finance and Control.

No part of this project has been submitted to any other

institution for the award of any other degree.

I wish him all success in his future endeavours.

PLACE: BHUBANESWAR

DATE: ((((Prof. Jayanta Kumar ParidaProf. Jayanta Kumar ParidaProf. Jayanta Kumar ParidaProf. Jayanta Kumar Parida))))

P.G. Dept. of Commerce

Utkal University.

DECLARATION

I do hereby declare that the project entitled ““““An An An An

Empirical Study on the Relationship between Gold Empirical Study on the Relationship between Gold Empirical Study on the Relationship between Gold Empirical Study on the Relationship between Gold

Market and stock Market of IndiaMarket and stock Market of IndiaMarket and stock Market of IndiaMarket and stock Market of India”””” is submitted to

Utkal University for the partial fulfillment of degree of

Master of Finance and ControlMaster of Finance and ControlMaster of Finance and ControlMaster of Finance and Control, Utkal University. The

project is an authentic piece of work done by me under

the guidance of Prof. Jayanta Kumar ParidaProf. Jayanta Kumar ParidaProf. Jayanta Kumar ParidaProf. Jayanta Kumar Parida, Co-

ordinator, MFC, P.G. Department of Commerce, Utkal

University and it has neither been submitted for award

of any other degree to any other University, Academy,

Institution nor published in any magazine or anywhere

else in part or full to best of my knowledge.

PLACE: BHUBANESWAR ((((DILLIP KHUNTIADILLIP KHUNTIADILLIP KHUNTIADILLIP KHUNTIA))))

DATE:

ACKNOWLEDGEMENT

The satisfaction that accompanies the successful completion of

any task would be incomplete without mentioning people who made

it possible, whose encouragement and consistent guidance crowned

my efforts with success.

I would like to express our heartfelt indebtedness and deep

sense of gratitude to my faculty guide Prof. Jayanta Kumar PProf. Jayanta Kumar PProf. Jayanta Kumar PProf. Jayanta Kumar Paridaaridaaridaarida,,,,

CoCoCoCo----ordinator, Master of Finance & Cordinator, Master of Finance & Cordinator, Master of Finance & Cordinator, Master of Finance & Control,ontrol,ontrol,ontrol, P.G. Department of

Commerce, Utkal University for sharing his knowledge and giving

me guidance and generous co-operation.

I am also thankful to faculties of P.G. Department of

commerce, Utkal University, Prof. Samson Moharana, Prof. R.K.

Bal, Dr. P.K. Pradhan, Dr. K.B. Das, Dr. P.K. Hota, Dr. M.

Sahoo, Dr. A.K. Swain, Dr. S.K. Digal, Mr. R.K. Swain and Mr. J.

Jhunjhunwale for their support and encouragement.

I would like to thank my friends for their support and

encouragement without which completion of this project would be

difficult.

PLACE: BHUBANESWAR ((((DILLIP KHUNTIADILLIP KHUNTIADILLIP KHUNTIADILLIP KHUNTIA))))

DATE:

EXECUTIVE SUMMERY

The study of the stock market of a country in terms of a wide range of

macro-economic and financial variables has been the subject matter of many

researches since last few decades. Empirical studies reveal that once financial

deregulation takes place, the stock markets of a country become more sensitive to

both domestic and external factors and one such factor is the gold.

In this project “An Empirical Study of Relationship between Gold Market

and Stock Market of India”, the major objective was to find out the relationship

between the volatility of stock market with that of the investment pattern of the

gold market in Indian scenario i.e. the impact of the volatility of stock market on

the gold market.

For pursuing the study relevant data were collected from various secondary

sources, which include the website of Reserve Bank of India (RBI), Securities and

Exchange Board of India (SEBI), Multi-commodity Exchange of India (MCX)

and others. Various journals and publications of the regulatory bodies and the

exchanges are followed for carrying out the research. The data collected are

tabulated and classified for analysis. The analysis is done by computerized

programs like SPSS Statistical Package, MS Excel etc.

From the study it is revealed that the volatility of the stock market is

relatively very high as compared to the gold market. The gold market imparted a

low growth in volatility, whereas the stock market has shown a wider range of

fluctuation. This is mainly due to the global financial crisis and its impact. FII

investment and under-developed mutual fund industry also contributed to this

volatility.

From the regression analysis, it is found that there exists a strong

relationship between the gold and the stock market of India. The volatility of stock

market has a significant impact on the gold market. It is observed that, when the

stock market is very volatile, investors generally shift to gold market for the safety

of their investment.

Thus the suggestions that emerged from the study are;

�

The government and the regulatory bodies should take aggressive steps to

bring down the volatility of the stock market.

�

Mutual fund industries should be encouraged and the investors should be

trained. The regulator, the exchanges and the other concerned government

bodies should conduct awareness programs to make the investors aware

about various facets of the stock market.

CONTENTCONTENTCONTENTCONTENT

Certificate……………………………………………………………………………………………………………………………i

Declaration…………………………………………………………………………………………………………………………ii

Acknowledgement………………………………………………………………………………………………………………iii

Executive Summery……………………………………………………………………………………………………….....iv

CHAPTER ONE: INTRODUCTION

1.1 Introduction……………………………………………………………………………………………………………..2

1.2 Rationale………………………………………………………………………………………………………………….2

1.3 Objective………………………………………………………………………………………………………………..3

1.4 Research Methodology………………………………………………………………………………………….3

1.5 Limitation…………………………………………………………………………………………………………….....4

1.6 Chapterisation………………………………………………………………………………………………………..4

CHAPTER TWO: THE STOCK MARKET OF INDIA

2.1 Introduction to Capital Market…………………………………………………………………………………7

2.2 Primary Market………………………………………………………………………………………………………7

2.3 Secondary Market: The Stock Market……………………………………………………………………9

2.4 History of Indian Stock Market – The Origin…………………………………………………………10

2.4.1 Pre-independence Scenario – Establishment of different Stock Exchanges…11

2.4.2 Post Independence Scenario………………………………………………………………..………….12

2.5 Present Scenario……………………………………………………………………………………………………14

CHAPTER THREE: THE GOLD MARKET OF INDIA

3.1 Indian Gold Market………………………………………………………………………………………………..22

3.2 Jewellery Consumption………………………………………………………………………………………..23

3.3 Investment Demand………………………………………………………………………………………………24

3.4 Gold Exchange Traded Funds………………………………………………………………………………..25

3.5 Decorative and Industrial Demand……………………………………………………………………….26

3.6 Gold Imports…………………………………………………………………………………………….……………27

3.7 Reserves with Reserve Bank of India…………………………………………………….…………….27

3.8 Recycled Gold Supply…………………………………………………………………………….……………..27

3.9 India in World Gold Industry………………………………………………………………….……………..28

3.10 Indian Gold Market – Present Scenario………………………………………………………………28

3.11 Market Moving Factors…………………………………………………………………………………………29

CHAPTER FOUR: VOLATILITY AND REGRESSION ANALYSIS

4.1 Volatility…………………………………………………………………………………………………………..……32

4.2 Regression……………………………………………………………………………………………….…………..33

4.2.1Simple Regression……………………………………………………………………………………………....33

4.2.2 Multiple Regression……………………………………………………………………………………………37

4.3 Essential Assumptions and Statistical Properties of Regression………………………39

CHAPTER FIVE: ANALYSIS AND INTERPRETATION

5.1 Volatility………………………………………………………………………………………………………….43

5.2 Regression Analysis………………………………………………………………………………………46

CHAPTER SIX: CONCLUSION

6.1 Major Findings………………………………………………………………………………………………….50

6.1.1 Volatility…………………………………………………………………………………………………………50

6.1.2 Gold Market of India………………………………………………………………………………………51

6.1.3 Regression……………………………………………………………………………………………………52

6.2 Recommendations…………………………………………………………………………………………..54

6.3 Conclusion………………………………………………………………………………………………………55

BIBLIOGRAPHY ………………………………………………………………………………………………………………….56

ANNEXTURE………………………………………………………………………………………………………………………..59

LIST OF FIGURESLIST OF FIGURESLIST OF FIGURESLIST OF FIGURES

Figure – 2.1 Total Capital Raised Through IPO…………………………………………………………………….8

Figure – 2.2 Resources Mobilized from the Primary Market…………………………………………….9

Figure – 2.3 Share of Broad Categories of Issues in Resource Mobilization…………………..15

Figure – 2.4 Sector-wise Resource Mobilization………………………………………………………………16

Figure – 2.5 Movement of Benchmark Stock Market Indices (2009-10)…………………………17

Figure – 2.6 Movement of Sectoral Indices of BSE (2009-10)…………………………………………17

Figure – 2.7 Movement of Sectoral Indices of NSE (2009-10)…………………………………………18

Figure – 2.8 Turnover of SENSEX and Nifty………………………………………………………………………19

Figure – 2.9 Market Capitalization of SENSEX and Nifty…………………………………………………..20

Figure – 3.1 Total Gold Demand…………………………………………………………………………………………22

Figure – 3.2 India gold market as a % of global gold market, tonnage terms (2009)……23

Figure – 4.3 Gold Jewellery Consumption in India…………………………………………………………..23

Figure – 4.4 Indian Gold Investment Demand…………………………………………………………………..24

Figure – 3.5 Gold ETFs………………………………………………………………………………………………………25

Figure – 4.6 Gold Volatility against Indian Indices……………………………………………………………26

Figure – 4.7 Indian Gold Decorative and Industrial Demand……………………………………………26

Figure – 5.1 Comparison between Spot and Future Market Volatility of Gold in Indian

Context………………………………………………………………………………………………………………………………43

Figure – 5.2 Comparison between Prices of Spot and Futures Market of Gold in Indian

Context………………………………………………………………………………………………………………………………44

Figure – 5.3 Comparison between Volatility of SENSEX, Spot and Futures Market of

Gold…………………………………………………………………………………..……………………………………………….44

Figure – 5.4 Comparison of Volatility of SENSEX and Gold Spot Market…………..……………45

Figure – 5.5 Volatility of Gold Futures and SENSEX…………………………………………………………46

LIST OF TABLESLIST OF TABLESLIST OF TABLESLIST OF TABLES

Table – 5.1 Regression Model between Gold Spot and Volatility of SENSEX……………………46

Table – 5.4 R2 between Gold Futures and SENSEX…………………………………………………………..47

Table – 5.3 Regression between Gold Futures and Volatility of SENSEX…………………………47

Table – 5.2 R2 between Volatility of S ENSEX and Gold Spot over the Years……………..……48

Introduction

1.1 INTRODUCTION

The study of the stock market of a country in terms of a wide range of macro-

economic and financial variables has been the subject matter of many researches since

last few decades. Empirical studies reveal that once financial deregulation takes place,

the stock markets of a country become more sensitive to both domestic and external

factors and one such factor is the gold. From 1900 to 1971, with the global systems of

gold standard and USD standard, gold price was regulated. But, since 1972, gold has

been disconnected from the USD. Particularly in 1976 when the International Monetary

Fund (IMF) passed Jamaica Agreement, did gold begin to evolve from currency to

ordinary merchandise and since then gold price has been determined by market supply

and demand. And, in India, the government started the process of globalization and

liberalization since 1991 which allowed prices to be determined by the market forces.

Since then, the government has been taking a number of steps to reform the

gold sector and ensure that India benefits from the demand-influence that it has on the

gold business internationally. The liberalization of the gold sector has been made in

stages; first allowing a number of banks to import gold – braking the monopoly of the

State Trading Corporations; then considerably reducing the import duty – destroying a

lucrative parallel smuggling channel and now, allowing traders, manufacturers as well

as investors to trade in gold futures in India itself.

Thus the gold market after deregulation exhibited more volatility and there is

subsequent increase in the price and investment pattern of gold market. Though the

volatility of gold market increased, but still it is considered as one of the safest venture

of investment. In India it is the second most preferred investment venture. For this

reason, investors prefer to invest in gold market, when the stock market is very volatile.

1.2 RATIONALE

The globalization policy of government has opened up the economy and

deregulated the markets. But these market deregulations, structural reforms in global

trade and technological development has revolutionized the financial market. A by-

product of this revolution is increased volatility of the market. The volatility of the

market is influenced by many factors. Gold is one of them.

Similarly the volatility of the stock market has a significant impact on the gold

market. A higher volatility in the stock market results higher investment in the gold

market, as gold is considered as a secure investment. This is because, gold is not

affected by the fluctuation in market fundamentals and it is used as a hedging

instrument for risk minimization. In this concern the study of the relationship between

the volatility of stock market with that of the investment in gold market is of prime

importance. A comprehensive study of the gold market and its volatility pattern and its

comparison with the stock market will depict the relationship between these two

markets. From which the investors can better assess the risk and return of the markets

and can take investment decisions accordingly.

1.3 OBJECTIVE OF THE STUDY

On the above premise, the following are laid down as the objectives of this study;

I. To analyze the causes and effects of volatility of stock market and compare it

with the volatility of gold market of India.

II. To carry out a comprehensive analysis of the gold market of India.

III. To find the relationship of gold spot and futures market.

IV. To study the relationship between the volatility of stock market of India with

that of the Gold market.

1.4 RESEARCH METHODOLOGY

1.4.1 SCOPE

The scope of this project is confined to the study the causes and effects of the

volatility of stock market, study of the gold market of India and the study of the

relationship the volatility of the stock market with the gold market. The software used

for this purpose, details about the process and the tools of analysis and the various

problems faced in this project.

1.4.2 SOURCES OF DATA

The data has been collected from the secondary source only. The secondary data

are collected from various books and journals, Securities and Exchange Board of India

circulars, annual report of Securities and Exchange Board of India, Handbook on

Statistics of Reserve Bank of India and the Securities and Exchange Board of India and

the Commodity Year Book of Multi Commodity Exchange Limited. Most of the price

information of both gold market and the stock market is collected from various

websites like, www.bseindia.com, www.sebi.gov.in, www.mcxindia.com,

www.rbi.gov.in, etc.

1.4.3 TOOLS AND TECHNIQUES

The data so collected were classified and tabulated for analysis and

interpretation. The tools and techniques used in this project are all computerized

programming. The data are programmed in MS Excel for finding out the volatility of

gold and the stock market of India. To find out the relationship between the gold

market and the stock market, regression models are built. For this purpose SPSS

Statistical Package is used.

1.5 LIMITATIONS

Some of the limitations that are faced during the study are;

I. The information collected is limited by authenticity and accuracy. Also some of

the relevant information is not available in the website of the exchanges or the

regulatory body.

II. The time predefined for this project is very short for covering such a vast study.

1.6 CHAPTERISATION

This project focuses on the relationship between the stock market and the gold

market of India. Various technicalities that are involved in this process has been

extensively discussed and dealt with this report. The report contains the following

seven chapters, which are summarized below.

Chapter one starts with introduction to the project report, stating the importance,

objectives and research methodology adopted. Limitations faced during the study are

also laid down. Chapter two deals with the capital market of India. A detailed study of

the stock market of India is carried out, which includes the history and present

prospects of the stock market. Chapter three gives an idea of the gold market of India,

which is a comprehensive analysis of the gold market of India and has been carried out

both in spot and futures market. Chapter four contains the conceptual aspects of

volatility and regression. A detailed theoretical prospect of both volatility and the

regression was presented therein. The fifth chapter comprises of the analysis and

interpretation of the data. The ultimate chapter aims at giving the findings, summary

and the conclusion of this project.

The Stock Market

of India

2.1 INTRODUCTION TO CAPITAL MARKET

Essentially, capital is wealth, usually in the form of money or property. Capital

markets exist when two groups interact: those who are seeking capital and those who

have capital to provide. The capital seekers are the businesses and governments who

want to finance their projects and enterprises by borrowing or selling equity stakes. The

capital providers are the people and institutions who are willing to lend or buy,

expecting to realize a profit.

Investment capital is wealth that you put to work. You might invest your capital

in business enterprises of your own. But there’s another way to achieve the same goal:

Let someone else do the investing for you. By participating in the stock and bond

markets, which are the pillars of the capital markets, you commit your capital by

investing in the equity or debt of issuers that you believe have a viable plan for using

that capital. Because so many investors participate in the capital markets, they make it

possible for enterprises to raise substantial capital, which is enough to carry out much

larger projects than might be possible otherwise. The amounts they raise allow

businesses to innovate and expand, create new products, reach new customers, improve

processes, and explore new ideas. They allow governments to carry out projects that

serve the public through building roads and firehouses, training armies, or feeding the

poor, for example. All of these things could be more difficult, perhaps even impossible

to achieve without the financing provided by the capital marketplace.

The capital market can be categorized into two parts, i.e. primary market and

the secondary market. The primary market deals with the raising of capital by the

government and the corporate entities, whereas the secondary market deals with the

trading of the shares of the companies.

2.2 PRIMARY MARKET

There are actually two levels of the capital markets in which investors

participate: the primary markets and the secondary markets. Businesses and

governments raise capital in primary markets, selling stocks and bonds to investors and

collecting the cash. Essentially primary market is one of the mechanisms from where

the government or the corporate entities raise capital for the first time through initial

public offerings (IPO). FPO is another way of raising capital from the market in which

the entity raises capital from the market for the second time. The primary market also

includes the rights issue and private placement. When new shares are issued infavour of

the existing shareholders or the owners, it is known as rights issue. Similarly, when the

shares are privately issued to the institutional investors or high net-worth individuals, it

is known as private placement.

The figures below depict the present scenario of the primary market of India.

Figure 2.1 show about the capital raised through initial public offerings and figure 2.2

shows the trend of primary market in India.

Figure – 2.1 Total Capital Raised Through IPO

The above figure shows about the amount of capital mobilized from the primary

market through initial public offerings (IPOs). It can be seen from the figure that the

more than 15,000 Cr rupees was raised from IPO issue in 1993-94, but after that, the

amount raised through IPOs came down. It was nearly about zero in 1998-99. But from

there the amount raised through IPOs increased and it was highest in 2007-08.

Suddenly, in 2008 there was a down fall in number of IPOs issue, because of the

recessionary effect in the global market. But recently there are some IPO issues, which

got a good response in the market.

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

IPO (Rs. Crore)

From 1998-99 to 2003-04, the IPOs market in India was virtually non-existence.

This may be due to increased volatility of the market at that point of time and because

of the scams that hit the market one after another, demising the investor’s confidence in

the market. But from 2003-04, the market showed an upward trend, because of good

economic conditions. The IPOs market in India is also very much volatile; it needs

certain reforms for its growth and development.

Figure – 2.2 Resources Mobilized from the Primary Market

The above figure depicts the amount of capital that is being raised from the

primary market of India from 1993 to 2009. It can be seen that, more capital is being

mobilized for productive purposes from 2002-03. During 2007-08, the primary market

of India is at its pick. But it all down suddenly, because of the economic downfall.

2.3 SECONDARY MARKET: THE STOCK MARKET

In secondary markets, investors buy and sell the stocks and bonds among

themselves or more precisely, through intermediaries. While the money raised in

secondary sales doesn’t go to the stock or bond issuers, it does create an incentive for

investors to commit capital to investments in the first place. The stock markets are

essentially the secondary market, where buying and selling of the shares take place.

The working of stock exchanges in India started in 1875. BSE is the oldest

stock market in India. The history of Indian stock trading starts with 318 persons taking

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

Resources Mobilised from the Primary Market

membership in Native Share and Stock Brokers Association, which we now know by

the name Bombay Stock Exchange or BSE in short. In 1965, BSE got permanent

recognition from the Government of India. National Stock Exchange comes second to

BSE in terms of popularity. BSE and NSE represent themselves as synonyms of Indian

stock market. The history of Indian stock market is almost the same as the history of

BSE.

The 30 stock sensitive index or SENSEX was first compiled in 1986. The

SENSEX is compiled based on the performance of the stocks of 30 financially sound

benchmark companies.

2.4 HISTORY OF THE INDIAN STOCK MARKET - THE ORIGIN

Stock markets refer to a market place where investors can buy and sell stocks.

The price at which each buying and selling transaction takes is determined by the

market forces i.e. demand and supply for a particular stock. Let us take an example for

a better understanding of how market forces determine stock prices. ABC Co. Ltd.

enjoys high investor confidence and there is an anticipation of an upward movement in

its stock price. More and more people would want to buy this stock (i.e. high demand)

and very few people will want to sell this stock at current market price (i.e. less

supply). Therefore, buyers will have to bid a higher price for this stock to match the ask

price from the seller which will increase the stock price of ABC Co. Ltd. On the

contrary, if there are more sellers than buyers (i.e. high supply and low demand) for the

stock of ABC Co. Ltd. in the market, its price will fall down.

In earlier times, buyers and sellers used to assemble at stock exchanges to make

a transaction but now with the dawn of IT, most of the operations are done

electronically and the stock markets have become almost paperless. Now investors

don’t have to gather at the Exchanges, and can trade freely from their home or office

over the phone or through Internet. One of the oldest stock markets in Asia, the Indian

Stock Markets has a 200 years old history.

18th

Century

East India Company was the dominant institution and by end of the

century, business in its loan securities gained full momentum

1830's Business on corporate stocks and shares in Bank and Cotton presses

started in Bombay. Trading list by the end of 1839 got broader

1840's Recognition from banks and merchants to about half a dozen brokers

1850's Rapid development of commercial enterprise saw brokerage business

attracting more people into the business

1860's The number of brokers increased to 60

1860-61 The American Civil War broke out which caused a stoppage of cotton

supply from United States of America; marking the beginning of the

"Share Mania" in India

1862-63 The number of brokers increased to about 200 to 250

1865 A disastrous slump began at the end of the American Civil War (as an

example, Bank of Bombay Share which had touched Rs. 2850 could only

be sold at Rs. 87)

2.4.1PRE-INDEPENDENCE SCENARIO - ESTABLISHMENT OF DIFFERENT

STOCK EXCHANGES

1874 With the rapidly developing share trading business, brokers used to gather

at a street (now well known as "Dalal Street") for the purpose of

transacting business.

1875 "The Native Share and Stock Brokers' Association" (also known as "The

Bombay Stock Exchange") was established in Bombay

1880's Development of cotton mills industry and set up of many others

1894 Establishment of "The Ahmedabad Share and Stock Brokers' Association"

1880 - 90's Sharp increase in share prices of jute industries in 1870's was followed by

a boom in tea stocks and coal

1908 "The Calcutta Stock Exchange Association" was formed

1920 Madras witnessed boom and business at "The Madras Stock Exchange"

was transacted with 100 brokers.

1923 When recession followed, number of brokers came down to 3 and the

Exchange was closed down

1934 Establishment of the Lahore Stock Exchange

1936 Merger of the Lahoe Stock Exchange with the Punjab Stock Exchange

1937 Re-organisation and set up of the Madras Stock Exchange Limited (Pvt.)

Limited led by improvement in stock market activities in South India with

establishment of new textile mills and plantation companies

1940 Uttar Pradesh Stock Exchange Limited and Nagpur Stock Exchange

Limited was established

1944 Establishment of "The Hyderabad Stock Exchange Limited"

1947 "Delhi Stock and Share Brokers' Association Limited" and "The Delhi

Stocks and Shares Exchange Limited" were established and later on

merged into "The Delhi Stock Exchange Association Limited"

2.4.2 POST INDEPENDENCE SCENARIO

The depression witnessed after the Independence led to closure of a lot of

exchanges in the country. Lahore Stock Exchange was closed down after the partition

of India, and later on merged with the Delhi Stock Exchange. Bangalore Stock

Exchange Limited was registered in 1957 and got recognition only by 1963. Most of

the other Exchanges were in a miserable state till 1957 when they applied for

recognition under Securities Contracts (Regulations) Act, 1956. The Exchanges that

were recognized under the Act were:

1. Bombay

2. Calcutta

3. Madras

4. Ahmedabad

5. Delhi

6. Hyderabad

7. Bangalore

8. Indore

Many more stock exchanges were established during 1980's, namely:

1. Cochin Stock Exchange (1980)

2. Uttar Pradesh Stock Exchange Association Limited (at Kanpur, 1982)

3. Pune Stock Exchange Limited (1982)

4. Ludhiana Stock Exchange Association Limited (1983)

5. Gauhati Stock Exchange Limited (1984)

6. Kanara Stock Exchange Limited (at Mangalore, 1985)

7. Magadh Stock Exchange Association (at Patna, 1986)

8. Jaipur Stock Exchange Limited (1989)

9. Bhubaneswar Stock Exchange Association Limited (1989)

10. Saurashtra Kutch Stock Exchange Limited (at Rajkot, 1989)

11. Vadodara Stock Exchange Limited (at Baroda, 1990)

12. Coimbatore Stock Exchange

13. Meerut Stock Exchange

Now there are about 25 recognized stock exchanges in India, out of which 19

are regional exchanges and 6 are national level stock exchanges. From these only two

stock exchanges are efficiently working with a nationwide reach, i.e. NSE and BSE.

The other stock exchanges are virtually non-existence.

2.5 PRESENT SCENARIO

The primary market segment witnessed positive trend during 2009-10. Earlier,

in 2008- 09, the volatility in stock markets, slowdown in economic growth, slackening

of expansion plans by corporate and poor investor response had led to a sharp fall in the

number of issues and amounts raised through the primary market. About 87.7 percent

of resource mobilization was through public issues of issue-size above Rs.500 crore

during 2009-10 compared to 78.1 percent during the previous financial year. Also, the

number of issues under category of issues having size of Rs.500 crore or above

increased to 21 in 2009- 10 from six in 2008-09. The average size of the issue more

than doubled in 2009-10 to Rs.757 crore from Rs.345 crore in 2008-09. Not only the

average size of issues improved substantially in 2009- 10, but also the fact that issues

under Rs.100 crore constituted just 2.2 percent of total resources mobilization shows

that bigger issues were clearly the flavour of the market.

During 2009-10, 76 issues accessed the primary market and raised Rs.57,555

crore through public (47) and rights issues (29) as against 47 issues which raised

Rs.16,220 crore in 2008-09 through public (22) and rights issues (25) . Due to better

financial environment, there were 39 IPOs during 2009-10 as against 21 during 2008-

09. The amount raised through IPOs during 2009-10 was significantly higher at

Rs.24,696 crore as compared to Rs.2,083 crore during 2008-09. The share of public

issues in the total resource mobilisation increased to 85.6 percent during 2009-10 from

22.1 percent in 2008-09 whereas share of rights issues declined from 77.9 percent in

2008-09 to 14.5 percent in 2009-10. It is observed that the share of IPOs has ranged

from 12.8 percent to 85.1 percent; FPOs from zero percent to 80.9 percent and rights

issues from 4.3 percent to 77.9 percent.

Sector-wise classification reveals that 70 private sector and six public sector

issues mobilised resources through primary market during 2009-10 as compared to 47

private sector issues in 2008-09. These companies raised Rs.57,555 crore though 76

issues in 2009-10 as compared to Rs.16,220 crore through 47 issues in 2008-09. The

share of private sector in total resource mobilisation declined from 100 percent in 2008-

09 to 45.9 percent in 2009-10. The analysis of last eight years since 2002-03 reveals

that 2008-09 was the only financial year wherein the public sector companies did not

participate in resource mobilisation through primary market.

Industry-wise classification reveals that Power sector accounted for 43.9 percent

of total resource mobilisation in the primary market as number of power sector

companies, namely, Adani Power, Indiabulls Power, NTPC, NHPC etc, accessed the

primary market. The power sector was followed by Banks/FIs with 5.5 percent, Cement

and Construction with 4.8 percent and Entertainment sector with 4.3 percent. In terms

of number of issues, Entertainment sector led the pack with nine issues during 2009-10

as compared to Textile sector seeing the highest number of five issues during 2008-09.

Figure – 2.3 Share of Broad Categories of Issues in Resource Mobilization

Figure – 2.4 Sector-wise Resource Mobilization

Equity markets witnessed significant uptrend during 2009-10 as compared to

downward and volatile trend in 2008-09. However, at times, the domestic markets

reflected the uncertainties in international financial market during the financial year

under review.

Markets were characterized by some bouts of volatility during the year, but it

rewarded investors by giving five year best return in 2009-10. The BSE SENSEX and

S&P CNX Nifty appreciated by 80.5 percent and 73.8 percent, respectively, over

March 31, 2009. The BSE SENSEX increased 7819 points to close at two year high

level at 17528 on March 31, 2010 from 9709 on March 31, 2009. The S&P CNX Nifty

also increased 2228 points to close at 5249 points at the end of March 2010 over 3021

at the end of March 2009 mainly driven by higher growth rate, positive sentiments in

market, better global environment, and FII inflows.

Indian markets had recorded substantial decline and volatility in 2008-09.

However, the environment improved in 2009-10 and got better with every subsequent

quarter. In tandem with the increase in stock prices in 2009-10, there was a significant

increase in turnover and market capitalization across the board.

Figure – 2.5 Movement of Benchmark Stock Market Indices (2009-10)

Figure – 2.6 Movement of Sectoral Indices of BSE (2009-10)

Figure – 2.7 Movement of Sectoral Indices of NSE (2009-10)

In the cash segment, the turnover at BSE and NSE increased by 25.4 percent

and 50.4 percent respectively during 2009-10 as compared to decline witnessed at BSE

and NSE by 30.3 percent and 22.5 percent, respectively during 2008-09. Derivatives

turnover showed substantial decline at BSE by 98.1 percent while that at NSE gained

by 60.4 percent over the previous year.

In 2009-10, global equity markets improved significantly and some markets

especially emerging markets showed sharp turnaround. This was unlike the situation

witnessed in 2008-09 when the global equity markets were dominated by the financial

turmoil and the signs of severe recession inflicted by the deteriorating financial

markets.

During 2009-10, all the equity markets witnessed uptrend, however, in different

magnitude. The upward trend was the highest for BUX index of Hungary (118.9

percent) followed by Argentina IBG (112.7 percent) and Russian CRTX index (104.8

percent). However, the Indian benchmark indices namely BSE SENSEX and S&P

CNX Nifty gave year on year return of 80.5 percent and 73.8 percent respectively in

2009-10.

In tandem with the upward trend in equity prices, there was an increase in the

trading volumes in stock exchanges in 2009- 10. During 2009-10, turnover of all stock

exchanges in the cash segment increased by 43.3 percent to Rs.55,18,470 crore from

Rs.38,52,579 crore in 2008-09. BSE and NSE together contributed 99.9 percent of the

turnover, of which NSE accounted for 74.9 percent in the total turnover in cash market

whereas BSE accounted for 24.9 percent to the total. Apart from NSE and BSE, the

only two stock exchanges which recorded turnover during 2009-10 were Calcutta Stock

Exchange Ltd. and UPSE. There was hardly any transaction on other stock exchanges.

Month-wise, BSE and NSE together recorded the highest turnover in June 2009

followed by July 2009 and May 2009.

Figure – 2.8 Turnover of SENSEX and Nifty

The market capitalization of BSE increased by 99.8 percent to Rs.61,65,619

crore in 2009-10 from Rs.30,86,075 crore in 2008-09. The upward trend in the stock

markets resulted in similar trend in market capitalization of many indices in 2009-10.

Among the indices of BSE, increase in market capitalization was the highest for

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Bankex (136.9 percent), BSE PSU (82.6 percent) and BSE Teck (80.3 percent) over the

previous year (Table 2.13). However, in terms of absolute value, among these three

indices, BSE PSU led the pack with market capitalization of Rs.17,33,662 crore

followed by BSE Teck at Rs.7,40,817 crore and BSE Bankex at Rs.5,54,127 crore . At

NSE, market capitalization increased by 107.5 percent to Rs.60,09,173 crore from

Rs.28,96,194 crore at the end of March 2009. At NSE, among sectoral indices, increase

in market capitalisation was the highest for CNX Pharma (101.1 percent). This was

followed by CNX Bank (41.6 percent) and CNX IT (13.3 percent).

Figure – 2.9 Market Capitalization of SENSEX and Nifty

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The Gold Market

of India

Gold has a very special place in history. It has been treasured since ancient

times and simple gold ornaments are among the earliest known metal objects made by

humans. Gold was so sought after that, in early times, alchemists tried to turn other

metals into precious gold! Gold has affected where and how people live, and many

towns have been developed by the wealth from gold mining.

Gold has featured in many myths and legends, including King Midas, King

Solomon, and Jason and the Argonauts. Fairytales often mention golden objects such as

eggs or harps, and most people have heard of the golden pot at the end of the rainbow.

Even today, achievements are rewarded by gold medals, and we associate the word

gold with greatness - as in ‘golden rules’ or ‘good as gold’. Gold has always been, and

still is, a very important metal. Its rarity and unique properties make it one of the most

prized and useful metals.

3.1 INDIAN GOLD MARKET

India’s centuries‐old gold industry is the worldʹs biggest market for the metal,

with imports meeting almost all the country’s requirements for jewellery and

investment. India owns over 18,000 tonnes of above ground gold stocks worth

approximately $ 800 billion and representing at least 11 per cent of global stock,

according to estimates of World Gold Council.

Figure – 3.1 Total Gold Demand

Figure – 3.2 India gold market as a % of global gold market, tonnage terms (2009)

Gold jewellery demand in India, the world’s largest gold jewellery market, rose

67% year‐on‐year to 272 tonnes in the first half of 2010. Over the same period, the

average domestic gold price surged to almost Rs 52,800/oz, before hitting a new high

of Rs 60,460/oz on 15 October 2010. Over the past ten years, the value of gold demand

in India has increased at an average rate of 13 % per year, outpacing the countryʹs real

GDP, inflation and population growth by 6 %, 8 % and 12 % respectively.

3.2 JEWELLERY CONSUMPTION

Gold jewellery accounted for around 75% of total Indian gold demand in 2009, the remainder being investment (23%) and decorative and industrial (2%). Indian consumers also regard gold jewellery as an investment and are well aware of gold’s benefit as a store of value.

Figure – 4.3

Wedding‐related demand accounts for a substantial proportion of overall

jewellery demand. This is particularly true in the south of India, where the most popular

wedding jewellery sets tend to be the more traditional, intricate but bulky styles in

heavier weights. In the northern cities there has been a trend towards more ‘western’

styles, and lighter wedding sets, as well as diamond‐set pieces, are becoming

increasingly popular.

In 2010, Indian gold jewellery consumption is likely to recover to near precredit

crisis level following the fall in demand in 2009. As consumers have adjusted their

price expectations upwards, a further rise in demand is anticipated.

In the longer term, India’s favourable demographic and age profile are likely to

ensure buoyant consumption growth, especially given the existing strong affinity to

gold in Indian culture. The improving economic position of many domestic consumers

will also play a part in determining demand for gold in coming years.

3.3 INVESTMENT DEMAND

Gold is an integral part of Indian society and a foundation of wealth and savings

in India. It is viewed as a secure, liquid investment, a capital and value preserver and is

the second preferred investment after bank deposits. Saving rates are estimated at

around 30% of total income of which we believe around 10% is invested in gold.

India’s gold investment revolution is gathering pace.

Figure – 4.4 Indian Gold Investment Demand

3.4 GOLD EXCHANGE TRADED FUNDS

The tonnage of gold in Indian gold Exchange Traded Funds (ETFs) remains

relatively small but there have been significant recent developments with the Indian

ETF market as investors seek greater access to more liquid gold investments. ETFs,

first brought to the Indian market just over 3 years ago, have grown in popularity as

investors seek exposure to gold within a fund structure. Total holdings from the Gold

Benchmark Exchange Traded Scheme, KOTAK Gold ETF, Quantum Gold Fund,

Reliance Gold Exchange Traded, UTI Gold Exchange Traded Fund, Religare Gold

Exchange Traded Fund and State of Bank of India (SBI) Gold Exchange Traded

Scheme amounted to approximately 11 tonnes by the end of August 2010, up 250% on

June 2007 from 3 tonnes.

Figure – 3.5

Gold ETFs

Figure – 4.6

3.5 DECORATIVE AND INDUSTRIAL DEMAND

Since 1992, approximately 22 tonnes of gold per annum have been used in

domestic decorative and industrial applications. This sector accounted for nearly 3% of

Indian gold demand in 2009. Industrial and decorative demand for gold in the country

is driven primarily by the use of jari, a gold thread used in clothing (particularly in the

weaving of wedding saris).

Figure – 4.7 Indian Gold Decorative and Industrial Demand

There is also growth in the electronics manufacturing sector in India,

particularly in regions such as Bangalore and this may well provide an additional driver

for gold demand in the coming years. The recent demand performance is seen as a

trough, given the increasing acceptance of higher prices, the recovering global

economic outlook and improving domestic living standards.

3.6 GOLD IMPORTS

Indian gold imports play an important role in the domestic gold market since

India currently produces around 0.5% of its annual gold consumption. The value of

annual gold imports increased by 1,015% between 1992 and 2009. In 1992, gold

imports were approximately Rs88 bn, this increased to Rs881 bn by the end of 2009.

3.7 RESERVES WITH RESERVE BANK OF INDIA

Recent developments have seen the Reserve Bank of India (RBI) purchasing

200 tonnes of gold from the IMF as a result of partially restoring a prior relationship

within its reserves. The deal was completed in October 2009, when the gold price was

trading around Rs 49,000/oz (or US$1,048), and was announced in early November

2009.

3.8 RECYCLED GOLD SUPPLY

Since 1992, Indians have recycled an average of 92 tonnes of gold per annum.

In 2009, the supply of domestic recycled gold rose 29% to 116 tonnes while domestic

gold demand fell by 19%. Historically, recycling activity has been sensitive to general

economic conditions, the price of gold and price expectations. This is attributable to the

fact that gold functions both as savings and as a form of money in India – i.e. gold is a

tradable, liquid asset.

3.9 INDIA IN WORLD GOLD INDUSTRY

(Rounded Figures) India (In

Tons)

World (In

Tons)

%

Share

Total Stocks 15000 160000 9

Central Bank holding 558 30,100 2

Annual Production 3 2450 0

Annual Recycling 250 1100 23

Annual Demand 700 3550 20

Annual Imports 600 --- ---

Annual Exports 60 --- ---

3.10 INDIAN GOLD MARKET – PRESENT SCENARIO

• India is the world's largest consumer of gold. Indians normally buy about 25 per

cent of the world's gold, purchasing around 700 - 750 tonnes of gold every year.

• However, the sharp price increase in 2008 and 2009 has impacted demand with

total demand in 2008 dipping to 660 tonnes. It is further expected to shrink in 2009

with demand in first three quarters of 2009 totaling only around 265 tonnes against

553.5 tonnes in the same period of the previous year.

• As India's domestic primary production of gold is very less, at around 2-3

tonnes a year, the country imports most of its domestic requirement.

• Thus, India is also the largest importer of the yellow metal and has averaged

imports of around 600 tonnes a year. However, 2008 imports dipped to around 400

tonnes of gold and it is further expected to dip to around 200-220 tonnes in 2009 owing

to high prices.

• India's gold demand is firmly embedded in cultural and religious traditions. It is

also valued in India as a savings and investment vehicle and is the second preferred

investment after bank deposits.

• Gold hoarding tendency is well engrained in the Indian society and unofficial

stocks held by Indians is estimated to be well above 15,000 tonnes, which is around 9%

of the total global gold stocks.

• Domestic consumption is dictated by monsoon, harvest and marriage season.

Indian jewellery offtake is sensitive to price increases and even more so to volatility.

• In the cities gold is facing competition from the stock market and a wide range

of consumer goods.

• Facilities for refining, assaying, making them into standard bars, coins in India,

as compared to the rest of the world, are insignificant, both qualitatively and

quantitatively.

• In July 1997 the RBI authorized the commercial banks to import gold for sale or

loan to jewellers and exporters. At present, 13 banks are active in the import of gold.

This reduced the disparity between international and domestic prices of gold from 57

percent during 1986 to 1991 to 8.5 percent in 2001.

3.11 MARKET MOVING FACTORS

• Indian gold prices are highly correlated with international prices. However, the

fluctuations in the INR-US Dollar impact domestic gold prices and have to be closely

followed.

• The global prices are driven by a host of factors with macro-economic factors

like strength of the economy, rising importance of emerging markets, currency

movements, interest rates being major influencing factors.

• Supply-demand is a major influencer, amid rising global investor demand and

almost stable supplies.

• Shifts in official gold reserves, reports of sales/purchases by central banks act as

major price influencing factors, whenever such reports surface.

• The investment in gold is influenced by comparative returns from other markets

like stock markets, real estate other commodities like crude oil.

• Domestically, demand and consequently prices to some extent are influenced by

seasonal factors like marriages. The rural demand is influenced by monsoon,

agricultural output and health of the rural economy.

Volatility

And

Regression

Analysis

4.1 VOLATILITY

Peripatetic stock prices and their volatility, which have now become endemic

features of securities market, add to the concern. The growing linkages of national

markets in currency, commodity and stock with world markets and existence of

common players, have given volatility a new property – that of its speedy

transmissibility across markets.

To many among the general public, the term volatility is simply synonymous

with risk: in their view high volatility is to be deplored, because it means that security

values are not dependable and the capital markets are not functioning as well as they

should. Merton Miller (1991) the winner of the 1990 Nobel Prize in economics - writes

in his book Financial Innovation And Market Volatility …. “By volatility public seems

to mean days when large market movements, particularly down moves, occur. These

precipitous market wide price drops cannot always be traced to a specific news event.

Nor should th is lack of smoking gun be seen as in any way anomalous in market for

assets like common stock whose value depends on subjective judgement about cash

flow and resale prices in highly uncertain future. The public takes a more deterministic

view of stock prices; if the market crashes, there must be a specific reason.”

As a concept, volatility is simple and intuitive. It measures variability or

dispersion about a central tendency. To be more meaningful, it is a measure of how far

the current price of an asset deviates from its average past prices. Greater this deviation,

greater is the volatility. At a more fundamental level, volatility can indicate the strength

or conviction behind a price move. Volatility is measured by the standard deviation in

the price level of the stock or commodity.

Volatility or Standard deviation is the positive square root of the arithmetic

mean of the squares of the deviations of the given values from their arithmetic mean.

For the frequency distribution xi/fi; i = 1,2,…….,n.

σ = √1/N∑fi(xi-x)2

The annualized volatility σ is the standard deviation of the instruments yearly.

The generalized volatility σT for time horizon T in years is expressed as:

Therefore, if the daily logarithmic returns of a stock have a standard deviation

of σSD and the time period of returns is

The formula used above to convert returns or volatility measures from o

period to another assume a particular underlying model or process. These formulas are

accurate extrapolations of a, or Wiener process, whose steps have finite variance.

However, more generally, for natural stochastic processes, the precise relation

between volatility measures for different time periods is more complicated. Some use

the Lévy stability exponent

4.2 REGRESSION

Regression analysis is a statistical tool for the investigation of relationships

between variables. Usually, the investigator seeks to ascertain the causal effect of one

variable upon another—the effect of a price increase upon demand, for example, or the

effect of changes in the money supply upon the inflation rate. To explore such is

the investigator assembles data on the underlying variables of interest and employs

regression to estimate the quantitative effect of the causal variables upon the variable

that they influence. The investigator also typically assesses the “statistica

of the estimated relationships, that is, the degree of confidence that the true relationship

is close to the estimated relationship.

4.2.1 SIMPLE REGRESSION

In reality, any effort to quantify the effects of education upon earnings without

careful attention to the other factors that affect earnings could create serious statistical

difficulties (termed “omitted variables bias”), which I will discuss later. But for now let

us assume away this problem. We also assume, again quite unrealistically

“education” can be measured by a single attribute


and the time period of returns is P, the annualized volatility is

The formula used above to convert returns or volatility measures from o



However, more generally, for natural stochastic processes, the precise relation


the Lévy stability exponent α to extrapolate natural processes:



the effect of a price increase upon demand, for example, or the

effect of changes in the money supply upon the inflation rate. To explore such is



that they influence. The investigator also typically assesses the “statistica


is close to the estimated relationship.

SIMPLE REGRESSION


areful attention to the other factors that affect earnings could create serious statistical


us assume away this problem. We also assume, again quite unrealistically

“education” can be measured by a single attribute—years of schooling. We thus


The formula used above to convert returns or volatility measures from one time



However, more generally, for natural stochastic processes, the precise relationship




the effect of a price increase upon demand, for example, or the

effect of changes in the money supply upon the inflation rate. To explore such issues,



that they influence. The investigator also typically assesses the “statistical significance”



areful attention to the other factors that affect earnings could create serious statistical


us assume away this problem. We also assume, again quite unrealistically, that

years of schooling. We thus

suppress the fact that a given number of years in school may represent widely varying

academic programs.

At the outset of any regression study, one formulates some hypothesis about the

relationship between the variables of interest, here, education and earnings. Common

experience suggests that better educated people tend to make more money. It further

suggests that the causal relation likely runs from education to earnings rather than the

other way around. Thus, the tentative hypothesis is that higher levels of education cause

higher levels of earnings, other things being equal.

To investigate this hypothesis, imagine that we gather data on education and

earnings for various individuals. Let E denote education in years of schooling for each

individual, and let I denote that individual’s earnings in dollars per year. We can plot

this information for all of the individuals in the sample using a two-dimensional

diagram, conventionally termed a “scatter” diagram. Each point in the diagram

represents an individual in the sample.

The diagram indeed suggests that higher values of E tend to yield higher values

of I, but the relationship is not perfect—it seems that knowledge of E does not suffice

for an entirely accurate prediction about I. We can then deduce either that the effect of

education upon earnings differs across individuals, or that factors other than education

influence earnings. Regression analysis ordinarily embraces the latter explanation.

Thus, pending discussion below of omitted variables bias; we now hypothesize those

earnings for each individual are determined by education and by an aggregation of

omitted factors that we term “noise.”

To refine the hypothesis further, it is natural to suppose that people in the labor

force with no education nevertheless make some positive amount of money, and that

education increases earnings above this baseline. We might also suppose that education

affects income in a “linear” fashion—that is, each additional year of schooling adds the

same amount to income. This linearity assumption is common in regression studies but

is by no means essential to the application of the technique, and can be relaxed where

the investigator has reason to suppose a priori that the relationship in question is

nonlinear.

Then, the hypothesized relationship between education and earnings may be

written

I = a + bE + e

Where

a = a constant amount (what one earns with zero education);

b = the effect in dollars of an additional year of schooling on income, hypothesized to

be positive; and

e = the “noise” term reflecting other factors that influence earnings.

The variable I is termed the “dependent” or “endogenous” variable; E is termed

the “independent,” “explanatory,” or “exogenous” variable; a is the “constant term” and

b the “coefficient” of the variable E.

Remember what is observable and what is not. The data set contains

observations for I and E. The noise component e is comprised of factors that are

unobservable, or at least unobserved. The parameters ‘a’ and ‘b’ are also unobservable.

The task of regression analysis is to produce an estimate of these two parameters, based

upon the information contained in the data set and, as shall be seen, upon some

assumptions about the characteristics of e.

To understand how the parameter estimates are generated, note that if we ignore

the noise term e, the equation above for the relationship between I and E is the equation

for a line—a line with an “intercept” of a on the vertical axis and a “slope” of b.

Returning to the scatter diagram, the hypothesized relationship thus implies that

somewhere on the diagram may be found a line with the equation I = a + bE. The task

of estimating a and b is equivalent to the task of estimating where this line is located.

What is the best estimate regarding the location of this line? The answer

depends in part upon what we think about the nature of the noise term e. If we believed

that e was usually a large negative number, for example, we would want to pick a line

lying above most or all of our data points, the logic is that if e is negative, the true value

of I (which we observe), given by I = a + bE + e, will be less than the value of I on the

line I = a + bE. Likewise, if we believed that e was systematically positive, a line lying

below the majority of data points would be appropriate. Regression analysis assumes,

however, that the noise term has no such systematic property, but is on average equal to

zero. Let us make the assumptions about the noise term more precise in a moment. The

assumption that the noise term is usually zero suggests an estimate of the line that lies

roughly in the midst of the data, some observations below and some observations

above.

But there are many such lines, and it remains to pick one line in particular.

Regression analysis does so by embracing a criterion that relates to the estimated noise

term or “error” for each observation. To be precise, define the “estimated error” for

each observation as the vertical distance between the value of I along the estimated line

I = a + bE (generated by plugging the actual value of E into this equation) and the true

value of I for the same observation. Superimposing a candidate line on the scatter

diagram, the estimated errors for each observation may be seen as follows:

With each possible line that might be superimposed upon the data, a different

set of estimated errors will result. Regression analysis then chooses among all possible

lines by selecting the one for which the sum of the squares of the estimated errors is at a

minimum. This is termed the minimum sum of squared errors (minimum SSE)

criterion. The intercept of the line chosen by this criterion provides the estimate of α,

and its slope provides the estimate of β.

It is hardly obvious why we should choose our line using the minimum SSE

criterion. We can readily imagine other criteria that might be utilized (minimizing the

sum of errors in absolute value, for example). One virtue of the SSE criterion is that it

is very easy to employ computationally. When one expresses the sum of squared errors

mathematically and employs calculus techniques to ascertain the values of α and β that

minimize it, one obtains expressions for α and β that are easy to evaluate with a

computer using only the observed values of E and I in the data sample. But

computational convenience is not the only virtue of the minimum SSE criterion; it also

has some attractive statistical properties under plausible assumptions about the noise

term. These properties will be discussed in a moment, after we introduce the concept of

multiple regression.

4.2.2 MULTIPLE REGRESSION

Plainly, earnings are affected by a variety of factors in addition to years of

schooling, factors that were aggregated into the noise term in the simple regression

model above. “Multiple regression” is a technique that allows additional factors to enter

the analysis separately so that the effect of each can be estimated. It is valuable for

quantifying the impact of various simultaneous influences upon a single dependent

variable. Further, because of omitted variables bias with simple regression, multiple

regression is often essential even when the investigator is only interested in the effects

of one of the independent variables.

For purposes of illustration, consider the introduction into the earnings analysis

of a second independent variable called “experience.” Holding constant the level of

education, we would expect someone who has been working for a longer time to earn

more. Let X denote years of experience in the labor force and, as in the case of

education, we will assume that it has a linear effect upon earnings that is stable across

individuals. The modified model may be written:

I = a + bE + gX + e

Where g is expected to be positive.

The task of estimating the parameters a, b, and g is conceptually identical to the

earlier task of estimating only a and b. The difference is that we can no longer think of

regression as choosing a line in a two-dimensional diagram—with two explanatory

variables we need three dimensions, and instead of estimating a line we are estimating a

plane. Multiple regression analysis will select a plane so that the sum of squared

errors—the error here being the vertical distance between the actual value of I and the

estimated plane—is at a minimum. The intercept of that plane with the I-axis (where E

and X are zero) implies the constant term a, it’s slope in the education dimension

implies the coefficient b, and its slope in the experience dimension implies the

coefficient g.

Multiple regression analysis is in fact capable of dealing with an arbitrarily

large number of explanatory variables. Though people lack the capacity to visualize in

more than three dimensions, mathematics does not. With n explanatory variables,

multiple regression analysis will estimate the equation of a “hyperplane” in n-space

such that the sum of squared errors has been minimized. Its intercept implies the

constant term, and its slope in each dimension implies one of the regression

coefficients. As in the case of simple regression, the SSE criterion is quite convenient

computationally. Formulae for the parameters a, b, g . . . can be derived readily and

evaluated easily on a computer, again using only the observed values of the dependent

and independent variables.

The interpretation of the coefficient estimates in a multiple regression warrants

brief comment. In the model I = a + bE + gX + e, a captures what an individual earns

with no education or experience, b captures the effect on income of a year of education,

and g captures the effect on income of a year of experience. To put it slightly

differently, b is an estimate of the effect of a year of education on income, holding

experience constant. Likewise, g is the estimated effect of a year of experience on

income, holding education constant.

4.3 ESSENTIAL ASSUMPTIONS AND STATISTICAL PROPERTIES OF

REGRESSION

As noted, the use of the minimum SSE criterion may be defended on two

grounds: its computational convenience, and its desirable statistical properties. We now

consider these properties and the assumptions that are necessary to ensure them.

Continuing with our illustration, the hypothesis is that earnings in the “real

world” are determined in accordance with the equation I = a + bE + gX + e—true

values of a, b, and g exist, and we desire to ascertain what they are. Because of the

noise term e, however, we can only estimate these parameters.

We can think of the noise term e as a random variable, drawn by nature from

some probability distribution—people obtain an education and accumulate work

experience, then nature generates a random number for each individual, called e, which

increases or decreases income accordingly. Once we think of the noise term as a

random variable, it becomes clear that the estimates of a, b, and g (as distinguished

from their true values) will also be random variables, because the estimates generated

by the SSE criterion will depend upon the particular value of e drawn by nature for

each individual in the data set. Likewise, because there exists a probability distribution

from which each e is drawn, there must also exist a probability distribution from which

each parameter estimate is drawn, the latter distribution a function of the former

distributions. The attractive statistical properties of regression all concern the

relationship between the probability distribution of the parameter estimates and the true

values of those parameters.

We begin with some definitions. The minimum SSE criterion is termed an

estimator. Alternative criteria for generating parameter estimates (such as minimizing

the sum of errors in absolute value) are also estimators.

Each parameter estimate that an estimator produces, as noted, can be viewed as

a random variable drawn from some probability distribution. If the mean of that

probability distribution is equal to the true value of the parameter that we are trying to

estimate, then the estimator is unbiased. In other words, to return to our illustration,

imagine creating a sequence of data sets each containing the same individuals with the

same values of education and experience, differing only in that nature draws a different

ε for each individual for each data set. Imagine further that we re-compute our

parameter estimates for each data set, thus generating a range of estimates for each

parameter α, β and γ. If the estimator is unbiased, we would find that on average we

recovered the true value of each parameter.

An estimator is termed consistent if it takes advantage of additional data to

generate more accurate estimates. More precisely, a consistent estimator yields

estimates that converge on the true value of the underlying parameter as the sample size

gets larger and larger. Thus, the probability distribution of the estimate for any

parameter has lower variance15 as the sample size increases, and in the limit (infinite

sample size) the estimate will equal the true value.

The variance of an estimator for a given sample size is also of interest. In

particular, let us restrict attention to estimators that are unbiased. Then, lower variance

in the probability distribution of the estimator is clearly desirable16—it reduces the

probability of an estimate that differs greatly from the true value of the underlying

parameter. In comparing different unbiased estimators, the one with the lowest variance

is termed efficient or best.

Under certain assumptions, the minimum SSE criterion has the characteristics

of unbiasedness, consistency, and efficiency; these assumptions and their consequences

follow:

(1) If the noise term for each observation, e, is drawn from a distribution

that has a mean of zero, then the sum of squared errors criterion generates estimates

that are unbiased and consistent.

That is, we can imagine that for each observation in the sample, nature draws a

noise term from a different probability distribution. As long as each of these

distributions has a mean of zero (even if the distributions are not the same), the

minimum SSE criterion is unbiased and consistent.17 This assumption is logically

sufficient to ensure that one other condition holds—namely, that each of the

explanatory variables in the model is uncorrelated with the expected value of the noise

term.18 This will prove important later.

(2) If the distributions from which the noise terms are drawn for each

observation have the same variance, and the noise terms are statistically independent of

each other (so that if there is a positive noise term for one observation, for example,

there is no reason to expect a positive or negative noise term for any other observation),

then the sum of squared errors criterion gives us the best or most efficient estimates

available from any linear estimator (defined as an estimator that computes the

parameter estimates as a linear function of the noise term, which the SSE criterion

does).

If assumptions (2) are violated, the SSE criterion remains unbiased and

consistent but it is possible to reduce the variance of the estimator by taking account of

what we know about the noise term. For example, if we know that the variance of the

distribution from which the noise term is drawn is bigger for certain observations, then

the size of the noise term for those observations is likely to be larger. And, because the

noise is larger, we will want to give those observations less weight in our analysis.

Analysis

And

Interpretation

In this paper, the volatility of gold futures market, the spot market and the stock

market (SENSEX) is being calculated. The price quote of MCX Gold Futures Contracts

and price quote of Mumbai spot market is considered for this purpose. For the stock

market the price quote of the BSE SENSEX is considered on a monthly basis. For

finding out the relationship between the stock market and the gold market, regression

models are analyzed. The SPSS statistical package is used for this purpose. The

relationship between the volatility of SENSEX and the gold spot market and the gold

futures market is taken into consideration for this paper.

5.1 VOLATILITY

The figure below shows the volatility of gold spot (Mumbai) and Futures

(MCX) market.

Figure – 5.1 Comparison between Spot and Future Market Volatility of Gold in Indian

Context

From the above figure, it is quite clear that the volatility of spot and future

market moved almost in the same pattern over the years. This confirms the strong

relation between the spot and futures market of gold. It can also be analyzed that the

spot market is more volatile than the futures market in Indian context. The last two

years has shown a more volatile movement. This may be because of the recessionary

pressure of the global financial crisis. Similarly if the spot and futures market price is

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compared, it can be seen that, they also moved on the same direction and more

fluctuations are seen in spot market rather than the futures market.

Figure – 5.2 Comparison between Prices of Spot and Futures Market of Gold in Indian

Context

The above figure shows the movement of prices of both the spot and the futures

market of gold in India. It is quite clear from the figure that both the market has strong

correlation between them. Over last five years the spot price and the futures price has

shown a constant and steady growth, which even continued in the recessionary period

also.

Figure – 5.3 Comparison between Volatility of SENSEX, Spot and Futures Market of Gold

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VOLATILITY OF GOLD FUTURE MARKET

The above figure shows a comparative analysis of the volatility of SENSEX

(BSE’s Index), with that of the volatility of gold spot and futures market. From the

figure it is confirmed that the stock market is much more volatile than the gold market

in India. After June 2007, the SENSEX is very much volatile, whereas the gold spot

and futures market is quite relatively less volatile. For this reason, investor shift to gold

market, when the stock market is very volatile.

Figure – 5.4 Comparison of Volatility of SENSEX and Gold Spot Market

The volatility of the spot market of gold is quite less and it showed a steady

growth over the years. This may be due to supply and demand side effect on the gold

market. Also the international gold market has a significant impact on the gold market

of India, as India imports most of its gold from other countries. During 2008, the gold

market showed a relatively higher volatility, which was one of the impacts of the global

financial meltdown. On the other hand the volatility of stock market segment is much

higher in comparison to the gold market. Specifically, in 2008-09, the SENSEX

showed a higher range of variability in the volatility level. This is the consequence of

the financial crisis and the erosion of confidence of the investors from the market.

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Similarly, when the gold futures market and the stock market is analyzed that,

the volatility of the stock market is much more that the gold futures market. The

volatility of the gold futures market moved in a relatively slow and steady pattern,

where the SENSEX exhibit a higher volatility. The reason for the volatility of the stock

market is the erosion of the confidence between the investors is one of the reasons,

which was a by-product of the financial crisis.

Figure – 5.5 Volatility of Gold Futures and SENSEX

5.2 REGRESSION ANALYSIS

Table – 5.1 Regression Model between Gold Spot And Volatility of SENSEX

Model Summary

Model R

R

Square

Adjusted

R Square

Std. Error of

the Estimate

1 .222a .049 .032 541.42745

a. Predictors: (Constant), Gold Spot Market Price

b. Dependent Variable: Volatility of SENSEX

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The above table shows the linear regression model done with the gold spot price

as the independent variable and the volatility of the SENSEX as the dependent variable.

R2, which shows the degree of Association, is 0.049 and positive. Thus the degree of

association between the spot gold price and the volatility of SENSEX is positive and

both the markets are strongly correlated.

Table – 5.2 R2 between Volatility of S ENSEX and Gold Spot over the Years

Year R2

2005-06 .119

2006-07 .270

2007-08 .095

2008-09 .071

2009-10 .089

The value of R2 implies that the in year 2005 - 06, the total variation of

SENSEX nearly 11.9% is explained by the variation in gold spot price, which was 27%

in 2006 - 07 and then decreased to 9.5% in 2007-08, 7.1% in 2008-09 and 8.9% in

2009-10. Thus, over the years it is following a very random trend.

Table – 5.3 Regression between Gold Futures and Volatility of SENSEX

Model Summary

Model R

R

Square

Adjust

ed R Square

Std. Error of

the Estimate

1 .343a .11

7

.102 521.66125

Model Summary

Model R

R

Square

Adjust

ed R Square

Std. Error of

the Estimate

1 .343a .11

7

.102 521.66125

a. Predictors: (Constant), Gold Futures Market

b. Dependent Variable: Volatility of SENSEX

The above table shows the linear regression model done with the gold futures

price as the independent variable and the volatility of the SENSEX as the dependent

variable. The co-efficient of regression, R2, which shows the degree of Association, is

0.117 and positive. Thus, the degree of association between the gold futures price and

the volatility of SENSEX is positive and both the markets are strongly correlated.

Table – 5.4 R2 between Gold Futures and SENSEX

Year R2

2005-06 .063

2006-07 .015

2007-08 .027

2008-09 .111

2009-10 .057


SENSEX nearly 6.3% is explained by the variation in gold futures price, which was

1.5% in 2006 - 07 and 2.7% in 2007-08, 11.1% in 2008-09 and 5.7% in 2009-10. Thus,

over the years it is following a very random trend.

Conclusion

6.1 MAJOR FINDINGS

On course of the study of the relationship between the gold market and the

stock market, the following observations are pointed out. Since the study is focused on

the equity market of the stock market only, the findings are concerned about the same.

6.1.1 VOLATILITY

The monthly volatility of the stock market is relatively very high in Indian

context. The SENSEX (BSE’s SENSetive indEX), showed higher volatility than the

gold market. Through it showed a normal volatility from April 2005 to June 2007, but

after July 2007 upto July 2009, it showed a very volatile movement. The price

fluctuation of SENSEX is very high. This may be due to the recessionary pressure at

that time. The lack of investor confidence and also Satyam scams contributed largely to

this volatility. The large participation of FIIs and lack of proper mutual funds industry

are yet some of the causes of stock market volatility. It can be easily marketed that

volatility of SENSEX was very high in the months of August 2007, January 2008,

April 2008, July 2008, October 2008 and April 2009.

If the volatility of both the gold spot market and the futures market is analyzed,

then it is found that it is relatively low than the stock market volatility. The volatility of

the gold spot market and the gold futures market, when compared showed almost the

same pattern of volatility. The spot market is relatively more volatile than the futures

market in Indian context. This may be due to non standardization of the gold spot

market. Since the futures are standardized and traded in recognized stock exchanges,

which gives guarantee on it, it exhibits a lesser volatility than the spot market. But this

difference of volatility is not significantly large.

When the volatility of the gold market is compared with the stock market of

India, it is observed that, both the markets are more volatile over the years. The

volatility of gold market is due to the variability of the demand and supply in gold. As

India produces only 0.5% of its total gold consumption, and depends largely on import,

the international market has a significant impact on the gold market of India. In 2009,

India imported more than Rs. 881 bn gold. The gold market has shown a higher

volatility in 2008-09, this is because of the global meltdown and recessionary pressure

at that time. Similar is the case, when a comparison is carried out between the gold

futures market and the stock market of India. It has also exhibited a lower volatility

than the stock market. The increase in volatility of gold futures market is just a

replication of the increase in the volatility of the gold spot market.

Since the gold market exhibit a lower volatility than the stock market, investors

prefer to shift to the gold market when the stock market is very much volatile. The

investors in India consider gold as the second best alternative of investment after bank

deposits because of lower risk involvement in gold market. This is also one of the

major reasons for lower volatility of gold than the stock market.

6.1.2 GOLD MARKET OF INDIA

� The total demand for gold in India is increasing. It was around Rs. 160 bn in

1992, whereas, in 2008, it was more than Rs. 1000 bn. It can also be marked that there

was strip rise in the overall demand of gold after 2004.

� India’s gold industry is world’s biggest market for gold, with imports meeting

almost all the requirement of the country. India owns over 18,000 tonnes of gold stock,

which worth approximately $800 billion and representing at least 11 percentage of

global gold stocks. This is because gold has a cultural significance in India. Gold is

needed in each and every occasion.

� Gold jewellery accounted for around 75% of total Indian gold demand in 2009,

the remaining 23% is in investment and 2% in decorative and industrial. The jewellery

consumption of India also showed an increasing trend. It increased from Rs. 100 bn in

1992 to Rs. 750 bn in 2008. In longer term, India’s favourable demographic and age

profile are likely to ensure buoyant consumption growth.

� In Indian context, gold is viewed as a secure, liquid investment, a capital and

value preserver and is second preferred investment alternative after bank deposits. This

is due to the risk averseness of the investors in India. Saving rates estimated at around

30% of the total income of which gold accounts around 10%. It can also be marked that

the investment in gold is increasing by day-by-day.

� One of the major innovations in the Indian stock market is gold ETFs. Since its

inception, it showed a regular growth. Many financial institutions are offering gold

ETFs. Gold ETFs are the benchmark performer in Indian capital market. It accounted

around 11 tonnes by the end of August 2010, which 250% times more on June 2007.

� Since 1992, approximately 22 tonnes of gold per annum have been used in

domestic decorative and industrial applications. This sector accounted nearly 2% of the

Indian gold demand in 2009.

� India produces only 0.5% of the total annual gold consumption, for rest, it

depends on import, thus the gold imports has increased significantly from Rs. 88 bn in

1992 to Rs 881 bn by the end of 2009.

� Recently Reserve Bank of India (RBI) purchased 200 tonnes of gold from IMF.

This boosted the gold reserve of India. Currently India is in 11th position in the globe in

gold reserves.

� India is recycling around 92 tonnes of gold every-year. In 2009, the supply of

domestic recycled gold rose by 29% to 116 tonnes.

� There exist a strong correlation between the spot and future market of gold. The

volatility of both the market moved in the same direction and the spot market is more

volatile than the futures market. Similarly, the prices in both the market moved in the

same direction. The futures market is nothing but a replication of the spot market. Both

the markets are very closely related, thus both the markets move in the same direction

in all respect.

� The volatility of gold is relatively low than the stock market indices. This may

be the reason; people invest in gold, when the stock market is very volatile.

6.1.3 REGRESSION

The regression analysis between the SENSEX and the gold spot market taking

SENSEX as the dependent variable and the gold spot price as the independent variable,

it is found that the degree of association between the SENSEX and the gold spot market

is found to be 0.049, which is positive. Thus, there exists a strong correlation between

both the markets. Hence any change in the SENSEX will have an impact equal to 4.9%

on the gold spot market.

The regression analysis between the SENSEX and the gold spot price over that

last five years, if analyzed found to be 11.9% in 2005-06, 27% in 2006-07, 9.5% in

2007-08, 7.1% in 2008-09 and 8.9% in 2009-10. This shows a random movement of

R2, which shows the degree of association between the gold spot market and the equity

market of India. R2 was high in 2005-06 and 2009-10, this is mainly due to the

favourable market conditions and various policies undertaken by the government of

India. Both the markets are bullion and were touching new heights at that time. The

economic condition prevailing at that point of time favoured the market to grow at a

rapid rate.

But from 2008, the stock market has seen many ups and downs, such as a peak

of 21000 and a lower point of 8000 within few hours. This is mainly due to the global

financial crisis that affected almost all the countries of the world. But the gold market

was relatively less affected by this crisis. This may be due to the consumption pattern

of gold and the less volatility of gold market. This is reason for which the R2 between

the gold spot market and the equity market reduced to 9.5% in 2007-08, 7.1% in 2008-

09 and 8.9% in 2009-10. So it can be said that gold market investment is not the only

factor that affects the volatility of the equity market. It is because of the bullion nature

or less riskiness of gold; people shift to or prefer to invest in the gold market, when the

equity market is very unstable and volatile.

Similarly, R2 between the volatility of SENSEX and the gold futures prices is

found to be positive, which is about .117 or 11.7%. Thus any change in volatility of the

SENSEX has an impact equal to 11.7% on the gold futures prices. The gold futures

market is more correlated to the stock market, because, it is an organized market and

the exchange provides guarantee on each contract. This encourages more trading in

gold futures and more over the hedging nature of gold futures make it a frequently

trading contract in Indian commodity market. Since both the markets; equity market

and the gold futures market are organized markets, there exists more correlation

between them.


SENSEX nearly 6.3% is explained by the variation in gold futures price, which was

1.5% in 2006 - 07 and 2.7% in 2007-08, 11.1% in 2008-09 and 5.7% in 2009-10. Thus,

over the years it is following a very random trend. This may be due to the instability of

the equity market of India. It can also be seen that the degree of association is highest in

2008-09, when the equity market is very volatile due to global financial meltdown.

6.2 RECOMMENDATIONS

Based on the analysis of the stock market and the gold market of India, it is

observed that both the markets are strongly correlated to each other. The gold spot

market and the gold futures market have a significant impact on the volatility of stock

market and vice – versa. The stock market exhibits more volatility than the gold market

in India. Based on the study, the following are the recommendations;

o The volatility of the stock market should be checked by the government and the

concerned regulatory body. The reasons of volatility should be found out and

appropriate should be taken, which will help in reducing the volatility.

o The activities of the FII should be check properly. Though the Securities and

Exchange Board of India is regulating the activities of FII, still there is requirement of

more stringent regulations for FII.

o The mutual fund industry should be encouraged. One of the major reasons for

the slow growth of the mutual funds in India is lack of awareness among the investors.

Awareness programs should be conducted by the regulatory body, the stock exchanges

and the brokers to educate the investors. Though the SEBI and the stock exchanges are

carrying out some of the awareness campaigns, it is not sufficient and more such

activities should be undertaken. The stock brokers should also take initiatives in this

regard.

o Innovation in gold market like gold ETFs, derivatives in gold market should be

encouraged. This will encourage investment in the market and ultimately the volatility

will be low.

o Till date only 2 percent of the total population in India invests in the stock

market. More participation should be encouraged. This will help in better resource

mobilization and also help in stabilizing the financial market which leads to less

volatility.

6.3 CONCLUSIONS

After analyzing the nature of gold market and the stock market of India, it is

found that there exists a strong and positive correlation between both the markets,

especially the volatility of the stock market and the gold prices are strongly correlated.

Any change in the volatility of the shock market has a significant impact on the

investment pattern in the gold market. It is generally found that, when the stock market

is very much volatile and imparts large fluctuation, investors prefer the gold market for

investment.

In India, gold is considered as the second best alternative of investment. It is

considered as most secured investment avenue after the bank deposits. This may be the

reason, investors shift to gold market at large rather than any other market, when the

stock market exhibits a large volatility.

Although the investment pattern in gold market is significantly influenced by

the stock market volatility, but it cannot be said that, this is the only factor that affects

the gold market. There are a number of factors that influence the market. But when

there is a higher fluctuation in the stock market and the market is quite unstable, the

investment in gold market increases. This is because, gold is considered as a safer

investment avenue than the stock market and it also provides a relative return to the

investors.

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� Securities and Exchange Board of India, Annual Report, 2009, SEBI.

� Securities and Exchange Board of India, Handbook on Statistics, 2009, SEBI.

� Reserve Bank of India, Handbook on Statistics, 2009, RBI.

WEBSITES

� http://www.sebi.gov.in/marketdata

� http://www.rbi.gov.in/database

� http://www.bseindia.com/marketdata

� http://www.mcxindia.com

� http://www.moneycontrol.com

� http://www.investorworld.com

� http://www.yahoo.com/finance

� http://www.en.wikipedia.org/wiki/volatility

� http://www.en.wikipedia.org/wiki/regression analysis

ANNEXTURE

Table – 1 Prices of SENSEX, Gold Spot and Gold Futures

TIME SENSEX GOLD SPOT MARKET

(MUMBAI) GOL DFUTURES MARKET

(MCX) Apr-05 6154.44 6151 6229 May-05 6715.11 6036 5966 Jun-05 7193.85 6142 6214 Jul-05 7635.42 6064 6084

Aug-05 7805.43 6248 6263 Sep-05 8634.48 6529 6731 Oct-05 7892.32 6878 6822 Nov-05 8788.81 7141 7416 Dec-05 9397.93 7585 7638 Jan-06 9919.89 7922 8166 Feb-06 10370.24 8030 8111 Mar-06 11279.96 8063 8382 Apr-06 12042.56 8963 9609 May-06 10398.61 9928 9526 Jun-06 10609.09 8958 9220 Jul-06 10743.88 9570 9560


Aug-07 15318.6 8828 8935 Sep-07 17291.1 9322 9530 Oct-07 19837.99 9695 10083 Nov-07 19363.19 10358 10033 Dec-07 20286.99 10293 10598 Jan-08 17648.71 11284 11707

Feb-08 17578.72 11889 12396 Mar-08 15644.44 12636 11920 Apr-08 17287.31 11834 11370 May-08 16415.57 12139 12199 Jun-08 13461.6 12356 12879 Jul-08 14355.75 13028 12618


Aug-09 15666.64 14962 15125 Sep-09 17126.84 15726 15703 Oct-09 15896.28 15859 15957 Nov-09 16926.22 17137 17614 Dec-09 17464.81 17147 16686 Jan-10 16357.96 16704 16200 Feb-10 16429.55 16531 16789 Mar-10 17527.77 16564 16295

Table – 2 Monthly Volatility of SENSEX, Gold Spot and Gold Futures

TIME VOLATILITY OF

SENSEX

VOLATILITY OF GOLD SPOT

MARKET

VOLATILITY OF GOLD FUTURE

MARKET May-05 396.45 81.32 185.97 Jun-05 338.52 74.95 175.36 Jul-05 312.24 55.15 91.92

Aug-05 120.22 130.11 126.57 Sep-05 586.23 198.70 330.93 Oct-05 524.79 246.78 64.35 Nov-05 633.91 185.97 420.02 Dec-05 430.71 313.96 156.98

Jan-06 369.08 238.29 373.35 Feb-06 318.45 76.37 38.89 Mar-06 643.27 23.33 191.63 Apr-06 539.24 636.40 867.62 May-06 1162.45 682.36 58.69 Jun-06 148.83 685.89 216.37 Jul-06 95.31 432.75 240.42

Aug-06 675.41 21.92 4.24 Sep-06 534.13 369.82 491.44 Oct-06 358.84 224.15 24.75 Nov-06 519.31 312.54 265.17 Dec-06 64.06 9.90 2.83 Jan-07 214.97 38.89 16.26 Feb-07 815.17 331.63 272.94 Mar-07 94.76 123.74 204.35 Apr-07 565.88 24.75 96.17 May-07 475.24 316.78 359.92 Jun-07 74.99 124.45 22.63 Jul-07 636.74 29.70 31.11

Aug-07 164.32 55.86 161.93 Sep-07 1394.77 349.31 420.73 Oct-07 1800.92 263.75 391.03 Nov-07 335.73 468.81 35.36 Dec-07 653.23 45.96 399.52 Jan-08 1865.55 700.74 784.18 Feb-08 49.49 427.80 487.20 Mar-08 1367.74 528.21 336.58 Apr-08 1161.68 567.10 388.91 May-08 616.41 215.67 586.19 Jun-08 2088.77 153.44 480.83 Jul-08 632.26 475.18 184.55

Aug-08 147.63 823.78 511.24 Sep-08 1204.98 253.14 917.12 Oct-08 2172.49 379.01 1104.50 Nov-08 491.68 422.85 1057.12 Dec-08 392.15 527.50 357.09 Jan-09 157.73 415.07 581.24 Feb-09 376.63 910.05 743.88 Mar-09 577.63 328.80 263.04 Apr-09 1198.37 542.35 444.77 May-09 2278.30 89.10 296.98

Jun-09 92.92 25.46 333.75 Jul-09 831.89 58.69 248.19

Aug-09 2.60 169.71 228.40 Sep-09 1032.52 540.23 408.71 Oct-09 870.14 94.05 179.61 Nov-09 728.28 903.68 1171.68 Dec-09 380.84 7.07 656.20 Jan-10 782.66 313.25 343.65 Feb-10 50.62 122.33 416.49 Mar-10 776.56 23.33 349.31

Table – 3 R2 between Volatility of SENSEX and Gold Spot 2005-06

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .344a .119 .021 158.23304


Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .520a .270 .197 303.79175


Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .266a .071 -.022 696.11373

Table -6 R2 between Volatility of SENSEX and Gold Spot 2008-09

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .266a .071 -.022 696.11373


Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .298a .089 -.002 622.72121

Table – 8 R2 between Volatility of SENSEX and Gold Futures 2005-06

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .396a .157 .063 154.78082

Table – 9

R2 between Volatility of SENSEX and Gold Futures 2006-07 Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .323a .104 .015 336.51069

Table – 10

R2 between Volatility of SENSEX and Gold Futures 2007-08 Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .340a .115 .027 646.65366

Table – 11 R2 between Volatility of SENSEX and Gold Futures 2008-09

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .437a .191 .111 649.36638

Table – 12

R2 between Volatility of SENSEX and Gold Futures 2009-10

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .239a .057 -.037 633.38549

An Empirical Analysis of Relationship Between Gold Market and Stock Market of India

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