+price integration in the indian stock market

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Price Integration in the Indian Stock Market: BSE Sensexand S&P CNX Nifty

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Price Integration in the Indian Stock Market: BSE Sensex and S&P CNX Nifty

M.P.Birla Institute Of Management, Bangalore.2

DECLARATION

I hereby declare that the dissertation entitled Price Integration in the IndianStock Market: BSE Sensex and S&P CNX Niftyis theresultofworkundertaken by me, under the guidance and supervision of Dr.T.V.N.Rao, Associate

Professor, M.P.Birla Institute of Management,Bangalore.

Ialsodeclare that thisdissertationhas notbeen submitted

to any other University/Institution for the award of any Degree

or Diploma.

Place:Bangalore

Date: 16th

June2006 Chiranjeevi Samudrala


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Principal Certificate.

I hereby certify that the research work embodied in

this dissertation entitled Price Integration in the IndianStock Market: BSE Sensex and S&P CNX Niftyhas been

undertaken and completed by Mr.Chiranjeevi Samudrala underthe guidance and supervision ofDr.T.V.N.Rao, Faculty, MPBIM,

Bangalore.

Place:Bangalore Dr.N.S. Malavalli

Date:16/06/2006 PrincipalMPBIM, Bangalore


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Guide Certificate.

I hereby declare that the research work embodied in

this dissertation entitled Price Integration in the IndianStock Market: BSE Sensex and S&P CNX Niftyhas been

undertaken and completed by Mr.Chiranjeevi Samudrala undermy guidance and supervision.

Place:Bangalore (Dr.T.V.N.Rao)

Date: 16/06/2006 Faculty Member.


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ACKNOWLEDGEMENT

I take this opportunity to sincerely thank Dr.T.V.N Rao

who guided me through out the project through his

valuable suggestions, without which the project would

not have been successful.

I also thank Dr N.S. Malavalli (Principal) for giving me

the opportunity to explore my areas of interest by consistently

lending support in terms of his expertise and also supplying valuable

inputs in terms of resources every step of the way.

My sincere thanks to my parents and friends who out of

hard sweat were able to help me at all time and given

encouragement for successful completion of this project.

Chiranjeevi Samudrala

(04XQCM6020)


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Serial no CONTENTS Page no

Executive Summary

Introduction 1

a).National Stock Exchange 1

b).Bombay Stock Exchange 3

Chapter 1

c). The structure of Indian Capital market and need for the study 4

Chapter 2 Review of Literature 7

Methodology 14

3.1 Research Problem 14

3.2 Objectives of the Study 14

3.3 Hypothesis of the Study 14

3.4 Limitations of the Study 14

3.4 Study Type 15

3.5 Study Population 15

3.6 Sample 15

3.7 Sampling Technique 15

3.8 Period of Study 15

Chapter 3

3.9 Data Gathering 15


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3.10 Statistical Models 16

Data Analysis and Interpretations

Table 1: Unit Root Test for BSE Daily and Interpretation 21

Table 2: Unit Root Test for BSE Monthly and Interpretation 22

Table 3: Unit Root Test for NSE Daily and Interpretation 23

Table 4: Unit Root Test for NSE Daily and Interpretation 24

Table 5: Grangers Cointegration Test for BSE and NSE

Daily and Interpretation for X on Y

25

Table 6: ANOVA for BSE and NSE Daily and Interpretation for

X on Y

26


Daily and Interpretation for Y on X

27

Table 8: ANOVA for BSE and NSE Daily and Interpretation for

Y on X

28


Monthly and Interpretation for X on Y

29

Table 10: ANOVA for BSE and NSE Monthly and

Interpretation for X on Y

30


Monthly and Interpretation for Y on X

31

Chapter 4

Table 12: ANOVA for BSE and NSE Monthly and

Interpretation for Y on X

32

Chapter 5 Conclusions 33

Chapter 6 Annexure 34

Chapter 7 Bibliography


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EXECUTIVE SUMMARY

There are various studies, which have analyzed the co-moments and cointegration

between various stock exchanges of different countries. We are not sure whether the

fluctuations of one index will affect the other and whether they are cointegrated or not.

The objective of the study is to examine the price integraton between two stock price

indices BSE sensex and S&P CNX Nifty in India from January 2000 to January 2006.

The performance and development of both the stock exchanges over the period has been

revealed every year.

In this we first tested whether the time series is stationary or not. For this AugmentedDickey fuller unit root test at different lags such as lag 0 and at lag 12. After examining

the series is stationary or not Engel-Granger test is conducted to test whether the Bombay

Stock Exchange and National Stock Exchange at different lags and found that there is

sufficient evidence for price integration between both the stocks.

The findings of the study are for daily and monthly closing BSE and Nifty are

cointegrated at different lags.


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INTRODUCTION:-

Integration is a process by which markets become open and unified so that, participants

in one market have an unimpeded access to other markets. The financial market's

integration in general implies that in the absence of administrative and informational

barriers, risk adjusted returns on assets of the same tenor in each segment of the market

should be comparable to one another. The empirical investigations of market integration

between two markets could be examined through various statistical and econometrics

techniques. Correlation analysis is a suitable technique, which has been widely and

extensively used in past for examining market integration. The prices of various assets in

these markets were the principal instrument and barometer. If the estimated correlation

coefficient is close to plus or minus one, then the prices between the markets was

considered as the supporting evidence for market integration. However, correlation

analysis has its own limitations. It suffers from the important limitation of possible serial

correlation, thus provides spurious or not meaningful results. In order to analyze the co-

movements of international stock prices and thereby stock market integration, most of the

studies applied cointegration techniques. The study applies the Engel-Engel-Granger

cointegration technique which is a popular and suitable technique for two variables.

NATIONAL STOCK EXCHANGE (NSE)

With the liberalization of the Indian economy, it was found inevitable to lift the Indian

stock market trading system on par with the international standards. On the basis of the

recommendations of high powered Pherwani Committee, the National Stock Exchange

was incorporated in 1992 by Industrial Development Bank of India, Industrial Credit and

Investment Corporation of India, Industrial Finance Corporation of India, all Insurance

Corporations, selected commercial banks and others.

Trading at NSE can be classified under two broad categories:

(a) Wholesale debt market and

(b) Capital market.


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Wholesale debt market operations are similar to money market operations - institutions

and corporate bodies enter into high value transactions in financial instruments such as

government securities, treasury bills, public sector unit bonds, commercial paper,

certificate of deposit, etc.

There are two kinds of players in NSE:

(a) Trading members and

(b) Participants.

Recognized members of NSE are called trading members who trade on behalf of

themselves and their clients. Participants include trading members and large players like

banks who take direct settlement responsibility.

Trading at NSE takes place through a fully automated screen-based trading mechanism

which adopts the principle of an order-driven market. Trading members can stay at their

offices and execute the trading, since they are linked through a communication network.

The prices at which the buyer and seller are willing to transact will appear on the screen.

When the prices match the transaction will be completed and a confirmation slip will be

printed at the office of the trading member.

NSE has several advantages over the traditional trading exchanges. They are as follows:

NSE brings an integrated stock market trading network across the nation.

Investors can trade at the same price from anywhere in the country since inter-market

operations are streamlined coupled with the countrywide access to the securities.

Delays in communication, late payments and the malpractices prevailing in the

traditional trading mechanism can be done away with greater operational efficiency and

informational transparency in the stock market operations, with the support of total

computerized network.


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BOMBAY STOCK EXCHANGE:

Bombay Stock Exchange Limited is the oldest stock exchange in Asia with a rich

heritage. Popularly known as "BSE", it was established as "The Native Share & Stock

Brokers Association" in 1875. It is the first stock exchange in the country to obtain

permanent recognition in 1956 from the Government of India under the Securities

Contracts (Regulation) Act, 1956.The Exchange's pivotal and pre-eminent role in the

development of the Indian capital market is widely recognized and its index, SENSEX, is

tracked worldwide. Earlier an Association of Persons (AOP), the Exchange is now a

demutualised and corporatised entity incorporated under the provisions of the Companies

Act, 1956, pursuant to the BSE (Corporatisation and Demutualisation) Scheme, 2005

notified by the Securities and Exchange Board of India (SEBI).

With demutualisation, the trading rights and ownership rights have been de-linked

effectively addressing concerns regarding perceived and real conflicts of interest. The

Exchange is professionally managed under the overall direction of the Board of

Directors. The Board comprises eminent professionals, representatives of Trading

Members and the Managing Director of the Exchange. The Board is inclusive and is

designed to benefit from the participation of market intermediaries.

The Exchange has a nation-wide reach with a presence in 417 cities and towns of India.

The systems and processes of the Exchange are designed to safeguard market integrity

and enhance transparency in operations. During the year 2004-2005, the trading volumes

on the Exchange showed robust growth.

The Exchange provides an efficient and transparent market for trading in equity, debt

instruments and derivatives. The BSE's On Line Trading System (BOLT) is a proprietory

system of the Exchange and is BS 7799-2-2002 certified.


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The Structure of Indian Capital Market and need for the study

The Indian financial system has undergone a sea change over the years especially after

nineties. Until the early nineties, it was characterized by regulated and administered

regime. The interest rates were administered, various markets participantsincluding

banks, financial institutions and corporate were restricted in terms of the nature and

volume of transactions they could undertake in various financial markets including the

money, for-ex and capital markets. The administrative limits were also imposed on the

transactions between residents and non-residents. However, a drastic change occurred

since mid-1991, when RBI has taken several steps to develop various segments of the

financial markets, strengthen their integration and enhance their efficiency, covering

various markets including stock market Financial system has become market oriented

rather than strictly controlled.

The structure of Indian capital market, consisting of primary and secondary market, has

evolved over time. The raising of resources in the primary market was subject to several

controls and restrictions until the onset of economic reforms of early nineties. The pricing

to be determined by market forces was disallowed. The secondary market transactions

were impenetrable. The trading and settlements system was traditional, old and almostoutdated. Informational flows to the markets participants were also inefficient. In spite of

these the volume of trading has marked substantial increase. An important development

in this respect is the statutory power given to Securities and Exchange Board of India

(SEBI) to undertake regulatory and supervision of capital market. Since then the

developmental process of capital market began. The introduction of SEBI guidelines is an

important hallmark to protect investors interest and promoting development of primary

market. One example of the developmental process is the introduction of book building

mechanism, which provides issuer the choice to raise resources either through IPOs

(Initial public Offerings) or fixed price mechanism. Both NSE and BSE offer their

infrastructure for conducting online IPOs through such book building. Online trading

system and Over The Counter (OTC) were also introduced to make the trading system


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easy and transparent. This could provide a sound background to establish the strong

relationship between the stock markets.

The capital market has also widen and deepened considerably in the recent years with the

enlargement of participants and emergence of new instruments in both markets. Besides

equity and debt instruments various derivatives instruments were allowed for trading. The

mutual funds and foreign institutional investors were also allowed to participate in the

stock markets. The trading, clearing and settlement systems have also been considerably

improved. The introduction of rolling settlement system shortens the settlement cycle.

These changing capital structures have significant positive impact on volatility, liquidity

and transaction costs over the years. Though the stock market continues to be volatile, the

volatility tended to be declined in recent years. The growth of liquidity measured by

traded value ratio and turnover ratio suggest that liquidity has increased in recent years.

However, despite the growing popularity of stock markets during 80s and 90s the

transaction costs are high maybe due to physical moment of paper, bad deliveries etc.

The reforms in Indian stock market started since mid-90s. As a part of the reform process

the two major stock exchanges viz., Bombay Stock Exchange (BSE). And National Stock

Exchange (NSE) has expanded their operations in different locations. Thus, they provide

investors across the country with the facility to trade in the stock listed/permitted in these

stock exchanges. Though various stock exchanges continued to follow different system of

settlement procedure, certain developments have resulted in better performance in the

various segments of the Indian securities markets. The Interconnected Stock Exchange of

India Ltd. (ICSI) has been set up as an interconnected market system. It provides its

trading members the facility to trade on the national market in addition to the trading

facility at the regional stock exchange. This has integrated various regional stock

exchanges although trading activity in the ICSI has not been very significant. Many

regional stock exchanges have also become members of the BSE and NSE, which further

strengthen the integration process of various stock exchanges in the country. With the

development of various trading techniques, the information from one market to the other

passes quickly, so also the stock price, returns and volatility.


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The performance and development of both the stock exchanges over the period can be

revealed from the data itself, which could also provide some useful information about

their relationship. The total market capitalization, daily turnover and total turnover at

NSE in November 1994 was Rs. 292637 cr, Rs. 7 cr and Rs. 125 cr respectively, which

has increased to Rs. 1585585 cr, Rs. 5139 cr and Rs. 113055 cr in March 2005. Where as

at BSE it was Rs. 401692 cr, Rs. 442 cr and Rs. 8831 cr during November 1994, and has

increased to Rs. 1698428, Rs. 2706, and Rs. 59512 cr in March 2005 respectively. The

number of listed companies in January 1996 and March 2005 at NSE was 406 and 970,

whereas at BSE it was 5451 and 4731(declines since June 2004) respectively. The total

returns of S&P CNX Nifty during 1994-95 was 15.88% and has increased to 14.89%, in

2004-05, whereas for BSE Sensex it was 13.71% and 16.14% respectively. On the other

hand the volatility of S&P CNX Nifty during 1994-95 and 2004-2005 was 1.13% and

1.61%, whereas for BSE Sensex it was 1.17% and 1.48% respectively. The average

number of sharers traded daily at NSE has increased from 43031681 in April 1996 to

380337290 in March 2005. Similarly, average number of sharers traded daily at BSE has

increased from 35162357 in April 1996 to 275609732 in March 2005. If we look at the

average value of trading daily, it shows increasing pattern both at NSE and BSE. At NSE

it has increased from Rs. 864 cr in April 1996 to Rs. 5137 cr in March 2005, similarly, at

BSE this is Rs. 368 and Rs. 2642 cr. It shows that both markets are simultaneously

developing and hence, follow some equilibrium relationship. In these contexts it is felt

necessary to examine the price integration between the two stock markets and see

whether they are related or not.


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LITERATURE REVIEW:

The purpose of literature review is to find out the various studies that have been

done in the relative fields of the present study and also to understand the various

methodologies followed by the authors to arrive at the conclusions.

The following are some of the relevant studies

According to Mahesh Kumar Tambi attempts to examine the financial integration of

emerging or newly industrialized countries and developed economies and test it

empirically. Stock market data have been used for the purpose of the study. Paper

considers three developed countries: USA, CANADA & UK and three developing

nations: India , Malaysia , Singapore.

The paper mainly discuss the degree of integration of Indian Stock market with other

developing and developed natins stock markets using various techniques like Engle

Engel-Granger two stage method, Johansen cointegration method, VAR-ECM , principle

Component Analysis and Impulse-response analysis.

Fianancial market integration can be understood as a situation where there are no

quantitative and qualitative barriers like tariffs,taxes,restriction on trading in foreignassets or information costs with hampers the free flow of capital from one market to

another. Fianancial market integration is a buzzword now a day. Fianancial markets can

be considered integrated if there is no barier on free capital mobility and same risk assets

command the same return across the different markets.

The paper uses the daily sock index data for a period of 11 years for three developing and

three developed countries US, Canada, UK, India, Singapore and Malaysia. Most popular

indexes of respective countries are selected for study like S&P/TSX Composite Index for

Canada, S&P 500 index for US , BSE sensex for India, Straits Time Index for Singapore,

FTSE 100 Index for UK and Composite Index for Malaysia. In this four stage approach.

In the First stage integration of the series was tested using the unit root hypothesis on the

logarithms of the indices.


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The testing procedure followed is Dickey Fuller and augmented Dickey Fuller(ADF) as

well as respective tests incorporating a deterministic trend. In the second stage a

multivariate VAR system is constructed, with its corresponding VECM. Then maximum

likelihood tests of Johansen(1988) and Johansen and Jusilius are used to determine the

number of cointegrating vectors. The third stage used Engel-Granger causality through

the analysis of a vector error correction feedback mechanism in cointegrated models .

block Engel-Granger non-casuality, which is a variation of the Engel-Granger causality

restricted model that tests for mutual depentdency on each of the other variables lag

structures, is applied to the non-cointegrating VAR models. The last stage follows the

autoregressive distributed lag (ARDL) approach to cointegration, developed by Peasaran

and Shin (1995) based on the results of the previous two sections.

The tests show that India is cointegrated with both developing economies like US,UK

and Canada and other developing economies like Malaysia and Singapore. Canada being

a closed economy shows no cointegration with other countries. UK and USA are

cointegrated with each other. The economies Malaysia and Singapore are open to each

other and show a high level of integration with each otherl. The tests also shows some

contradictory results like India is integrated with all other six countries considered in the

test when India is taken as the dependent variable but when the regression was run taking

India as independent variable and residuals were tested for stationary it was observed that

India was not integrated with the countries.

According to Bala Arsanapalli, he examined the linkages and dynamic interaction among

stock price indices in the major world stock exchanges. The data used in this study are

daily closing stock market index time series.

This study concentrates on the worlds 5 largest stock exchanges : New York , Frankfurt

,London , Japan and Paris. The stock price performance across exchanges for the month

of October 1987 is characterized as very unusual in the rectent history of the stock

market. To assure that the results are not being influenced by the stock price data of this

period, two additional data sets are employed in this study: the pre-crash (January 1980-

September 1987) and the post-crash (November 1987 May 1990). Since national stock


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markets are generally operating in different time zones with different opening an closing

times, it is important to note that there is no trading overlap between the Tokyo stock

exchange and the exhanges in Paris , Frankfurt and London and between the New York

stock exchange and the exchanges in Frankfurt , paris and finally between the New York

and Tokyo exchanges.

In his research he uses a cointegration test which involves four steps.

Determine the presence of units(order of integration) by using Dickey Fuller and

Augmented Dickey Fuller

The second state involves estimating the following co integration regression

Xt = Ct + dyt, + zt

Where Xt = US stock market Index

yt = foreign stock market Index

In co integration the null hypothesis is non-co integration

We test for the stationarity of the co integration regression equation in error (zt )

DZt = -PZ t_ 1 +et

4. The estimation of error-correction model

Xt - Xt-1 = a0 + a1 Z t_ 1 +B1 (L) (Xt - Xt Xt ) + B2 (L)( yt - yt-1 ) + et

The evidence indicates that the degree of integrational comovements in stock price

indices has changed significantly since the crash of October 1987, with the Nikkei index

the only exception. Specifically, for the pre-crash period he find that France , germany

and UK stock markets are not related to the US stock market. This result is not consistent

with previous studies, which report substantial interdependence among these stock

markets. For the post-crash period, however , our results show that the major European

stock markets are indeed strongly linked with the US stock market. Moreover, the error-


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correction analysis given results with respect to the stock market interactions among the

five major world stock exchanges. The US stock market is found to have a substantial

impact on the French, German and UK markets in the post-crash period. Stock market

innovations in any of the three European stock markets have no impact on the US stock

market. In addition, he found no evidence of interdependence among the stock price

indices between US and Japan. He also found that the US and japan stock markets have

drifted far away from each other since the October crash. Finally a similar result is

obtained between japan and the three European stock markets. The pattern of

interanctions among France, Germany, UK and Japan suggests that Japanese stock

market innovations are unrelated to the performance of the major European stock

markets.

According to Bala Arsanapalli he examined the common stochastic trends among

national stock prices of the U.S. and five East Asian countis, including Japan, Taiwan,

Hong Kong, Singapme, and South Korea.

The methodology he used is JOHANSENS PROCEDURE.

JOHANSENS PROCEDURE

Xt = + xt-1 c . . . + t -1 Xt-k +t

Where: - Xt and , are of dimension p x 1, , - N(O,A), and Qs are p x p and p x 1

Regression coefficients. Following Johansen (1988, 1991), and Johansen and Juselius

(1990), Equation 1 can be reparametrized as:

Xt= + xt-1 +. . . + t -1 Xt-k+1 + xt-k

The short-term dynamics of the model are captured by matrices I, through Fk_1, while

matrix II provides information about the long-run relationships among the series. If the

series are nonststionary but co integrated, the rank of II, denoted by r, is less than the

dimension of II (i.e., 0 < r< p), and equals the number of co integration vectors in the


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system. In this case, there exist r stationary cointegrating relationships among the series

and p r common unit roots dictating the long-run stochastic trend of the variables.

He founded that the stock price series are found to be nonstationary and yet co integrated.

Two co integrating relationships are identified and the six stock price variables are found

to share four common unit roots. The result shows that the stochastic trends dictated by

the four common unit roots are important to the long-run movement of the stock prices,

especially those of the U.S. and Taiwan. Though not conclusive, the result suggests that

U.S. and Taiwan markets may not belong to a common stock region containing the

remaining four countries. The result also shows that most variables have the same

adjustment speed in moving from short-run disequilibria toward the common trend.

There are some more authors who studied different market to know whether they are

cointegrated or not. Hordick (1972), Argy and Hodreja (1973) and Salant and Sweeny

(1976) tested the degree of integration in different markets using different techniques.

Fase (1976) found evidence of substantial degree of market integration in eleven

European countries, base on monthly short-term interest rate data for the period of 1961-

1972 and using Principle Component analysis technique. The wave of globalization

accompanied by financial sector reforms in many emerging countries during 1990s (or

1980s more specifically) motivated many empirical studies in this area.

Mishkin (1982) studied the equality of real interest rates and other parity conditions for

countries UK, West Germany, Netherlands and Switzerland and found evidences that real

interest rates are not equal in these countries, although he acknowledged that there is a

tendency for real interest rates across these countries are equalizing over time.

Mark (1983), Cumby and Mishkin (1984) investigated the movement of real interest rates

in developed countries and found a strong positive correlation between interest rate

movements in US and these countries. Kasa (1992) examined number of common

stochastic trends in the equity market of US, Japan, England, Germany and Canada and

found a strong common trend driving stock prices of these countries

Cheun and Mark (1992) using weekly return series for the period of 1977 and 1988

Investigated the relationship between the two developed markets US and Japan and eight


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Asian-Pacific markets; Australia, HK, Korea, Malaysia, Philippines, Singapore, Taiwan

and Thailand and found that US market leads the stock market of most of these countries

with the exception of Korea, Taiwan and Thailand. While Japanese market found to have

a less important influence in this region. Malliaris and Urrutia (1992) analyzed lead-lag

relationship for six major stock market indexes2 for before and after 1987 market crash.

Although they did not find any causality for pre-crash and post-crash period but they

found important feedback relationship and unidirectional causality for the month of

causality.

Chung and Liu (1994) conducted a study to examine the common stochastic trend among

national stock prices of the US and five East Asian countries Japan, HK, Singapore,

Taiwan and Korea. Their study suggests that except Taiwan, all other countries in the

sample has a strong linkage with US market and holds same speed of adjustments from

short term disequilibrium.

Corhay et al (1995) conducted a study to investigate long-run relationship among five

major European markets France, Germany, Italy, Netherlands and UK and found

evidences of strong integration among these countries. Korajczyk (1996) and Harvey

(1995) found asymmetric integration relationship; stock markets of developed notions are

more integrated than those of emerging nations.

Choudhary (1994) test the stochastic structure of individual stock markets of US, UK,

Japan, Italy, France, Canada and Germany. Their study supports the efficient market

hypothesis. All stock indices contain a long-term permanent stochastic trend (unit root)

that makes long run predictions impossible. Using Johansen method of cointegration,

they found no evidence of the presence of common stochastic trend among these stock

markets (for the period of 1953-1989). In a different paper, same author (Choudhary

1997) used a bivariate framework for the period of 1985-1993 for six Latin-American

markets and found support for the existence of long-run relations

Solnik et al (1996) examined the correlation of volatility in stock prices in different

markets and found that this correlation increases during the period of high market

volatility. They also found that in the last 37 years (1959-1995) correlation of individual

foreign markets with the US stock market has increased slightly, but in the last 10 yrs

(1985-1995) there is no significant increase in this correlation.


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Research problem

There are various studies, which have analyzed the co-moments and

cointegration between various stock exchanges of different countries. We are not sure

whether the fluctuations of one index will affect the other and whether they are

cointegrated or not. This study examines the price integraton between two stock price

indices BSE sensex and S&P CNX Nifty in India.

Objectives of the study

To examine the price integration between the two domestic stock markets in India

i.e., Bombay Stock Exchange and National Stock Exchange, using the daily and monthly

closing on BSE Sensex and S&P CNX Nifty, from January 2000 to January 2006. Using

cointegration techniques of Engel- Engel-Granger at different lags the study finds that

there is sufficient evidence of long run relationship between the prices of both the

markets

Hypothesis of the study

Hypothesis 1

H0: There is no significant relation between BSE Sensex and S&P CNX Nifty.

H1: There is significant relation between BSE Sensex and S&P CNX Nifty.

Limitations of the study

The primary focus of the study is on the co movements between BSE Sensex and

S&P CNX Nifty by using Engle-Engel-Granger technique only.

The study is limited to only for two stock exchanges in India.

This study is limited for the period of January 2000 to January 2006


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Study Type: The study type is analytical, quantitative and historical.

Analytical because facts and existing information is used for the analysis,

Quantitative as relationship is examined by expressing variables in

measurable terms

Historical as the historical information is used for analysis and interpretation.

Study population: population is the closing of national stock exchange & Bombay

stock exchange.

Sample: Sample chosen is daily and monthly closing values of BSE Sensex, S&P CNX

Nifty.

Sampling technique: Deliberate sampling is used because only particular units are

selected from the sampling frame. Such a selection is undertaken as these units represent

the population in a better way and reflect better relationship with the other variable.

Period of the study: the period is different for different indices. S&P CNX nifty is

taken for ten years from January , 2000 to January , 2005, and BSE Sensex from January

, 2000 to January , 2005.

Data gathering procedures and instruments:

Data: Historical values of monthly closing and daily closing of BSE Sensex and S&P

CNX nifty.

Data Source: Historical share prices of the NSE sample are taken from

www.nseindia.com and BSE from www.financeyahoo.com .

Statistical Models

Augmented Dicky Fuller test to test the stationary of the series.

Engel-Grangers cointegration approach to test for co integration between the series.
http://www.financeyahoo.com/http://www.nseindia.com/


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Statistical Models

In this study, a co-integration approach the Engle-Engel-Granger methodology is

applied to study whether the BSE Sensex, S&P CNX Nifty are cointegrated before doing

co-integration analysis, it is necessary to test whether the time series are stationary at

levels by running Augmented Dickey fuller (ADF) test on the series. Because most time

series are non stationary in levels, and the original data need to be transformed to obtain

stationary series

Stationarity

According to Engle and Engel-Granger, a time series is said to be stationary if

displacement over time does not alter the characteristics of a series in a sense that

probability distribution remains constant over time. In other words, the mean, variance

and co-variance of the series should be constant over time. A nonstatioanry time series

will have a time varying mean or a time varying variance or both or are

autocorrelated.The degree of co-integration is closely related with stationary.

It is evident from the time-series literature that the standard estimation and

statistical test procedures are highly inappropriate, and even invalid, when the variables

involved are nonstationary.

The empirical works based on time series data assumes that the underlying time

series is stationary. In regressing a time series variable on another time series variables,

one often obtains a very high R2 (residuals) even though there is no meaningful

relationship between the two variables. This situation exemplifies the problem of

spurious or nonsense regression, which arises when data is non stationary.

A series is said to be integrated of order one [I (1)] if it has to be differentiated

once before becoming stationary. Similarly, a series is of order two [I(2)] if it has to be

differentiated twice before becoming stationary.


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test is carried out by estimating an equation with Yt-1 subtracted from both sides of the

equation.

yt = C + t-1 + t

Where = (-1), and the null and alternative hypotheses are

Ho: = 0 ..Non Stationary

H1: < 0 ..Stationary

Dickey and fuller simulated the critical values for selected sample sizes. More recently,

Mackinnon (19991) has implemented a much larger set of simulations than those

tabulated by Dickey and Fuller.

Unit root test [Augmented dickey fuller test]

The simple Unit root test is valid only if the series is an AR (1) Process. If the series is

correlated at high order lags, the assumption of white noise disturbances is violated. [In

other words, in DF test,it was assumed that the error term t was uncorrelated. But in

case the error ter mis correlated, Dickey and Fuller have developed a test, knoen as

Augmented Dickey Fuller test]. The ADF controls for higher - order correlation by

adding lagged difference terms of the dependent variable to the right-hand side of the

regression

Yt = C + t-1 + 1 yt-1 + 2 y t-2 + ..+ p y t-p + t

This augmented specification is then tested

H0: = 0 Non Stationary

H1: < 0 Stationary

The unit root test is based on the following three regression forms:

1. Without intercept and trend (random walk) Yt = Yt-1 + t

2. with intercept (random walk with drift) Yt = + Yt-1 +t

3. with intercept and trend( with drift around a stochactic trend) Yt = T + Yt-1 +t


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Where, is the intercept/constant, T is trend, is the slope i.e level of

dependency and integration, is drift parameter i.e.change from Yt to Yt-1 and t is the

error term.

In general, the procedure start with whether the variables X and Y in its level form under

none, intercept and trend and intercept is stationary. If the hypothesis is rejected, then the

series is transformed into first difference of the variable and tested for stationarity. If first

difference series is stationary, this implies that X and Y are I(1).

Engel-Grangers co-integration Test

The fundamental aim of co integration analysis is to detect any common

stochastic trends in the price data and to use these common trends for a dynamic analysis

of correlation in returns. Correlation is based only on return data, but full co integration

analysis is based on the raw prices, rate or yield as well as return data.

According to co integration theory, two variables that are stationary in changes

are co integration if a linear combination of them in levels is stationary. Thus, changes in

the prices are taken for running the test.

Engel-Granger introduced the concept of co-integration when he wrote that two variables

may move together though individually they are non stationary. Co-integration is based

on the long run relationship between variables. The idea arises from considering

equilibrium relationships, where equilibrium is a stationary point characterized by forces

that tend to push the variables back toward equilibrium.

In general, if Yt and Xt are both integrated of order I (d), then any linear combination of

the two series will also be I (d)... That is, the residuals obtained on regressing Yt on Xt

are I (d).

If two or more series are co integrated then even though the series themselves may be non

stationary, they will move closely together over time and their difference will be

stationary. Their long run relationship is the equilibrium to which the system converges

overtime and the disturbance term Et can be construed as the disequilibrium error or the

distance that the system is away from equilibrium at time t.


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Table 1: UNIT ROOT TEST [AUGMENTED DICKEY FULLER TEST] for BSE

DAILY. In this daily closing of BSE Sensex is taken from January 2000 to January 2006.

LAGS ADF VALUES SIGNIFICANCE

(%)

CRITICAL

VALUES

1 -2.5671

5 -1.9396

0 -35.77700

10 -1.6157

1 -2.5671

5 -1.9396

10 -1.6157

12 -10.52569

10 -1.6157

Level of significance graph

Hypothesis:

H0 = ADF > critical values -- not reject hull hypothesis i.e., unit root exists.

H1 = ADF < critical values reject null hypothesis i.e. unit root does not exist.

INTERPRETATION:-

For ADF test statistic at 0 lags:-

The ADF test statistics at 0 lags indicates that data is stationary at all the significant

levels such as 1%, 5%, 10%. Since the critical values are greater than the ADF test

statistics the data is stationary. Therefore the null hypothesis is rejected and we accept the

alternative hypothesis indicating that the data is stationary. Therefore unit root does not

exist.






exist.


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TABLE 2: UNIT ROOT TEST [AUGMENTED DICKEY FULLER TEST] for BSE

MONTHLY. In this monthly closing of BSE Sensex is taken from January 2000 to

January 2006.

LAGS ADF TEST

STATISTIC

SIGNIFICANCE

(%)

CRITICAL

VALUES

1 -2.5958

5 -1.9450

0 -7.8282292

10 -1.6182

1 -2.6026

5 -1.9462

12 -0.987514

10 -1.6187


Hypothesis:



INTERPRETATION:-






exist.

For ADF test statistic at 12lags:-

The ADF test statistics at 12 lags indicates that data is not stationary at the significant

level 1%, 5% and at 10% since the critical values are less than the ADF test statistics the

data is not stationary and the unit root exists. Therefore the null hypothesis is accepted.


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Table 3: UNIT ROOT TEST [AUGMENTED DICKEY FULLER TEST] for NSE

DAILY. In this daily closing of NSE S&P is taken from January 2000 to January 2006.

LAGS ADF TEST

STATISTIC

SIGNIFICANCE

(%)

CRITICAL

VALUES

1 -2.5671

5 -1.9396

0 -34.90966

10 -1.6157

1 -2.5671

5 -1.9396

12 -10.39008

10 -1.6157


Hypothesis:



INTERPRETATION:-






exist.






exist.


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Table 4: UNIT ROOT TEST [AUGMENTED DICKEY FULLER TEST] for NSE

MONTHLY. In this Monthly closing of NSE S&P is taken from January 2000 to

January 2006.

LAGS ADF TEST

STATISTIC

SIGNIFICANCE

(%)

CRITICAL

VALUES

1 -2.5958

5 -1.9452

0 -8.347865

10 -1.6183

1 -2.6040

5 -1.9464

12 -1.160638

10 -1.6188


Hypothesis:



INTERPRETATION:-





alternative hypothesis indicating that the data is stationary. Therefore unit root exists.

For ADF test statistic at 12lags:-The ADF test statistics at 12 lags indicates that data is not stationary at the significant

level 1%, 5% and at 10% since the critical values are less than the ADF test statistics the

data is not stationary and the unit root exists. Therefore the null hypothesis is accepted.


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Engel-Grangers Co integration Test:

Cointegration between S&P CNX Nifty DAILY and BSE SENSEX

DAILY

An ordinary least square (OLS) regression is done on the data. First x is regressed on y

then y on x.

X on Y -- Dependent variable(X) is BSE SENSEX daily and Independent variable(Y) is

S&P NIFTY daily.

Y on X -- Dependent variable(X) is S&P NIFTY daily and Independent variable(Y) is

BSE SENSEX daily.

BSE SENSEX t = a + b S&P NIFTY t + e t

S&P NIFTY t = a + b BSE SENSEX t + e t

TABLE 5: ADF UNIT ROOT TEST X ON Y

LAGS CONSTRAINTS ADF

VALUES

SIGNIFICANCE

(%)

CRITICAL

VALUES

1 -2.5671

5 -1.9396

0 FIRST

LEVEL OF

DIFFERENCE

X ON Y -35.92243

10 -1.6157

1 -2.5671

5 -1.9396

12 FIRST

LEVEL OF

DIFFERENCE

X ON Y -10.56207

10 -1.6157


Hypothesis:




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INTERPRETATION:-






exist we can say that BSE and NSE are cointegrated.







Regression analysis:-

ANOVA

TABLE 6Model Sum ofSquares

df MeanSquare

F Sig.

1 Regression 2.938E-04 1 2.938E-04 6.717 .010

Residual 6.518E-02 1490 4.374E-05

Total 6.547E-02 1491

Predictors: (Constant), NSEDAILYDependent Variable: BSEDAILY

Coefficients

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

Model B Std. Error Beta

1 (Constant) 1.518E-04 .000 .886 .376

NSEDAILY 6.807E-02 .026 .067 2.592 .010

Dependent Variable: BSEDAILY


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General rule of Cointegration:-

The Grangers Cointegration test has been conducted by running the OLS

Regression equations. The residuals have been obtained for BSE & NSE daily from

the estimated equations. Then the Unit Root test has been done to check the

Stationarity of residual series. The ADF calculated statistics shows that there is no

Unit Root. If the residuals do not have Unit Roots, then the series is said to be

Cointedgrated at I (1). Therefore the series is Cointegrated at I (1,0) and I (1,12). It

is bidirectional with reference to ANOVA TABLE 6.

TABLE 7: ADF UNIT ROOT TEST Y ON X


VALUES

SIGNIFICANCE

(%)

CRITICAL

VALUES

1 -2.5671

5 -1.9396

0 FIRST

LEVEL OF

DIFFERENCE

Y ON X -35.7891

10 -1.6157

1 -2.5671

5 -1.9396

12 FIRST

LEVEL OF

DIFFERENCE

Y ON X -11.61969

10 -1.6157


Hypothesis:



INTERPRETATION:-








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Regression analysis:-ANOVA TABLE 8

Model Sum ofSquares

df MeanSquare

F Sig.

1 Regression 2.846E-04 1 2.846E-04 6.717 .010

Residual 6.313E-02 1490 4.237E-05

Total 6.341E-02 1491

Predictors: (Constant), BSEDAILYDependent Variable: NSEDAILY

Coefficients

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.


1 (Constant) 1.490E-04 .000 .884 .377

BSEDAILY 6.593E-02 .025 .067 2.592 .010

Dependent Variable: NSEDAILY



Regression equations. The residuals have been obtained for BSE & NSE daily from

the estimated equations. Then the Unit Root test has been done to check the






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INTERPRETATION:-







For ADF test statistic at second level of difference 12 lags:-







Model Sum ofSquares

df MeanSquare

F Sig.

1 Regression 1.158E-02 1 1.158E-02 14.806 .000

Residual 5.238E-02 67 7.818E-04

Total 6.395E-02 68

Predictors: (Constant), NSEMONTHDependent Variable: BSEMONTH

Coefficients

Unstandardi

zedCoefficients

Standardize

dCoefficients

t Sig.


1 (Constant) 1.200E-03 .003 .354 .724

NSEMONTH .372 .097 .425 3.848 .000

Dependent Variable: BSEMONTH


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Regression equations. The residuals have been obtained for BSE & NSE monthly

from the estimated equations. Then the Unit Root test has been done to check the





TABLE 11: ADF UNIT ROOT TEST Y ON X


VALUES

SIGNIFICANCE

(%)

CRITICAL

VALUES

1 -2.5968

5 -1.9452

0 FIRST

LEVEL OF

DIFFERENCE

Y ON X -10.22153

10 -1.6183

1 -2.6048

5 -1.9465

12 SECOND

LEVEL OF

DIFFERENCE

Y ON X -5.081737

10 -1.6189


Hypothesis:



INTERPRETATION:-

For ADF test statistic at 0 lags:-The ADF test statistics at 0 lags indicates that data is stationary at all the significant






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Model Sum ofSquares

df MeanSquare

F Sig.

1 Regression 1.516E-02 1 1.516E-02 14.806 .000

Residual 6.860E-02 67 1.024E-03Total 8.376E-02 68

Predictors: (Constant), BSEMONTHDependent Variable: NSEMONTH

Coefficients

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.


1 (Constant) 2.544E-03 .004 .658 .513

BSEMONTH .487 .127 .425 3.848 .000

Dependent Variable: NSEMONTH



Regression equations. The residuals have been obtained for BSE & NSE monthly

from the estimated equations. Then the Unit Root test has been done to check the



Cointegrated at I (1). Therefore the series is Cointegrated at I (1,0) and I (1,12). It is

bidirectional with reference to ANOVA TABLE 12.


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Conclusions:-

We check the data series for stationarity by Augmented Dickey-Fuller unit root test. A

data series is said to be stationary if its mean variance are constant. The time series both

BSE and NSE were stationary which supports that the series can be used for further

analysis.

There are many factors which are affecting the price cointegration such as the

information flows from one market to another market. The results are very useful to

regulators as well as to market participants NSE used to follow accounting period

settlement starting from Wednesday and ending on the following Tuesday. Wednesday

being the first day it is advantageous for traders to buy / sell and keep the position opens

till next Tuesday. Investors get longest possible period without full investment. Where as,

BSE used to follow Monday to Friday accounting period settlement. Owing to this

different accounting period settlement there were arbitrage opportunities available.

By using the Engel-Granger the study finds that there is sufficient evidence of long-run

relationships between BSE Sensex and S&P Nifty for different lags i.e. 0 and 12.


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ADF Test Statistic -35.77700 1% Critical Value* -2.5671

5% Critical Value -1.9396


*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(BSEDAILY)

Method: Least Squares

Date: 06/18/06 Time: 14:00

Sample(adjusted): 1/04/2000 9/20/2005

Included observations: 1491 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob.

BSEDAILY(-1) -0.923621 0.025816 -35.77700 0.0000

R-squared 0.462093 Mean dependent var -3.89E-06

Adjusted R-squared 0.462093 S.D. dependent var 0.009009

S.E. of regression 0.006607 Akaike info criterion -7.200665

Sum squared resid 0.065045 Schwarz criterion -7.197106

Log likelihood 5369.096 Durbin-Watson stat 1.988115


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-0.06

-0.04

-0.02

0.00

0.02

0.04

1/03/00 12/03/01 11/03/03 10/03/05

BSEDAILY


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ADF Test Statistic -10.52569 1% Critical Value* -2.56715% Critical Value -1.9396




Dependent Variable: D(BSEDAILY)


Date: 06/18/06 Time: 14:01Sample(adjusted): 1/20/2000 9/20/2005



BSEDAILY(-1) -0.904771 0.085958 -10.52569 0.0000

D(BSEDAILY(-1)) 0.000188 0.082731 0.002270 0.9982

D(BSEDAILY(-2)) -0.043580 0.079471 -0.548371 0.5835

D(BSEDAILY(-3)) -0.035484 0.076243 -0.465415 0.6417

D(BSEDAILY(-4)) 0.043481 0.072703 0.598055 0.5499

D(BSEDAILY(-5)) 0.003211 0.068497 0.046884 0.9626D(BSEDAILY(-6)) -0.042231 0.064207 -0.657727 0.5108

D(BSEDAILY(-7)) -0.008635 0.059265 -0.145693 0.8842

D(BSEDAILY(-8)) -0.030226 0.054371 -0.555919 0.5784

D(BSEDAILY(-9)) 0.025690 0.049562 0.518336 0.6043

D(BSEDAILY(-10)) 0.045875 0.042979 1.067369 0.2860

D(BSEDAILY(-11)) 0.023634 0.035204 0.671336 0.5021

D(BSEDAILY(-12)) 0.007140 0.025952 0.275118 0.7833

R-squared 0.465327 Mean dependent var 8.98E-06

Adjusted R-squared 0.460951 S.D. dependent var 0.008922S.E. of regression 0.006550 Akaike info criterion -7.209810

Sum squared resid 0.062904 Schwarz criterion -7.163233

Log likelihood 5344.655 Durbin-Watson stat 1.997350


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-0.06

-0.04

-0.02

0.00

0.02

0.04

1/03/00 12/03/01 11/03/03 10/03/05

BSEDAILY


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Dependent Variable: D(XONY,2)


Date: 06/18/06 Time: 13:26

Sample(adjusted): 1/05/2000 9/21/2005



D(XONY(-1)) -0.927734 0.025826 -35.92243 0.0000

R-squared 0.464110 Mean dependent var -0.027794


S.E. of regression 72.37837 Akaike info criterion 11.40236

Sum squared resid 7805557. Schwarz criterion 11.40592

Log likelihood -8499.461 Durbin-Watson stat 1.985296


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2000

4000

6000

8000

10000

1/03/00 12/03/01 11/03/03 10/03/05

XONY


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ADF Test Statistic -10.56207 1% Critical Value* -2.56715% Critical Value -1.9396






Date: 06/18/06 Time: 13:30

Sample(adjusted): 1/21/2000 9/21/2005



D(XONY(-1)) -0.907319 0.085904 -10.56207 0.0000

D(XONY(-1),2) 0.006444 0.082767 0.077861 0.9379

D(XONY(-2),2) -0.050881 0.079542 -0.639667 0.5225

D(XONY(-3),2) -0.028927 0.076366 -0.378792 0.7049

D(XONY(-4),2) 0.048135 0.072752 0.661622 0.5083

D(XONY(-5),2) 0.004309 0.068447 0.062947 0.9498

D(XONY(-6),2) -0.041849 0.064310 -0.650739 0.5153

D(XONY(-7),2) 0.010654 0.059372 0.179436 0.8576

D(XONY(-8),2) -0.039617 0.054500 -0.726912 0.4674

D(XONY(-9),2) 0.018936 0.049751 0.380611 0.7035

D(XONY(-10),2) 0.055764 0.043182 1.291366 0.1968

D(XONY(-11),2) 0.031583 0.035282 0.895147 0.3709

D(XONY(-12),2) 0.023134 0.026035 0.888605 0.3744

R-squared 0.469182 Mean dependent var 0.129436






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2000

4000

6000

8000

10000

1/03/00 12/03/01 11/03/03 10/03/05

XONY


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Dependent Variable: D(YONX,2)


Date: 06/18/06 Time: 13:35

Sample(adjusted): 1/05/2000 9/21/2005



D(YONX(-1)) -0.924866 0.025842 -35.78971 0.0000




Sum squared resid 757662.2 Schwarz criterion 9.073569



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Date: 06/18/06 Time: 13:40

Sample(adjusted): 1/21/2000 9/21/2005



D(YONX(-1)) -1.072493 0.092300 -11.61969 0.0000

D(YONX(-1),2) 0.169153 0.088044 1.921236 0.0549

D(YONX(-2),2) 0.049822 0.084187 0.591795 0.5541

D(YONX(-3),2) 0.091556 0.080446 1.138104 0.2553

D(YONX(-4),2) 0.134980 0.076081 1.774157 0.0762

D(YONX(-5),2) 0.130492 0.071371 1.828372 0.0677

D(YONX(-6),2) 0.095006 0.066820 1.421809 0.1553

D(YONX(-7),2) 0.103239 0.061707 1.673055 0.0945

D(YONX(-8),2) 0.054258 0.056648 0.957808 0.3383

D(YONX(-9),2) 0.070304 0.051122 1.375224 0.1693

D(YONX(-10),2) 0.129067 0.044176 2.921689 0.0035

D(YONX(-11),2) 0.075151 0.035335 2.126789 0.0336

D(YONX(-12),2) 0.018005 0.026125 0.689210 0.4908




Sum squared resid 723530.7 Schwarz criterion 9.094811Log likelihood -6678.169 Durbin-Watson stat 1.994124


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400

800

1200

1600

2000

2400

1/03/00 12/03/01 11/03/03 10/03/05

YONX


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Date: 06/18/06 Time: 13:43

Sample(adjusted): 2000:03 2005:10



D(XONY(-1)) -0.999719 0.127867 -7.818444 0.0000







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2000

3000

4000

5000

6000

7000

8000

2000 2001 2002 2003 2004 2005

XONY


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Date: 06/18/06 Time: 13:46




D(XONY(-1),2) -8.688357 2.619005 -3.317427 0.0019

D(XONY(-1),3) 6.684659 2.542637 2.629026 0.0119

D(XONY(-2),3) 5.864413 2.397331 2.446226 0.0187

D(XONY(-3),3) 5.148280 2.200984 2.339081 0.0242

D(XONY(-4),3) 4.469174 1.986172 2.250144 0.0297

D(XONY(-5),3) 3.523455 1.777164 1.982628 0.0540

D(XONY(-6),3) 2.806212 1.560637 1.798120 0.0793

D(XONY(-7),3) 2.193991 1.336596 1.641477 0.1082

D(XONY(-8),3) 1.493572 1.099031 1.358989 0.1814

D(XONY(-9),3) 0.874570 0.861087 1.015658 0.3156

D(XONY(-10),3) 0.481749 0.617843 0.779728 0.4399

D(XONY(-11),3) 0.272711 0.381816 0.714249 0.4790

D(XONY(-12),3) 0.134952 0.168477 0.801015 0.4276







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2000

3000

4000

5000

6000

7000

8000

2000 2001 2002 2003 2004 2005

XONY


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Date: 06/18/06 Time: 13:48




D(YONX(-1)) -1.303646 0.127539 -10.22153 0.0000







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400

200

0

200

400

2000 2001 2002 2003 2004 2005

YONX


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+price integration in the indian stock market

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