The relationship between vietnam united states and related asian stock markets luận văn thạc sĩ

And recent movements of international financial liberation in Vietnam and its region raise questions whether Vietnam and its region stock markets in Asian are being integrated into world

THE RELATIONSHIP

UNITED STATES

STOCK MARKETS

TRẦN THÙY HUYÊN

THE RELATIONSHIP BETWEEN VIETNAM,

STATES AND RELATED A STOCK MARKETS

MASTER THESIS

Ho Chi Minh City 2011

VIETNAM, RELATED ASIAN

MINISTRY OF EDUCATION AND TRAINING

UNIVERSITY OF ECONOMICS HOCHIMINH CITY

THE RELATIONSHIP BETWEEN VIETNAM, UNITED STATES AND RELATED

STOCK MARKETS

MAJOR: BANKING AND

Supervisor:

MINISTRY OF EDUCATION AND TRAINING

UNIVERSITY OF ECONOMICS HOCHIMINH CITY

Ho Chi Minh City 2011

MINISTRY OF EDUCATION AND TRAINING

UNIVERSITY OF ECONOMICS HOCHIMINH CITY

THE RELATIONSHIP BETWEEN VIETNAM,

ASIAN

PROFESSOR TRAN HOANG NGAN

sociable, motivation and professional guidance and comment I understand you sacrificed your valuable time and resources to help me conduct this thesis

Secondly, my heartfelt gratitude goes to my supervisor the Professor Tran Hoang Ngan It is by his clemency that I am writing this thesis today Thank you very much for your understanding, commitment and your advice regarding my thesis

Professors and the entire lecturers in University of economics HCMC, I thank you for academic guidance and other support during my study

My master’s classmates, I thank you for your advice and I will always cherish the moments we had together

Last but not least, I would like to thank my family, especially my husband, my baby for the moral support and patience I am particularly grateful to them for the moral and advisory support during the entire of my studies and my life

ABSTRACT

The international relationship amongst the stock markets plays an important roles and implications for cost of capital and international portfolio diversification to both Vietnamese and foreigner investors Vietnam becomes widely opening the trade and equity market since its entering to WTO in 2007 And recent movements of international financial liberation in Vietnam and its region raise questions whether Vietnam and its region stock markets in Asian are being integrated into world stock markets This research aims to examine that Vietnam and related Asian stock markets have interrelationships to the world stock markets using the daily data for the period 2005-2010 In determining the impact of the international financial crisis

on interrelationship amongst stock markets, the research period is being divided into two sample periods: the pre crisis period spans from January 1st, 2005 to July 22,

2007 and the crisis period spans from July 23, 2007 to June 30, 2010 Seven indices

of six stock markets are chosen based on the countries’ level of development and geographical factor Six stock markets are Vietnam (Vietnam index), Singapore (Straits times’ index), Hong Kong (Hang Seng index), China (Shanghai stock exchange), Japan (Nikkei index) and United States (Standard & Poor’s index and Dow Jones index)

The first content examines whether there is long run comovement amongst the stock markets in sample Bivariate and multivariate Johansen test are applied to daily stock price and daily stock return after conducting the tests for unit root with three tests of Dickey Fuller, Augmented Dickey Fuller and Phillip Perron The second content investigates whether there is causal relationship amongst these markets using Granger causality test The converging test is employed to know whether they are converging over time To analyses the return linkage, we replied

on variance decomposition analysis

The first finding of research is that only two cointegrations exist between two pairs

of US-China and US-Hong Kong stock market in the pre crisis period and no

cointegrations exist in the crisis period when we conduct the bivariate Johansen test for daily stock price Moreover, we also could not find the cointegration exist when multivariate Johansen tests when daily stock price is used These results imply that the portfolio diversification is potentially worthwhile for investors However, the results of both bivariate and multivariate test for daily return are opposite Cointegrations are found in the pre crisis and during the crisis period It can be concluded that portfolio diversification may not bring benefits to investors The Granger causality test results indicate the current global financial crisis affect significantly to the Granger causality relationship amongst the markets in sample and US market are recognized as a significant influence on the returns of Asian stock markets In addition, converging trend test results find that seven indies of six stock markets converge towards the group in whole period The five Asian stock markets are also converging to US stock market in the whole period and crisis period Except for Hong Kong and China, the other markets are converging to the group and US stock markets in the pre crisis time When apply the variance decomposition analysis, we get the findings demonstrate that most of indices of six stock markets in our sample demonstrate the strong endogenous characteristics except for US, Vietnam and China stock market Generally, the results of this research find the relationship amongst Vietnam, US and other related Asian stock markets in both sub periods Hence, the Vietnam and international investors should focus on portfolio diversification to other stock markets On the other hand, policy makers should draw up the appropriate macroeconomic strategies to promote economic performance in Asian region and in the world because high international financial integration will help to upgrade the financial development

Key word: cointegration, stock markets, Granger causality, integration

TABLE OF CONTENT

Acknowledgement i

Abstract ii

TABLE OF CONTENT iv

LIST OF FIGURES vi

LIST OF TABLE vi

I INTRODUCTION 1

1 Rationales of research 1

2 Problem statements 3

3 Research objectives and research questions 4

3.1 Research objectives 4

3.2 Research questions 4

4 Scope and methodology 5

4.1 Scope of research 5

4.2 Methodology of research 5

5 Structure of research 7

II LITERATURE REVIEW 8

III METHODOLOGY 15

1 Johansen Cointegration test 15

1.1 Unit root test 16

1.1.1 The Dickey-Fuller test (DF) 16

1.1.2 The Augmented Dickey-Fuller test (ADF) 17

1.1.3 The Phillips-Perron test (PP) 18

1.2 Cointegration test (Johansen) 19

2 Granger causality 20

3 Converging trend 22

4 Variance decomposition analysis 22

IV DATA AND DATA DESCRIPTION 24

1.Data 24

2 Data descriptive statistics 26

2.1 Summary statistics 26

2.2 Correlation 30

V EMPIRICAL RESULTS 33

1 Analysis of results of unit root test 33

1.1 The Dickey-Fuller test (DF) 33

1.2 The Augmented Dickey-Fuller test (ADF) 34

1.3 The Phillips-Perron test (PP) 35

2 Cointegration 38

2.1 Johansen cointegration test with daily stock price 38

2.1.1 Bi-variate cointegration test 38

2.1.2 Multivariate cointegration test 44

2.2 Johansen cointegration test with daily stock returns 45

2.2.1 Bi-variate cointegration test 45

2.2.2 Multivariate cointegration test 50

3 Granger causality 55

4 Converging trend 59

5 Variance decomposition analysis 61

VI CONCLUSION 64

1 Conclusion related to research questions 64

2 Implications 68

3 Contribution of research 70

4 Limitation and Further research 71

Appendix 73

List of References 130

LIST OF FIGURES

Figure 1.1: Structure of research 7

Figure 4.1: Movement of daily closing price of stock index from Jan 1st 2005 to Jun 2010 25

Figure 5.1: Short run causality effects (whole period) 56

Figure 5.2: Short run causality effects (pre crisis period) 57

Figure 5.3: Short run causality effects (crisis period) 59

LIST OF TABLES Table 4.1 Summary statistics of the stock returns (Whole period) 27

Table 4.2: Summary statistics of the stock returns (Pre-crisis period) 28

Table 4.3: Summary statistics of the stock returns (Crisis period) 29

Table 4.4: Correlation matrix of average daily return (Whole period) 30

Table 4.5: Correlation matrix of average daily return (Pre-crisis period) 31

Table 4.6: Correlation matrix of average daily return (Crisis period) 31

Table 5.1: Var lag Order Selection 33

Table 5.2: DF unit root test (daily price) 34

Table 5.3: ADF unit root test (daily price) 35

Table 5.4: PP unit root test (daily price) 36

Table 5.5: DF unit root test (daily return) 36

Table 5.6: ADF unit root test (daily return) 37

Table 5.7: PP unit root test (daily return) 37

Table 5.8: Bi-variate cointegration test result with intercept (no trend) in CE and test VAR (daily price of all stocks) 40

Table 5.9: Multivariate cointegration test result with intercept (no trend) in CE and test VAR (daily stock price) 44

Table 5.10: Bi-variate cointegration test result with intercept (no trend) in CE and test VAR 46

Table 5.11: Multivariate cointegration test result with intercept (no trend) in CE and test VAR 51

Table 5.12: Granger causality results (Whole period) 56

Table 5.13: Granger causality results (Pre crisis period) 57

Table 5.14: Granger causality results (Crisis period) 58

Table 5.15: Test result for converging trend 60

Table 5.19: the comparision of degree of exogeneity in pre crisis and crisis period 63

Table 5.16: the comparision of degree of exogeneity in whole period 72

Table 5.17: the comparision of degree of exogeneity in pre crisis period 75

Table 5.18: the comparision of degree of exogeneity in crisis period 78

Researches on regional stock market linkages have become increasingly important for most investors The Asian region is vulnerable to ‘shocks’ (financial crisis as an example) and where the crisis is contagious, it can affect the entire region For this reason, the countries in the region should become more concerned about their interdependency in the event of any occurrences of any financial crisis For instance, the Asian financial crisis began with collapse of Thai baht in July 1997 and its stock market, and the subsequent erosion in Hong Kong and other Asian markets in October 1997 and as a result, the co-movement amongst the Asian financial markets increased (Maran, (2010) Thus, a question can be raised from the 2007-2010 international financial crisis whether this international financial crisis causes the changes of interrelationship amongst Vietnam stock market, US and region stock markets in short run and long run

In globalization integration economic environment, both investors and portfolio managers shall pay more attention to the knowledge of the international stock market structure Many financial theories suggest that individual and institutional investors should hold a well-diversified portfolio to reduce risk As investors become more risk averse, further risk diversification continues to be their main concern To the international investor who is willing to make portfolio investments

in different stock markets, they really need to know that diversification can give some gain or not The world co-movements amongst financial markets have reduced the diversification benefit The international financial markets are quickly integrating into a global market since investors are driven to developing countries searching for higher returns and opportunities for risk diversification If the stock markets amongst different countries move together, investment in different stock markets shall not create any long term gain to portfolio diversification Hence researching results of stock market integration are useful when Asian economies are fastest growing economies This research results could be useful for investors, portfolio managers, corporate executives and policy makers

To our best knowledge, most empirical works have focused on developed markets, developing markets of South East Asia such as Becker et al (1990), Mak (1992) or Chan (1992) However, researches on the Vietnam stock market or Vietnam stock market’s data of the 2007-2010 international financial crisis have been very limited This paper will investigates the interrelationship in long and short run amongst Vietnam, US and other related Asian stock markets in the pre crisis in 2007-2010 and during the crisis of period 2007-2010 By this research, we hope to contribute towards adding the literature by providing the latest empirical proof on this topic

2 Problem statement

Vietnam becomes WTO’s 150th member on 11 January 2007 According to the route of entering WTO, Vietnam opens the economics including the financial market as from the end of the year 2007 It means that Vietnam will play an important role in world economics Whatever happens to Vietnam economics may affect to the economic in region and the world Conversely, the regional and international economics change will cause effect to Vietnam And the stock market

is not considered an exception However, there is an argument that some researchers conclude Vietnam stock market is frigid with world stock market while other researchers believe that Vietnam is affected by the US and some other stock markets in region In addition, it is well know that many investors in Vietnam stock market still invest with a crowd psychology or “tam ly dam dong” in Vietnamese

As a result, not many investors pay attention to the region or world stock market’s fluctuation

The issues of financial integration or stock markets cointegration between emerging stock markets and developed stock markets have attracted a great deal of interest of policy makers and finance researchers The emerging stock markets in some developing countries have achieved considerable improvements in recent time These improvements of emerging stock markets come from some factors such as: stock market renovations, financial liberalization, economics policies… An important point for this research is because there is an increase in funds flowing from developed markets such as US and Japan toward developing markets like Vietnam, Hong Kong, China… Therefore, these markets are becoming increasingly important in terms of portfolio management Hawawini (1994)

In summary the problem to be addressed in this research is to study the relationship amongst Vietnam, US and other Asian stock markets The research results will be useful to investors, policy makers and academic scholars

3 Research objectives and research questions

3.1 Research objectives

Research objectives are defined as the vision of researcher of the research problem Their roles are explanation the purpose of the research in measurable and defined standards of what the research should accomplish Zikmund (1997) In order to solve the research problems, this research intends to achieve the following objectives:

The first objective of this research is to examine the interrelationship amongst the stock markets of Vietnam, US, Singapore, Hong Kong, China and Japan using the daily data for the period from January 1st 2005 to June 30th 2010

The second objective is to evaluate the level of stock markets integration and how these stock markets affect together

The third objective is to study the changes of integration through the 2007-2010 financial crisis From there, investors can forecast movement of Vietnam stock market replying on the world and region stock market’s movement And the policy makers can issue effective policies when the international crises occur

Final objective is to suggest investment and policy advice

3.2 Research questions

According to Zikmund (1997), research questions involve the research translation of problem into the need for inquiry As above research problems and research objectives, the tasks of this research are to answer questions following:

Question 1: Is there a relationship between Vietnam and Asian stock market with

US stock market? (Answer in part VI)

Question 2: Is there a relationship between Vietnam stock market & related Asian stock markets of Singapore, Hong Kong, China and Japan? (Answer in part VI)

Question 3: Is there a cause-effect relationship between these stock markets? How the relationship changes before and during the global financial crisis of 2007-2010? (Answer in VI)

4 Scope and methodology

4.1 Scope of research

This research examines the relationship amongst Vietnam, US and the Vietnam’s region stock markets We choose some countries around Vietnam as a representative of Asian stock markets such as Singapore, Hong Kong, China and Japan The changes of these stock markets relation through the international financial crisis in 2007-2010 are conducted in this research so the period time is chosen from 2005 to June 2010 Choosing more countries and longer period of time may give more exact and convinced results

4.2 Methodology of research

In order to solve the research problems, achieve the research objectives and answer the research questions, it is quite important to choose and apply suitable methodologies Based on the theory in literature review and guideline from Brooks (2008), we apply some following methodologies and econometric testings as a brief description:

• Carrying out a review of the relevant theoretical literature and empirical literature

• Getting daily data of each index from yahoo.finance website from 2005 to June 2010

• Conducting data descriptive statistics (summary basic statistic and simple correlation estimation) Some of basic statistic tests are mean, median, standard deviation, skewness, kurtosis and others

• Applying the test for stationary with three tests: The Dickey-Fuller test (DF), The Augmented Dickey-Fuller test (ADF) and The Phillips-Perron test (PP)

• Performing the Johansen cointegration test to test for cointegration amongstst the sample stock markets

• Carrying out the Granger causality test to find the causal effect amongstst these markets

• Executing the test of converging trend to identify the converging or diverging trend of variables

• Implementing variance decomposition analysis to identify exogenous and endogenous variable

Above methodologies are test by the Eview 6 software Part 3 will mention detail of each above methodologies

II LITERATURE REVIEW

A great numbers of previous related studies researched on the relationship between stock markets in the world We can summary the content of these studies as follow: studied the interrelationship/linkage amongst the stock markets, examine the long run cointegration between some stock markets, investigate the benefit/risk of international diversification and find the leading market amongst developed countries

The first topic is studied the interrelation/linkages amongst the stock markets

Becker et al (1990) examine the intertemporal relation between the US and Japanese stock markets They find a high correlation between US stocks’ previous trading day and the Japanese equity market’s current period performance Meanwhile the Japanese market has a small effect on the US return in the current trading period Later, Cheung (1992) study the causal relationship between developed markets and the Asian Pacific markets for the year from 1977 to 1988 They conclude that the Asia Pacific markets seem to be led by the US market apart from Thailand, Taiwan and Korea The Japanese market also impacts these markets but it is not more important than US

Following in Cheung (1992) footstep, Chan (1992) seek the relationship of stock prices in major Asian markets and the US market They analyze over 1,000 observations of daily data and 224 observations of weekly data of Wednesday’s closing price for each market of Hong Kong, South Korea, Singapore, Taiwan, Japan and Standard & Poor’s 500 index (United States) from 1983 to 1987 Their unit root test results suggest that the individual stock markets are weak form efficient and no evidence of cointegration is found both single countries’ stock price and stock price of group countries

After that Park (1993) uses a vector autoregression analysis to analysis the interrelation between the equity markets of Pacific Basin countries and those of the

US, UK and Japan There are three results from this research US are most effective

to Australia UK and Japan have less interrelation to Pacific Baisan than US A group of Hong Kong, Singapore and New Zealand would have more linkages Bala Arshanapalli (1995) researches the pre and post-October 1987 stock market linkages between US and Asian markets This paper finds the evidences that the linkages increase since October 1987 It is again minded that the US market are most influential to Asian equity market compared to that of Japan market This integration would be grader during the post October 1987 period

One more researching related to crisis in year 1987 is Palac-McMiken (1997) They discover the cointegration amongst ASEAN stock markets They analyze the data of monthly ASEAN market index including Singapore, Thailand, Indonesia, Malaysia and Philippines from 1987 to 1995 Their outcomes are that ASEAN markets are not efficient for the period studied However those markets appear to be linked to each other except for Indonesia market Janakiramanan (1998) carries out the linkages between Pacific-Basin stock markets during period 1988 to 1996 by using

a vector autoregression model They report that US market influence those markets except for Indonesia

Later Abul (1999) starts with fluctuations of Asian stock market due mainly to intra-regional contagion effects based on Asian emerging stock markets and using vector error-correction model (Toda and Phillips, 1993) and level VAR model containing integrated and cointegrated processes of arbitrary orders (Toda and Yamamoto, 1995) They also find a high level of interdependence amongst these markets in Malaysia, Thailand, Japan, Hong Kong, Singapore and US during the period from 1992 to 1997 Sheng (2000) surveys cointegration and variance decomposition amongst national equity indices before and during the period of the

Asian financial crisis They record the evidences of the interrelation between US

The second content is examine the long run cointegration between some stock

markets such as Andy et al (1995) studies relationship of nine stock markets The

Engle and Granger cointegration analysis and Granger causality tests are applied to monthly time series of nine major stock market indices over the period January

1982 to February 1991 to examine for causal linkages The empirical results indicate that there are adequate evidences to refute the notion of informationally efficient stock markets So no long run relationship exists Stulz (1996) investigates the properties of cross country stock return comoverments of US and Japan from

1988 to 1992 They construct overnight and intraday returns for a portfolio of Japanese stocks using their NYSE-traded American Depository Receipts (ADRs) and a matched-sample portfolio of US stocks We find that US macroeconomic announcements, shocks to the Yen/Dollar foreign exchange rate and Treasury bill returns and industry effects have no measurable influence on US and Japanese return correlations However, large shocks to broad-based market indices (Nikkei Stock Average and Standard and Poor's 500 Stock Index) positively impact both the magnitude and persistence of the return correlations The results suggest that covariance change over time and can be forecasted using various instrumental variables They also conclude that it is not suitable to assume that covariance between countries are constant

Ng.(2002) examines the linkages between the South–East Asian stock markets following the opening of the stock markets in the 1990s No evidence is found to indicate a long–run relationship amongst the South–East Asian stock markets over the period 1988–1997 However, correlation analyses indicated that the South–East Asian stock markets were becoming more integrated The results from the time–varying parameter model also show that the stock market returns of Indonesia, the

Philippines and Thailand have all become more closely linked with that of Singapore

Jian Yang (2003) examines whether long-run integration between the United States and many international stock markets has strengthened over time, with special attention paid to the impact of the abolition of capital control in these markets and the 1987 international stock market crash with data from (January 1970-December 2001) The results show that no long-run relationship between most of these markets and the United States exist However, there are evidences of recent increasing integration between many smaller markets and the United States while

no such pattern emerges for larger markets including Japan, the United Kingdom, and Germany, which suggests long-run benefits to U.S investors of diversifying into these larger markets

Ng (2010) studies an analysis on the long run relationship and risk diversification amongst Malaysian and Tiger markets (Hong Kong, South Korea, Singapore and Taiwan) with adopting the Johansen multivariate cointegration test and VECM using a five variable model and followed Granger causality test Their findings conclude that existed the long run relationship amongst the regional stock markets though such relationships appear to be weak in the short run The Hong Kong, South Korea and Taiwan markets influence the Malaysian and Tiger markets and the Malaysian market affected the Singapore market

The third content is investigation of benefit/risk of international diversification

Markridakis (1974) investigates an analysis the interrelationships amongst the major world stock exchanges Their purposes are to analyze the potential gains from international portfolio diversification amongst stock markets of France, Germany, England, Canada, Australia, Japan, Belgium, Netherlands, Italy, Sweden, and Switzerland They also divide the studied period into three sub-periods of bull market (5/1/1968-29/11/1968), bear market (29/11/1968-26/5/1970) and bull market

(26/5/1970-30/9/1970) in three cases of one day lag, no lag-no lead and one day lead by using the factor analysis and principal components analysis Two conclusions drawn from this study are the unstable relationships amongst these markets and no way to predict the form of these possible interrelationships before

the fact

Angelos (1999) discovers the long run benefits from international equity diversification for a UK investor diversifying in the US equity market by using the MSCI indices from 03/01/1983 to 29/11/1996 (entire period), 03/01/1983 to 30/09/1987 (pre crash period) and 1/11/1987 to 29/11/1996 (post crash period) Their methodologies are the ADF test with a deterministic trend and cointegration tests based on the Johansen method The conclusions are those the long run diversification benefits for a UK investor diversifying in the US equity market are reduced during the post crash period And the long run relationship during the post crash period exists and no long run relationship during the pre crash period

David Ely (2001) studies American depositary receipts-analysis of international stock price movements They employ the cointegration techniques and estimate error correction (EC) models to examine the degree of integration between US (American depositary receipts-ADRs) and three foreign equity markets (UK, Japan and Germany) They use data from 02/1/1996 to 31/3/1999 They find that ADRs were cointegrated with ordinary shares trading in three markets For long term investors, ADRs are a substitute for ordinary shares trading in foreign markets

Tzeng (2009) examines the international equity diversification between the US and its major trading partners (Canada, Germany, Japan and Mexico) with more powerful nonparametric cointegration test developed by Bierens (1997) from 02/01/2000 to 31/08/2008 The evidences show that the US stock markets are not pairwise cointegrated with all the stock markets of its major trading partners with the exception of Mexican stock markets

The fourth content is finding the leading market amongst developed countries Shim

(1989) studies the international transmission of stock market movements by estimating a nine market vector autoregression (VAR) system (Australia, Canada, France, Germany, Hong Kong, Japan, Switzerland, the UK and US) from the period

of 1979 to 1985 Innovations in the US are rapidly transmitted to other markets in a clearly recognizable fashion, whereas no single foreign market can significantly explain the US market movements The evidences imply that US market is a leading worldwide trend

Gerrits (1999) studies the short and long term links amongst European (Germany,

UK and Netherlands) and US stock markets for period from 1990 to 1994 Results

of the tests show that the US exerts a significant impact on European markets and are a leading stock market of this group Moreover, the three European markets influence each other in the short and long run Therefore, diversification amongst these national stock markets will not greatly reduce the portfolio risk without sacrificing the expected return

David (2003) investigates the structure of interdependence in international stock markets (Australia, Canada, France, Germany, Hong Kong, Japan, Switzerland, the

UK and US) using the error correction model (EC) and directed acyclic graphs (DAG) The results are the US market is highly influenced by its own historical innovations and also influenced by market innovations from the UK, Switzerland, Hong Kong, France and Germany The US market is the only market that has a consistently strong impact on price movements in other major stock markets in the long run

Above group of four contents may not mention about the relationship amongststst Vietnam stock market and US and related Asian countries While Vietnam has joined WTO over four years; many economic operations have changed including the stock market exchange Many foreign investors join Vietnam stock market and number of investors of Vietnam stock market increase strongly Not so many researchers study those interrelationships before, during global financial crisis of 2007-2010 This thesis hopes to fill those gaps

III METHODOLOGY

This part of thesis presents the analytical framework which is applied to find the answers of the thesis objective The proxies and data used in this thesis are also mentioned in this content With the purpose of investigating the relationship amongst the stock markets in sample, we follow the other empirical researches in choosing the methodologies (Chan (1992); Subramanian (2008); Lim (2008); Ng (Ng 2010); V.X.Vinh (2009)) The Johansen cointegration test is used to analyse the long run comovement, the Granger causality to test the causality relationship, the test for converging trend to test for converging or diverging trend of a market index series, the variance decomposition analysis to describe the causality outside of the sample estimation

1 Johansen Cointegration test

In statistics, the Johansen test is a procedure for testing cointegration of several I(1) time series As the pioneering work by Engle and Granger (1987) reports that if the individual economic series are stationary only after differencing but a linear combination of their levels is stationary and the series are said to be cointegrated This test permits more than one cointegrating relationship so it is more generally applicable than the Engle–Granger test which is based on the Dickey–Fuller (or the augmented) test for unit roots in the residuals from a single (estimated) cointegrating relationship Johansen (1988) and Johansen and Juselius (1990) propose a maximum likeihood method for estimating long run equilibrium relationships or cointegrating vectors and derive likelihood ratio tests for cointegration There are two types of Johansen test: trace test or eigenvalue test The

null hypothesis for the trace test is the number of cointegration vectors r ≤ n, the null hypothesis for the eigenvalue test is r = n

The purpose of applying the cointegration test in this thesis is to find the cointegration (long run relationship) amongst stock markets in the studied sample Before conducting the cointegration test, unit root test need to be analyzed the stationary of time series variables

1.1 Unit root test

An econometric model of cointegration requires knowledge of stationary and order

of integration for the time series variables Condition for cointegration that all the time series to be analyzed is integrated of the same order or that all series contain a deterministic trend (Granger, 1986) Thus, prior to conducting the cointegration analysis of stock markets, it is necessary to examine the order of integration of each stock index There are some methods of testing for stationary such as visual plots of data; the autocorrelation function or unit root test The unit root test is the most widely used test for stationary For this reason, unit root test is applied in this studying Three common tests which employed to test for unit root in this research are Dickey-Fuller test (Dickey and Fuller, 1979; Fuller 1976), Augmented Dickey Fuller test (1979, 1981) and Phillips Perron test (1988) These three unit root tests are employed by several previous researches (V.X.Vinh (2009); David (2001)) The presence of a unit root reveals that the time series data is non stationary In order to make time series data stationary, it is differenced d times or integrated of order d (I(d)) Cointegration procedure requires the variables are stationary at the first difference I(1)

1.1.1 The Dickey-Fuller test (DF)

To apply the DF test, let consider a simple autoregression:

1

y =ρy− +δx +ε (1)

Where y is a non stationary time series ρand δ are parameters to be estimated x t

are optional exogenous regressors that may include a constant or a constant and

trend εt are sequence of independent normal random variables with mean zero and

a constant variance The time seriesy t is a non stationary and has unit root if the absolute value of ρequals one If the absolute value of ρless than one, the time seriesy t is a stationary Hence, the basis objective of unit root test is to test the null hypothesis thatρ=1 So we have:

1.1.2 The Augmented Dickey-Fuller test (ADF)

The DF test are only valid if εt are white noise If the dependent variables of the equation (2) which we do not model are autocorrelation, εt will be autocorrelated Said and Dickey (1984) augment the test using p lags of the dependent variable y to the right hand of the equation (2):

1.1.3 The Phillips-Perron test (PP)

Phillips and Perron develop a more comprehensive theory of unit root non stationary PP test is similar to ADF test but they associate an automatic correction

to the DF test procedure to allow for autocorrelated residuals

The PP test comes with trend and drift with below model:

t t

y =µ+β( − / 2 ) +α −1+ε (4)

β

µ, and α are the parameters to be estimated εt is error term

With t=1,2, ,N where N are number of observations to which regression equation (4) is fitted

If the time series data have unit root, the following hypothesis is tested:

Null hypothesis H0 : α=1

Alternative hypothesisH1: α<1

If the hypothesis is as mentioned above, the related test statistic for equation (4) is the adjusted t-statistic Z(tα) given in PP

1.2 Cointegration test (Johansen)

Suppose that a set of g variables (g≥2) are under consideration which are integrated

of order one I(1) They are also thought that may be cointegrated Let consider a vector autoregressive model (VAR) with ρ lags holding these variables could be set up below:

} {

There is an indispensable factor which strongly influences the test results are choosing the appropriate lag length applying these tests According to Brooks (2008), two ways of choosing appropriate lag length are implemented Those are base on the frequency of the using data and base on the decision on the information criterion Our data is daily data so it is considered high frequency data Hence the first way is not a suitable option in our thesis The second option is our choice in this case There are several different criteria which vary according to how stiff the penalty term is Brooks (2008) introduces three most popular information criteria

are Akaike’s (1974) information criterion (AIC), Schwarz’s (1978) Bayesian information criterion (SBIC) and the Hannan-Quinn Criterion (HQIC)

These are respectively expressed as:

In this thesis we apply the SBIC to determine the optimal lag length as SBIC embodies a much stiffer penalty than AIC, while HQIC is somewhere in between

2 Granger causality

We use cointegration test to analyze the cointegration relationship amongst time series variables However, the results from cointegration test will not probably express the causal dynamic relationship amongst the variables Granger introduced Granger causality test (1969, 1980 and 1988) It is considered one of the important issue that has been studied in empirical macroeconomics and empirical finance issue Granger causality analysis is adopted in order to investigate the existence of causal dynamic relationships between the studied variables

Granger causality theory presents test in a bivariate system of I(1) time series x and

y With using F-tests to test whether lagged information on a variable y provides any statistically significant information about a variable x in the presence of lagged

x If lagged x can improves the prediction and estimation of y, then x is said to Granger cause y If variable x causes (Granger cause) variable y, then changes in x

shall precede changes in y A similar definition applies if y causes (Granger cause)

x It is emphasized a note that when we say x causes (Granger cause) y, it does not imply that y is the effect or the result of x

With the purpose of testing the Granger causality test across two variables xt and yt,

we run the bivariate regression with a lag length set as k for all possible pairs (x,y)

On the assumption that all returns are stationary, the equations for pairs Granger causality test are given by:

Where xt and yt are daily returns for stock markets of variable x and y respectively

at time t εt and ut are random disturbances with zero means and finite variances Unrestricted vector autoregression (VAR) is applied to equation (7) and (8)

The Granger causality test is examined by testing the null hypothesis that returns on

x do not Grange cause returns on y is obtained using a Wald test for joint significance of each of the lagged returns on x in equation (7) or all βl are equal to zero

regression without the lags of yt RSSfull is the residual sum of square of the full model

y is average of market index for

n countries in the sample

It is assumed that W changes according to the process below in each period:

If Ψ<1, the market index in country i convergence from the sample group If Ψ>1, the market index in country i divergence from the sample group

4 Variance decomposition analysis

Granger causality provides the causality relation within the sample period Hence the variance decomposition analysis will be applied to describe the causality relation outside of the sample estimation

According to Brooks (2008), the variance decomposition offers a slightly different method for examining VAR system dynamics They give the proportion of the

movements in the dependent variable that are due to their own shocks, versus shocks to the other variables A shock happening to a variable is not only affect that variable but may also be transmitted to all of the other variables in the system through the dynamic structure of the VAR Variance decomposition test determines how much of s-step-ahead forecast error variance of a given variable is explained by innovations to each explanatory variable for s=1, 2,3…

We apply the formula of Sheng (2000) to estimate the forecast Xt+n the n period forecast error is below:

IV DATA AND DATA DESCRIPTION

1 Data

A research on international interrelationship of equity markets chooses a proxy depending on the research objectives and the econometric theory The research on long and short term interrelationship amongst international stock markets often employ the closing stock market indices for the respective stock markets (Allen(1995)) In addition, these proxies are normally non stationary level and non stationary series is a first condition for using the cointegration test

The sample data indices include VNI, SSE, S&P, DJI, NIK, STI and HIS which are collected from the Finance Yahoo’s data base and S&P index data services Above indices are used for the respective stock markets: VNI (Vietnam index) for the Vietnam stock market, SSE (Shanghai Stock Exchange index) for the Chinese stock market, S&P (Standard and Poor’s 500 index) and DJI (Dow Jones index) for the

US stock market, NIK (The Nikkei Stock Average index) for the Japanese index,

STI (Straits Times Index) for the Singapore stock market and HSI (Hang Seng Index) for the Hong Kong stock market The reason of above choice of respective

indices is that they are widely used in previous studies The US stock market is chosen because it is the world’s largest stock market The choice of Japanese stock market is due to it is the world’s second largest stock market while China, Hong Kong and Singapore are in the same region of Vietnam which is the world’s large growing emerging economy

This studying uses the data of daily closing indices (five trading days a week) Daily data are selected instead of weekly or monthly data because the weekly or monthly data’s time interval might be so large that do not permit the underpinning

of interrelations which reason out within one or only a few days (Ng (2010);

Glezakos (2007 )) Some previous authors use the daily data in order to update the dynamic of international transmission (Michael Tucker (1996); Marimuthu (2010))

Figure 4.1: Movement of daily closing price of stock index from Jan 1st 2005 to Jun

30th 2010

Examinations of the figure 4.1 reveal that all indices have similar trend: up-trend in 2005-2007 (leading to the positive average daily return); downward trend in 2008- first haft of 2009 (leading to the negative average daily return) and up-trend in last haft of 2009-2010 (leading to the positive average daily return) The closing price of HSI, NIK, DJI, SSE are far more fluctuant than it of S&P, VNI and STI HSI is the

most varying while VNI is the least one

The whole sample period is from January 1st 2005 to June 30th 2010 In order to examine the effects for the global financial crisis from the year 2007, this sample data is divided into two sub periods This first period is pre-crisis (from Jan 1st 2005

to July 22, 2007); the second period is crisis (from July 23, 2007 to Jun 30th, 2010) The date for the during crisis period is followed the US subprime mortgage crisis

DJI

started on July 23, 2007 (Adwin (2009); Yellen (2008); Dungey Mardi (2008)) Whole studied data includes more than 1,300 daily closing price observations of each stock market After matching the daily data amongst stock markets for co-integration test performance, the remainders of daily data are about 1,100 daily observations It means that over 200 daily observations are taken out of original data for different none trading days and notional holidays of these stock markets during the studied period In addition, time differences are also a vital factor to our analysis The trading time of the daily data of price indices is not the same time For this reason, the research will be designed that Asian stock price and return at time t will be run tests with the US’s daily stock price and return at time t-1

All seven indices are analyzed in daily price and return The daily returns of each stock market are gained from logarithmic differences of daily market indices over the entire sample period and sub periods The indices are presented in local currencies The rationale for this presentation is to evade the problems concerned with currency transfer since the exchange rate fluctuation in cross countries and avoid the confounding effect of the regional wide currency devaluation after the occurrence of the crisis (Yang, (2005)) All estimation results are derived using the Eviews 6.0 software

According to Dwyer (1992), the dividends are not dispensable for impacting on the null hypothesis of the research that no co-integration amongst the mentioned stock markets Therefore dividends are not careful consideration for this examination Whole data are daily closing price of end of trading day without adjustments

2 Data descriptive statistics

2.1 Summary statistics

The data descriptive statistic provides the initial information about the nature and volatility of the stock indices It also helps observation of how they fare against

each other and a comparison of the basis performance indicators of the stock indices

A table 4.1 is the summary of basis statistics of the daily stock returns in whole period A table 4.2 is summary of basis statistics of the daily stock returns in pre-crisis period, and table 4.3 is summary of basis statistics of the daily stock returns in the crisis period respectively Statistic items shown in these summary statistics are mean, median, maximum, minimum, standard deviation, skewness, kurtosis and Jarque-Bera test

Table 4.1: Summary statistics of the stock returns (Whole period)

Mean 0.000479 -2.18E-05 9.38E-05 -0.000323 -0.000464 0.000295 -0.000292 Median 0.000756 0.000678 0.000549 0.000836 0.000146 0.000799 0.00041 Maximum 0.077505 0.075305 0.090343 0.102457 0.094941 0.134068 0.103259 Minimum -0.049801 -0.08696 -0.092562 -0.094695 -0.12111 -0.13582 -0.082005 Std Dev 0.018292 0.013705 0.019593 0.014574 0.016859 0.018328 0.013479 Skewness -0.10833 -0.384935 -0.249449 -0.555004 -0.946628 0.049783 -0.298714 Kurtosis 3.530333 8.490096 5.776303 11.83843 10.9322 12.45058 11.99076

A normal distribution is considered to have a zero skewness and kurtosis of three The value from above table shows that all daily returns of indices are not normal distribution Specifically all daily returns have different zero skewness and almost negative values except for HSI The skewness of VNI is less skewed as compared to that of others The reason can be explained for that is daily price limit changed from seven percent to five percent over the period These negative skewness values indicate a long left tail The values of kurtosis are also different with three Most of kurtosis values are positive and quite higher than three in this period Positive and exceed three values of kurtosis imply a fat tail distribution The significant probability values of Jarque-Bera test once again confirm daily returns have non normal distribution

Table 4.2: Summary statistics of the stock returns (Pre-crisis period)

Mean 0.001789 0.000772 0.001 0.000273 0.000415 0.000695 0.000267 Median 0.000811 0.001166 0.000706 0.000864 0.000428 0.001034 0.000415

Maximum 0.077505 0.030573 0.078903 0.021336 0.03522 0.026567 0.020691 Minimum -0.049801 -0.040367 -0.092562 -0.035343 -0.042304 -0.040793 -0.033488 Std Dev 0.016522 0.008434 0.016419 0.00671 0.010527 0.00878 0.006556 Skewness -0.025825 -0.766934 -0.746109 -0.34611 -0.279356 -0.510736 -0.342649 Kurtosis 4.761022 5.905579 8.048134 4.746291 4.30143 4.715569 4.698732

slow VNI (0.016522) and DIJ (0.006556) continuously keep a most and lowest volatility Generally, the stock markets in developing countries are more volatile than those in the developed countries Hence, the conventional wisdom in finance of

“the higher risk, higher return” is supported by this results All stock markets have negative skewness Most kurtosis values in pre-crisis period are lower than entire period But they are still positive value

Table 4.3: Summary statistics of the stock returns (Crisis period)

Mean -0.000707 -0.000736 -0.000796 -0.000874 -0.001246 -7.43E-05 -0.000812 Median 0.00041 -0.000298 0.000328 0.000738 -0.000233 0 0.000387 Maximum 0.04653 0.075305 0.090343 0.102457 0.094941 0.134068 0.103259 Minimum -0.048157 -0.08696 -0.074624 -0.094695 -0.12111 -0.13582 -0.082005 Std Dev 0.01972 0.01713 0.022027 0.019096 0.021009 0.023919 0.017543 Skewness -0.09833 -0.199479 -0.004742 -0.382377 -0.820863 0.106006 -0.166203 Kurtosis 3.812884 6.311301 4.680412 7.574971 8.303522 8.145164 7.891023

2.2 Correlation

The correlation coefficient is mainly applied to measure whether and how strongly studied variables are integrated By using the historical data of daily return, correlation will help us to examine the short-run relationship amongst indices

Table 4.4, 4.5, 4.6 list the correlation coefficients amongst seven indices in six stock markets in whole period, pre crisis and crisis period DJI and S&P almost have relatively correlated because they are in same stocks market of US So we will not mention about this correlation coefficient in this studying

Table 4.4: Correlation matrix of average daily return (Whole period)

the correlation between return should be negative to ensure that some markets will

go up if some go down

Table 4.5: Correlation matrix of average daily return (Pre-crisis period)

Table 4.6: Correlation matrix of average daily return (Crisis period)

All daily returns are positive in the crisis period except for the correlation between

US and Vietnam stock market (VNI and S&P: -0.00818; VNI and DJI: -0.00914) or

Vietnam stock market are still different trend with the US stock market in financial crisis The others also have correlation increasing STI, NIK and HSI continuously have high correlation We can see positive correlations in table 4.6 Group three indices of STI, NIK and HSI are always kept a leading position in high correlated entire period and each sub periods Conversely, three indices of VNI, S&P and DJI are low correlation during the studied period It can be concluded that seven daily returns are not strong correlation