Return and volatility spillovers vietnamese and some asian markets

Purpose - This thesis investigates the interdependence between the Vietnamese stock market and other nine Asian markets in terms of return and volatilityspillovers during three periods:

UNIVERSITY OF ECONOMICS HOCHIMINH CITY

HO CHI MINH CITY, 2012

Foremost, I would like to express my sincere gratitude to my advisor Dr VõXuân Vinh for the continuous support of my thesis, for his patience, motivation,enthusiasm, and immense knowledge His guidance helped me in all the time ofresearch and writing of this thesis

I would like to thank professors at Faculty of Business Administration andPostgraduate Faculty, University of Economics Ho Chi Minh City for theirteaching, their guidance and support during my MBA course

I wish to thank my family for the love, support and constant encouragement Ihave got over the years

Purpose - This thesis investigates the interdependence between the Vietnamese

stock market and other nine Asian markets in terms of return and volatilityspillovers during three periods: pre-crisis, crisis and post-crisis

Methodology - Long run and short run integration are examined through

Johansen cointegration and Granger causality test respectively Vectorautoregressive model is used to estimate the conditional return spillover amongthese indices Volatility spillover is studied through BEKK and AR-GARCHModel

Findings - We find evidence of the integration of Vietnamese market with

statically significant correlation, cointegration, return spillover and volatilityspillover with other markets The crisis has strong impacts to marketinterdependence with higher correlation, cointegration and spillovers In thecurrent period, there may be long run benefits from portfolio diversification toVietnamese stocks

Originality/Value - The thesis points out the return and volatility between

Vietnamese stock market and other nine Asian Markets and suggests potentialbenefits from diversification

Key words - Return spillover, Volatility spillover, VAR, BEKK, VAR-GARCH,

Cointegration, Granger causality

Acknowledgement

Abstract

Contents

List of Figures

List of Tables

Chapter 1 1.1 Background

1.2 Purpose and scope

1.3 Basic definition

1.3.1 1.3.2 1.3.3 1.3.4 1.3.5 1.3.6 1.3.7 1.3.8 1.4 Research questions

1.5 Structure

Chapter 2 Chapter 3 3.1 Data

3.2 The model and methods

3.2.1 3.2.2 3.2.3 3.2.4 3.2.5 3.2.6 3.2.7 Chapter 4 4.1 Descriptive statistics and correlation matrix

4.1.1

List of Figures

Figure 1 Index timings by UTC Time 22Figure 2 Index closing price 51Figure 3 Index return 52

List of Tables

Table 1 Indices and their origination 2

Table 2 Descriptive statistics of Indices in pre-crisis period 23

Table 3 Descriptive statistics of Indices in crisis period 23

Table 4 Descriptive statistics of Indices in post-crisis period 23

Table 5 Descriptive statistics of Indices’ return in pre-crisis period 24

Table 6 Descriptive statistics of Indices’ return in crisis period 24

Table 7 Descriptive statistics of Indices’ return in post-crisis period 24

Table 8 Correlation Matrix between Indices' returns in pre-crisis period 25

Table 9.Correlation Matrix between Indices' returns in crisis period 26

Table 10.Correlation Matrix between Indices’ returns in post-crisis period 26

Table 11 Unit root test result on Indices 27

Table 12 Unit root test results on Indices' return 27

Table 13 Johansen's cointegration test for pre-crisis period 30

Table 14 Johansen's cointegration test for crisis period 30

Table 15.Johansen's cointegration test for post-crisis period 31

Table 16 Granger causality test results for pre-crisis period 33

Table 17 Granger causality test results for crisis period 33

Table 18 Granger causality test results for post-crisis period 34

Table 19 Bivariate VAR Model (VNIndex and other Indices) estimates of model on indices return in pre-crisis period 37

Table 20 Bivariate VAR Model (VNIndex and other Indices) estimates of model on indices return in crisis period 38

Table 21 Bivariate VAR Model (VNIndex and other Indices) estimates of model on indices return in post-crisis period 39

Table 22 Parameters estimates of BEKK model for pre-crisis period 42

Table 23 Parameters estimates of BEKK model for crisis period 42

Table 24 Parameters estimates of BEKK model for post-crisis period 43

Table 25 Volatility spillover estimates of AR(1) GARCH(1,1) model for pre-crisis period 46

Table 26 Volatility spillover estimates of AR(1) GARCH(1,1) model for crisis period 47

Table 27 Volatility spillover estimates of AR(1) GARCH(1,1) model for post-crisis period 48

Chapter 1 Introduction

1.1 Background

Currently, the globalization of domestic market becomes an evident trend Theequity markets attract capital not only from domestic but also from internationalinvestors who expect to reduce the risk via diversification This trend wouldreduce the isolation of domestics markets and the markets can react quickly tointernational news and shocks

The information transmission across market has been widely studied in twodifferent faces First, the long term interdependence and causality among marketsare considered as strong signal of information transmission And secondly, thevolatility transmission across markets gets more studies these days because itbecomes important as a good measure of the risk of internationally diversifiedportfolio which very helpful in deciding the asset diversification strategy

Vietnamese stock market was formed a decade ago and now attracts valuableinvestment However, there have been relatively few studies on the linkages ofVietnamese equity market with international markets, especially the Asianmarkets

1.2 Purpose and scope

This study attempts to investigate interactions in terms of price and volatilityspillover amongst Vietnamese equity market and other nine Asian markets (India,Hong Kong, Indonesia, Malaysia, Japan, Philippines, China, Singapore andTaiwan)

The return spillovers are examined with Johansen co-integration (for long termspillovers) and Granger causality test (for short term spillovers) Meanwhile, thebivariate BEKK and AR-GARCH model is used to evaluate the volatilityspillovers

Both the return spillovers and volatility spillovers are considered through threeperiods: the pre-crisis period (from 03rd January 2005to 31st December 2007), thecrisis period (from 01st January 2008 to 30th June 2010) and the post-crisis period(from 1st July 2010 to 31st August 2012) The evaluation based on these threeperiods would indicate the effect of financial crisis to the return and volatilityspillovers between Vietnamese stock market and other nine Asian markets.

The markets are presented by their Indices as following:

Table 1 Indices and their origination

- Hong Kong and Japan are regarded as one of the mature financial centers

in Asia and play important role in the regional economy with hightransaction volume and high influences to other markets

- China is the fastest developing economy in the world and gains strongerposition today in financial market; furthermore Vietnam shares sameborder with China and the trade among Vietnam and China gets large

portion of the Vietnamese international trading, so we expect information transmission among China and Vietnam.

- Other markets (Indonesia, Malaysia, Philippines and Singapore) are in thesame ASEAN (Association of Southeast Asian Nations) organization asVietnam ASEAN is the ninth largest economy in the world and isgrowing with more and proven integration between its members

1.3 Basic definition

1.3.1 Stock index

A stock index or stock market index is a method of measuring the value of asection of the stock market It is computed from the prices of selected stocks(sometimes a weighted average) It is a tool used by investors and financialmanagers to describe the market, and to compare the return on specificinvestments

1.3.2 Return

Most financial studies involve returns, instead of prices, of assets Campbell et al.(1996) give two main reasons for using returns First, for average investors,return of an asset is a complete and scale-free summary of the investmentopportunity Second, return series are easier to handle than price series becausethe former have more attractive statistical properties

There are several definitions of an asset return, and in this thesis, we use theword ‘return’ in means of continuously compounded return

Continuously compounded return

The natural logarithm of the simple gross return of an asset is called thecontinuously compounded return or log return:

=

where is the price/index value at time t, and is the log return.

1.3.3 Volatility

Volatility is a statistical measure of the dispersion of returns for a given security

or market index Volatility can either be measured by using the standard deviation

or variance between returns from that same security or market index Commonly,the higher the volatility, the riskier the security

1.3.4 Return spillover

The return spillover is considered in the sense that the return of one index mayhave some impact on the return of other index, in the term that it would makeother index’s return increase or decrease

1.3.5 Volatility spillover

The volatility spillover is considered in the sense that the volatility of one index’sreturn may have some impact on the volatility of other index’s return, in the termthat it would increase or decrease the volatility of targeted index’s return

1.3.6 Time series

Time series is a sequence of data points, measured typically at successive timeinstants spaced at uniform time intervals In this thesis, the daily indices’ closingand theirs daily returns are considered as time series

Time series analysis comprises methods for analyzing time series data in order toextract meaningful statistics and other characteristics of the data This studyexplores the time series analysis to answer the proposed questions in this section.However with the time series analysis we confront some problems, for examplethe unit root Unit root can cause problems in statistical inference if it is notadequately dealt with Often, ordinary least squares (OLS) is used to estimate theslope coefficients of the auto-regressive model Use of OLS relies on thestochastic process being stationary When the stochastic process is non-stationary

or has the unit root, the use of OLS can produce invalid estimates.Granger &

and high t-ratios yielding results with no economic meaning

The Granger causality test (Granger (1969; Granger (1988)) is a statistical

another A time series X is said to Granger-cause Y if it can be shown, usuallythrough a series of t-tests and F-tests on lagged values of X (and with laggedvalues of Y also included), that those X values provide statistically significantinformation about future values of Y

We discuss in details the Granger causality test in chapter three

H1: There is no return spillover between Vietnam and other markets

The second research question is answered with the following null hypothesis analternative hypothesis:

H0: There is volatility spillover between Vietnam and other markets

H1: There is no volatility spillover between Vietnam and other markets

In order to assess how the spillovers response to the financial crisis, we study theresearch questions through three time frames as earlier discussed

1.5 Structure

The rest of this thesis is organized as follows Chapter two gives a brief andcritical review of existing literature review relevant with this thesis Chapter threecontinues with the comprehensive description of the methodology applied in thestudy The results are presented and discussed in chapter four, and finally thestudy is concluded in chapter five

Chapter 2 Literature review

The market integration in terms of price spillover between equity markets hasbeen widely studied Grubel (1968) studied the co-movement and correlationbetween different markets from a US perspective for gains of internationaldiversification Eun & Shim (1989) investigated the international transmissionmechanism of stock market movements and found a substantial amount of multi-lateral interaction among national stock markets King & Wadhwani (1990)

constructed a model in which "contagion" between markets occurs as a result ofattempts by rational agents to infer information from price changes in othermarkets The authors offered supporting evidence for contagion effects using twodifferent sources of data Jon (2003) provided evidence of transmission ofinformation from the U.S and Japan to Korean and Thai equity markets duringthe period from 1995 through 2000 Berben & Jansen (2001) investigated theshifts in correlation patterns among international equity returns at the marketlevel as well as the industry level from Germany, Japan, the UK and the US in theperiod 1980-2000

The volatility spillovers also gained focus of various authors Hamao, Masulis &

London to Tokyo, and New York to London was observed but no price volatilityspillover effects in other directions is found for the pre-October 1987 period

stocks traded on the New York and Toronto stock exchanges with a multivariateGARCH model The main finding of the study was that inferences about themagnitude and persistence of return innovations that originate in either marketand that transmit to the other market depend importantly on how the cross-marketdynamics in volatility are modeled

countries and found that the correlation between the conditional variances ofmajor equity markets has increased substantially over the last two decades Baele

(2003) quantified the magnitude and time-varying nature of volatility spilloversfrom the aggregate European (EU) and US market to 13 local European equitymarkets.

In Asian markets context, Johnson & Soenen (2002) investigated the integration

of 12 equity markets in Asia and found that the equity markets of Australia,China, Hong Kong, Malaysia, New Zealand, and Singapore are highly integratedwith the stock market in Japan and that these Asian markets became moreintegrated over time, especially since 1994 Tatsuyoshi (2003) examined themagnitude of return and volatility spillovers from Japan and the US to sevenAsian equity markets Author found that only the influence of the US is importantfor Asian market returns; there is no influence from Japan; but the volatility ofthe Asian market is influenced more by the Japanese market than by the US; andthere exists an adverse influence of volatility from the Asian market to theJapanese market

across 15 North American, European and Asian stock markets using a VARmodel for the returns and an AR-GARCH for volatility They concluded that thedirection of both return and volatility spillover was primarily from the US market

to Japanese and Korean markets, then to Singapore and Taiwan, and then to HongKong and Europe before returning to the US They also reported that theJapanese, Korean, Singapore, and Hong Kong markets were the markets with thegreatest influencing power within the Asian markets

positive mean and volatility spillovers between three developed markets (HongKong, Japan and Singapore) and six emerging markets (Indonesia, Korea,Malaysia, Philippines, Taiwan and Thailand) Nevertheless, mean spillovers fromthe developed to the emerging markets are not homogeneous across the emergingmarkets, and own-volatility spillovers are generally higher than cross-volatilityspillovers for all markets, but especially for the emerging markets Gamini &

Lakshmi (2004) pointed a high degree of volatility co-movement betweenSingapore, US, UK and Hong Kong market.

East Asian markets using the VAR-BEKK The results showed that theinterdependence of equity market conditional variances was high; the Japanesemarket is the most influential in transmitting volatility to the other East Asianmarkets Lee (2009) used [VAR(p)-GARCH(1,1)] model to examine the volatilityspillover effects among six Asian country stock markets (India, Hong Kong,South Korea, Japan, Singapore and Taiwan) and found the statistically significantvolatility spillover effects within the stock markets of these countries

three Asian stock exchange markets including India, Singapore and Hong Kong,during the period July 1997 to October 2005 The empirical analysis showed thatthe markets exhibit a strong GARCH affect and are highly integrated reacting toinformation which influences not only the mean returns but their volatility aswell Giampiero & Edoardo (2008) explored the transmission mechanisms ofvolatility between markets using the Markov Switching bi-variate model Theresults show plausible market characterizations over the long run with a spilloverfrom Hong Kong to Korea and Thailand, interdependence with Malaysia and co-movement with Singapore

Other authors including Jang & Sul (2002), In et al (2001), Yilmaz (2010),

financial crisis periods

market integration across selected stock markets during the Asian financial crisisperiods 1997 and 1998 with the VAR-EGARCH model The authors stated thatHong Kong played a significant role in volatility transmission to the other Asianmarkets The data also indicated market integration in that each market reacted to

both local news and news originating in the other markets, particularly adversenews.

FTSE 100 stock market indices to trace the spillover of returns and volatilitybetween these three major world stock market indices before, during and after the

2008 financial crisis Authors found that the depth of market integration changedsignificantly between the pre-crisis period and the crisis and post- crisis period

by studying the dynamics of correlation between eleven Asian and six LatinAmerican stock markets vis-a-vis the US stock market There was evidence ofcontagion from the US stock market to markets in the two regions during theglobal financial turmoil The magnitude of the contagion effect to both regions inthe global financial crisis is very similar, albeit their different economic, politicaland institutional characteristics

returns and their volatility, focusing on the Asian and global financial crises of1997-98 and 2008-09 for Australia, Singapore, the UK, and the USusingMGARCH model The research could not find any significant impact on returnsarising from the Asian crisis and more recent global financial crises across thesefour markets However, both crises significantly increased the stock returnvolatilities across all of the four markets

East Asian equity markets since early 1990s and compares the ongoing crisiswith earlier episodes The study shows that there is substantial differencebetween the behavior of the East Asian return and volatility spillover indices overtime While the return spillover index reveals increased integration among theEast Asian equity markets, the volatility spillover index experiences significantbursts during major market crises, including the East Asian crisis The fact thatboth return and volatility spillover indices reached their respective peaks

during the current global financial crisis attests to the severity of the currentepisode.

spillovers between the Chinese and world equity markets It was found that the

US market had dominant volatility impacts on other markets during the subprimemortgage crisis The volatility interactions among the markets of China, HongKong, and Taiwan were more prominent than those among the Chinese, Western,and other Asian markets were

Korea and Singapore markets to the Chinese markets in the pre-crisis period ofthe financial crisis in 2008; and found strong volatility linkages between theChinese market and the other Asian markets and that the Chinese stock markethas become an important information source among Asian emerging

However, there is not much study on the price and volatility spillover within theVietnamese market context The aim and the main contribution of this study are

to fill up this gap

- Long-run integration is tested through Johansen co-integration techniques.

If two or more series are found to be-cointegrating, then they are said tohave common stochastic trends They tend to move together in the longrun but may divergence in short run

- Short-run dynamics is examined through Granger causality test and Vectorautoregressive model Because Granger causality test does not explainwhether there is a positive or negative relation between endogenousvariable, if any; this is investigated through VAR model The returnspillover is also tested via VAR model

- BEKK model and AR-GARCH model and are applied to investigate volatility spillover.

3.2.2 Unit root and stationary test

ADF method (Dickey & Fuller (1979)) is widely used for the unit root andstationary test in financial time series

hypothesis H0: β = 1 versus the alternative hypothesis H1: β <1 using the regression

−1

=1

where is a deterministic function of the time index t

In practice, can be zero or a constant or = 0 + 1 .

The ADF test value is calculated as:

− = −1

( )

Dickey–Fuller (ADF) unit-root test

Hypothesis for the ADF Test

- H0: the series has unit root (β = 1)

- H1: the series has no unit root (β <1)

To decide the result, we use the following rules:

- If ( ADF value> ADF critical value )

 we fail reject the null hypothesis,

 the unit root exist

- If ( ADF value < ADF critical value )

 we reject the null hypothesis,

 There is no unit root in the time series

3.2.3 Johansen’s cointegration techniques

The Johansen multivariate cointegration procedure (Johansen (1988)) is widelyused to perform the co-integration analysis If the two or more series are found to

be co-integrating, then they are said to have common stochastic trend: they tend

to move together in the long run but may divergence in short run

Consider a k-dimensional VAR (p) time serieswith possible time trend so that themodel is

where the innovation is assumed to be Gaussian and = 0 + 1 , where 0 and 1 are k-dimensional constant vectors.

An error correction model (ECM) for the VAR(p) process is

∆ = + Π −1 + Φ 1∗Δ −1 + ⋯ + Φ∗−1 Δ − +1 +

With П = αβ ′ = −Φ(1) Three cases are of interest in considering the ECM

1. If( ) = 0 This implies is not cointegrated.

3 If 0< ( ) = m < This implies is cointegrated with m linearly independent cointegrating vectors and has k - m unit roots that give k - m common stochastic trends

of

Two likelihood ratio tests are used to test the long run relationship: the trace test and the maximum eigenvalue test

The trace test

Consider the hypothesis

H0: Rank( ) = m or there is m cointegrating vector, versus

H1: Rank( ) > , or there is more than m cointegrating vector

perform the test

()=−(−)�

= +1

where T is the number of observations

λ is the eigenvalues

To decide the result, we use the following rules:

- If Trace Value < Critical value :

 we fail to reject the null hypothesis H0

 there is m cointegrating vector

- If Trace Value > Critical value :

 we can reject the null hypothesis H0

 there is more than m cointegrating vector

The maximum eigenvalue test

Consider the hypothesis

H0: Rank( ) = m or there is m cointegrating vector, versus

H1: Rank( ) = m + 1 or there is m+1 cointegrating vector

The LR ratio test statistic, called the maximum eigenvalue statistic, is

()=−(−)�

= +1

If n-1 vector out of n vectors are found cointegrated (having a common stochastictrend), then all n vector are called “cointegrated in long run or represents longrun equilibrium”

To decide the result, we use the following rules:

- If max eigenvalue < Critical value :

 we fail to reject the null hypothesis H0

 there is m cointegrating vector

- If max eigenvalue > Critical value :

 we can reject the null hypothesis H0

 there is m + 1cointegrating vector

3.2.4 Granger causality analysis

Short run interrelationship is examined through Granger Causality test (Granger

X causes Y is to see how much of the current Y can be explained by past values

of Y and then to see whether adding lagged values of X can improve theexplanation

Y is said to be Granger-caused by X if X helps in the prediction of Y, orequivalently if the coefficients on the lagged X’s are statistically significant.Two-way causation is frequently the case; X Granger causes Y and Y Grangercauses X.

It is important to note that the statement “Granger causes” does not imply that isthe effect or the result of Granger causality measures precedence andinformation content but does not by itself indicate causality in the more commonuse of the term

Let y and x be stationary time series To test the null hypothesis that x does notGranger-cause y, one first finds the proper lagged values of y to include in a uni-variate auto regression of y:

- If F-statistics value < Critical value :

 we fail to reject the null hypothesis Ho

 the X does not Granger cause Y

- If F-statistics value > Critical value :

 we can reject the null hypothesis Ho

 the X Granger cause Y

3.2.5 VAR Model

The vector auto regression (VAR) is commonly used for forecasting systems ofinterrelated time series and for analyzing the dynamic impact of randomdisturbances on the system of variables The VAR approach sidesteps the needfor structural modeling by treating every endogenous variable in the system as afunction of the lagged values of all of the endogenous variables in the system

The mathematical representation of a VAR(p) is:

where is a k vector of endogenous variables,

is a d vector of exogenous variables,

p is the number of lag,

1 , … , and B are matrices of coefficients, and is a vector of innovation.

3.2.6 Bivariate BEKK Model

The VAR (k)-BEKK (1, 1) model to estimate volatility spillover is explainedbelow

Let endogenous is Nx1 vector with the mean equation:

Where and are kxk parameter matrix

Based on the symmetric parameterization of the model, is almost surely positive definitive provided that A’A is positive definitive The BEKK (p,q) with

The BEKK (p=1, q=1) with 10 variables requires 255 parameters and theoptimization will be tedious So, we use the bivariate BEKK (1, 1) whichrequires only 11 parameters to examine the volatility spillovers between twomarkets The method to handle the volatility spillover between more than twomarkets is discussed in section 3.2.7

The bivariate VAR (k) BEKK(1, 1) model can be written as

0

Or more simpler way

Where ℎ 11 , ℎ 12 are the conditional variances of market 1 and 2 respectively.

ℎ 12 is the conditional covariance of market 1 and 2.

indicates the spillover from market 1 to market 2 Hence, the statistical significance of these parameters tells about the volatility spillover among the two markets.

3.2.7 GARCH Model

We employ the two-stage GARCH approach to examine the volatility spilloveracross all indices In this method we also considered the same day effect andestimated the partial coefficient of the parameters

First stage: in this stage we fit the AR (1) - GARCH (1, 1) model to each index

and obtain the residuals from the mean equations

, −1+

where | −1 ~ (0, 2) and

2 = 0 + ∗ 2−1 + ∗ 2−1

is the error or unexpected return of the jth index,

Second stage: the residuals are then used in the GARCH equation of the other

indices as follows

2 =

where k: number of the indices open/close before the jth index

l: number of the indices open/close after jth index

The coefficients , is the volatility spillover from market k and l respectively tothe market j Hence the values of these coefficients and theirs statisticalsignificance provide useful information about the volatility spillovers

Consider the GARCH variance equation of VNIndex: three indices Nikkei, SSE,TWII open/close before VNIndex and we expect there are volatility spilloverswithin the same day; so the same day residuals of these three indices are used inthe GARCH equation Meanwhile; other six indices BES, HIS, JKSE, KLSE,PSE, STI open/close after the VNIndex; the volatility spillovers, if any, wouldhappen in the next day so the one lag day residuals of these six indices are used

in the equation

Chapter 4 Data Description, Results

and Analysis of Results

Chapter four will report the results and provide an analysis of the results based onthe methodologies we present in the previous chapter We use Eviews and RATSapplication to generate the results, and the details analysis are also discussed herewith main findings

4.1 Descriptive statistics and correlation matrix

The brief descriptive statistics of indices and returns are described as followings:

4.1.1 Opening and closing time of Indices

The opening and closing timing of the indices by UTC time are presented inFigure 1 This information is necessary to determine which index would havepotential effect on other indices in the same day or within next day For example,the Nikkei index opens and closes before the VNIndex, so the former indexwould have potential effect to VNIndex in the same day; and the VNIndex mayhave effect to Nikkei, if any, after one day lag

Figure 1 Index timings by UTC Time

4.1.2 Descriptive statistics of Indices

Table 2, 3 and 4 present the descriptive statistics of the studied indices Allskewness and kurtosis exhibit larger values and the J-B test statistics is highlysignificant at the l% level (expect the VNIndex in the post crisis period),indicating the distribution of all the prices is not distributed normally

Table 2 Descriptive statistics of Indices in pre-crisis period

4.1.3 Descriptive statistics of Indices’ return

Table 5, 6 and 7 present the descriptive statistics of the studied indices returns

All skewness and kurtosis exhibit larger values and the J-B test statistics is highly

significant at the l% level (expect the VNIndex in the crisis period), indicating

the distribution of all returns is not distributed normally

Table 5 Descriptive statistics of Indices’ return in pre-crisis period

The crisis has negative impact on the return: during the crisis period almost all

the indices have zero or negative mean return except JKSE