Introduction
Background
The globalization of domestic markets is increasingly evident, as equity markets draw capital from both local and international investors seeking to mitigate risk through diversification This trend diminishes the isolation of domestic markets, enabling them to respond swiftly to global news and economic shocks.
Research on information transmission across markets has primarily focused on two aspects: the long-term interdependence and causality among markets, which signal strong information transmission, and the increasing study of volatility transmission The latter has gained importance as it serves as a key measure of risk for internationally diversified portfolios, aiding in the formulation of effective asset diversification strategies.
The Vietnamese stock market, established a decade ago, has increasingly become a focal point for valuable investments Despite its growth, there is a notable lack of research examining the connections between the Vietnamese equity market and international markets, particularly within the Asian region.
Purpose and scope
This study explores the interactions of price and volatility spillover between the Vietnamese equity market and nine other Asian markets, including India, Hong Kong, Indonesia, Malaysia, Japan, the Philippines, China, Singapore, and Taiwan.
The study analyzes return spillovers using the Johansen co-integration method for long-term relationships and the Granger causality test for short-term dynamics Additionally, the bivariate BEKK and AR-GARCH models are employed to assess volatility spillovers effectively.
This study analyzes return and volatility spillovers across three distinct periods: the pre-crisis period (January 3, 2005, to December 31, 2007), the crisis period (January 1, 2008, to June 30, 2010), and the post-crisis period (July 1, 2010, to August 31, 2012) By evaluating these periods, the research aims to assess the impact of the financial crisis on the return and volatility spillovers between the Vietnamese stock market and nine other Asian markets.
The markets are presented by their Indices as following:
Table 1 Indices and their origination
BSESN BSE Sensex Index India
HIS Hang Seng Index Hong Kong
JKSE Jakarta Composite Index Indonesia
KLSE FTSE Bursa Malaysia Malaysia
PSEI Philippines Stock Exchange PSEi index Philippines
SSE SSE Composite Index China
STI Straights Times Index Singapore
TWII TSEC weighted index Taiwan
These selected markets encompass both developed and emerging economies within Asia, significantly influencing the Vietnamese stock market Additionally, the chosen indices are recognized as widely accepted benchmarks.
Hong Kong and Japan are recognized as leading financial centers in Asia, significantly contributing to the regional economy through their high transaction volumes and substantial influence on other markets.
China is currently the fastest-growing economy globally, significantly enhancing its position in the financial market As Vietnam shares a border with China, the trade relationship between the two countries constitutes a substantial portion of Vietnam's international trade This proximity suggests a strong expectation for increased information transmission between China and Vietnam, further bolstering their economic ties.
Vietnam is part of the ASEAN (Association of Southeast Asian Nations) organization, which includes other key markets such as Indonesia, Malaysia, the Philippines, and Singapore As the ninth largest economy globally, ASEAN is experiencing significant growth and enhanced integration among its member countries.
Basic definition
A stock index, also known as a stock market index, is a valuable tool for measuring the performance of a specific segment of the stock market It is calculated using the prices of selected stocks, often employing a weighted average approach Investors and financial managers utilize stock indices to assess market trends and compare the returns on particular investments.
Most financial studies involve returns, instead of prices, of assets Campbell et al.
In 1996, it was emphasized that returns serve two primary purposes for average investors: first, they provide a comprehensive and scale-invariant summary of the investment opportunity; second, return series are more manageable than price series due to their superior statistical properties.
There are several definitions of an asset return, and in this thesis, we use the word ‘return’ in means of continuously compounded return.
The natural logarithm of the simple gross return of an asset is called the continuously compounded return or log return:
� � �−1 = ln(� � ) − ln(� � −1 ) where � � is the price/index value at time t, and � � is the log return.
Volatility quantifies the degree of variation in returns for a specific security or market index, typically assessed through standard deviation or variance Generally, increased volatility indicates a higher level of risk associated with the security.
Return spillover refers to the phenomenon where the performance of one index influences the returns of other indices, potentially causing their returns to rise or fall in response to changes in the first index.
Volatility spillover refers to the phenomenon where the fluctuations in the returns of one index can influence the volatility of another index's returns, potentially leading to an increase or decrease in the targeted index's volatility.
A time series is defined as a sequence of data points collected at uniform time intervals, often reflecting changes over time This thesis focuses on the daily closing indices and their corresponding daily returns, analyzing them as time series data.
Time series analysis involves various techniques for examining time-based data to derive significant statistics and insights This research investigates time series analysis to address the questions outlined in this section.
Time series analysis often encounters challenges, particularly with the presence of a unit root, which can hinder statistical inference if not properly addressed The ordinary least squares (OLS) method is commonly employed to estimate the slope coefficients in auto-regressive models, but its effectiveness hinges on the assumption that the stochastic process is stationary When dealing with non-stationary processes or those exhibiting a unit root, OLS can lead to invalid estimates Granger and Newbold (1974) referred to these misleading outcomes as spurious regression results, characterized by inflated R² values and high t-ratios that lack genuine economic significance.
When two or more series exhibit cointegration, they share common stochastic trends and tend to move together over the long term A detailed exploration of cointegration and its testing methods is presented in Chapter Three.
The Granger causality test, developed by Granger in 1969 and expanded in 1988, is a statistical method used to assess whether one time series can effectively forecast another A time series X is considered to Granger-cause Y if statistical tests, such as t-tests and F-tests, demonstrate that past values of X, along with lagged values of Y, significantly contribute to predicting future values of Y.
We discuss in details the Granger causality test in chapter three.
Research questions
From the above perspectives; we develop the thesis with two research questions as follows.
Research Question 1: Is there return spillover between Vietnamese and other markets?
Research Question 2: Is there volatility spillover between Vietnamese and other markets?
For the first research question, we use the following null hypothesis an alternative hypothesis:
H0: There is return spillover between Vietnam and other markets
H1: There is no return spillover between Vietnam and other markets
The second research question is answered with the following null hypothesis an alternative 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 the research questions through three time frames as earlier discussed.
Structure
This thesis is structured into five chapters: Chapter two provides a critical overview of the relevant literature, Chapter three details the methodology used in the study, Chapter four presents and discusses the results, and Chapter five concludes the research.
Literature review
Market integration and price spillover between equity markets have been extensively researched Grubel (1968) examined the co-movement and correlation of various markets from a U.S perspective, highlighting the benefits of international diversification Eun & Shim (1989) explored the international transmission of stock market movements, revealing significant multi-lateral interactions among national markets King & Wadhwani (1990) developed a model illustrating how "contagion" occurs when rational agents infer information from price changes in other markets, providing evidence of such effects from diverse data sources Jon (2003) demonstrated the transmission of information from the U.S and Japan to Korean and Thai equity markets between 1995 and 2000 Additionally, Berben & Jansen (2001) analyzed shifts in correlation patterns among international equity returns at both the market and industry levels, focusing on Germany, Japan, the UK, and the U.S from 1980 to 2000.
The volatility spillovers also gained focus of various authors Hamao, Masulis & Ng
In 1990, evidence was found of price volatility spillovers from New York to Tokyo, London to Tokyo, and New York to London, while no spillover effects were observed in the opposite directions for the pre-October 1987 period Additionally, Karolyi (1995) investigated the short-run dynamics of returns and volatility for stocks traded on the New York and Toronto stock exchanges using a multivariate GARCH model The study concluded that the magnitude and persistence of return innovations originating from either market and transmitted to the other are significantly influenced by the modeling of cross-market volatility dynamics.
In a study conducted by Chelley-Steeley (2000), it was revealed that equity market volatility across different countries has seen a significant increase in the correlation of conditional variances among major equity markets over the past twenty years.
(2003) quantified the magnitude and time-varying nature of volatility spillovers from the aggregate European (EU) and US market to 13 local European equity markets.
Johnson & Soenen (2002) explored the integration of 12 Asian equity markets, revealing that Australia, China, Hong Kong, Malaysia, New Zealand, and Singapore are closely linked to Japan's stock market, with this integration strengthening since 1994 Meanwhile, Tatsuyoshi (2003) analyzed return and volatility spillovers from Japan and the US to seven Asian markets, concluding that the US significantly impacts Asian market returns, while Japan's influence is negligible However, Japanese market volatility affects Asian markets more than the US does, and there is a negative spillover of volatility from Asian markets to Japan.
Singh, Kumar, and Pandey (2010) investigated price and volatility spillovers among 15 stock markets across North America, Europe, and Asia using a VAR model for returns and an AR-GARCH model for volatility Their findings indicated that spillovers primarily flowed from the US market to Japan and Korea, followed by Singapore and Taiwan, and then to Hong Kong and Europe before returning to the US The study also identified Japan, Korea, Singapore, and Hong Kong as the most influential markets within Asia.
Worthington & Higgs (2004) found significant positive mean and volatility spillovers between three developed markets—Hong Kong, Japan, and Singapore—and six emerging markets, including Indonesia, Korea, Malaysia, Philippines, Taiwan, and Thailand However, the mean spillover effects from developed to emerging markets vary across the emerging economies Additionally, the study revealed that own-volatility spillovers tend to be greater than cross-volatility spillovers for all markets, particularly in the emerging markets.
Lakshmi (2004) pointed a high degree of volatility co-movement between Singapore, US, UK and Hong Kong market.
Chuang, Lu, and Tswei (2007) explored the interdependence of volatility across six East Asian markets using the VAR-BEKK model, revealing a high level of conditional variance interdependence, with the Japanese market being the most influential in transmitting volatility to other markets Additionally, a study in 2009 employed the VAR(p)-GARCH(1,1) model to analyze volatility spillover effects among stock markets in India, Hong Kong, South Korea, Japan, Singapore, and Taiwan, finding statistically significant spillover effects within these markets.
Sariannidis, Konteos, and Drimbetas (2010) examined the volatility linkages among the stock markets of India, Singapore, and Hong Kong from July 1997 to October 2005, revealing a strong GARCH effect and high market integration, with volatility influenced by information affecting mean returns Similarly, Giampiero and Edoardo (2008) investigated the transmission mechanisms of volatility using a Markov Switching bi-variate model, finding long-term market spillovers from Hong Kong to Korea and Thailand, interdependence with Malaysia, and co-movement with Singapore.
Several authors, including Jang & Sul (2002), In et al (2001), Yilmaz (2010), Alethea et al (2012), and Matthew, Wai-Yip Alex & Lu (2010), along with Indika, Abbas & Martin (2010), have focused on the interdependence and volatility spillover effects observed during financial crises.
In their 2001 study, et al analyzed dynamic interdependence, volatility transmission, and market integration among selected Asian stock markets during the 1997-1998 financial crisis using the VAR-EGARCH model The findings revealed that Hong Kong significantly influenced volatility transmission to other Asian markets, with evidence of market integration as each market responded to both local and external news, especially negative developments.
Alethea et al (2012) utilized graphical modeling to analyze the S&P 500, Nikkei 225, and FTSE 100 stock market indices, examining the spillover effects of returns and volatility across these significant global markets before, during, and after a specified period.
2008 financial crisis Authors found that the depth of market integration changed significantly between the pre-crisis period and the crisis and post- crisis period.
Matthew, Wai-Yip Alex, and Lu (2010) explored the spillover effects of financial crises by analyzing the correlation dynamics between eleven Asian and six Latin American stock markets in relation to the US stock market Their findings indicated a significant contagion effect from the US stock market to both regions during the global financial crisis Notably, the intensity of this contagion was comparable in both regions, despite their distinct economic, political, and institutional characteristics.
Indika, Abbas, and Martin (2010) analyzed the relationship between stock market returns and volatility during the Asian and global financial crises of 1997-98 and 2008-09, focusing on Australia, Singapore, the UK, and the US using the MGARCH model Their findings revealed that the Asian crisis and the more recent global financial crises did not have a significant impact on stock returns in these markets However, both crises notably heightened stock return volatility across all four markets.
Yilmaz (2010) examined the contagion and interdependence of East Asian equity markets since the early 1990s, comparing the current crisis to previous episodes The study highlights significant differences in the behavior of return and volatility spillover indices over time While the return spillover index indicates increased integration among these markets, the volatility spillover index shows notable spikes during major crises, such as the East Asian crisis The peaks of both indices during the ongoing global financial crisis underscore the severity of the current situation.
Zhou, Zhang & Zhang (2012) proposed measures of the directional volatility spillovers between the Chinese and world equity markets It was found that the
During the subprime mortgage crisis, the US market significantly influenced volatility across global markets, particularly affecting China, Hong Kong, and Taiwan The interactions of volatility among these Asian markets were notably stronger compared to those observed between Chinese, Western, and other Asian markets.
Methodology
Data
The index values for the analyzed markets were sourced from Yahoo! Finance, including both open and close prices Using this raw data, we calculated the daily returns as outlined in the first chapter The analysis covers the period from January 3, 2005, to August 30, 2012.
The model and methods
Before discussing in details each testing method, we present here some of their basic characteristics and their rationales.
The ADF unit-root test is conducted to determine the presence of a unit root in all indices and their returns This test is essential because subsequent tests, such as the Johansen co-integration test, necessitate a uniform degree of integration among the data.
- Long-run integration is tested through Johansen co-integration techniques.
When two or more series exhibit cointegration, they share common stochastic trends, indicating that they move together over the long term However, in the short term, these series may diverge from one another.
The short-run dynamics are analyzed using the Granger causality test and the Vector Autoregressive (VAR) model While the Granger causality test identifies potential relationships between endogenous variables, it does not specify their directionality Therefore, the VAR model is employed to further investigate these relationships, including the examination of return spillover effects.
- 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 and stationary test in financial time series.
Denote the series by x t , to verify the existence of a unit root of x t , we may perform the test with null hypothesis H0: β = 1 versus the alternative hypothesis H1: β Critical value :
we can reject the null hypothesis H0
there is more than m cointegrating vector
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
If n-1 vector out of n vectors are found cointegrated (having a common stochastic trend), then all n vector are called “cointegrated in long run or represents long run equilibrium”.
To decide the result, we use the following rules:
- If max eigenvalue < Critical value :
we fail to reject the null hypothesis H0
- If max eigenvalue > Critical value :
we can reject the null hypothesis H0
Short run interrelationship is examined through Granger Causality test (Granger (1969; Granger (1988)) The Granger (1969) approach to the question of whether
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 the explanation.
Granger causation indicates that variable X can predict variable Y if the coefficients of lagged X are statistically significant Often, this relationship is bidirectional, where X Granger causes Y and Y also Granger causes X.
The phrase "Granger causes" should not be misunderstood as indicating a direct effect or result Instead, Granger causality assesses the precedence and informational content between variables, but it does not establish causality in the traditional sense.
To test the null hypothesis that x does not Granger-cause y in stationary time series, it is essential to identify the appropriate lagged values of y for inclusion in a univariate autoregression model of y.
� � = � 0 + � 1 � �−1 + � 2 � �−2 + … + � � � �−� + �������� � Next, the auto regression is augmented by including lagged values of x:
In regression analysis, all lagged values of x that are individually significant based on their t-statistics are retained, as long as they collectively enhance the regression's explanatory power, verified by an F-test The null hypothesis, which states that x does not Granger-cause y, is accepted only if no lagged values of x are included in the regression model.
To decide the result, we use the following rules:
- 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
Vector Auto Regression (VAR) is a widely utilized method for forecasting interconnected time series and examining the dynamic effects of random disturbances on a set of variables By considering each endogenous variable as a function of the lagged values of all endogenous variables, the VAR approach eliminates the necessity for structural modeling The mathematical representation of a VAR model is denoted as VAR(p).
� � = � 1 � � −1 + + � � � �− � + �� � + � � 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.
The VAR (k)-BEKK (1, 1) model to estimate volatility spillover is explained below
Let endogenous � � is Nx1 vector with the mean equation:
� � = � + � 1 � �−1 + … + � 1 � � −� + � � The error term has multinomial normal distribution as
The BEKK(p,q) representation of the variance of error term � �
Where � � and � � are kxk parameter matrix
� 0 is kxk upper trangular 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 k variables requires � 2 (� + )� + �(� + 1)/2 parameters which increases rapidly with p and q.
The BEKK (p=1, q=1) model with 10 variables necessitates 255 parameters, making optimization challenging To analyze volatility spillovers between two markets more efficiently, we utilize the bivariate BEKK (1, 1) model, which requires only 11 parameters For handling volatility spillovers among multiple markets, refer to section 3.2.7 for detailed methodology.
The bivariate VAR (k) BEKK(1, 1) model can be written as
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.
In the BEKK representation of volatility, the parameter � 21 is the volatility spillover from market 2 to market 1and � 12 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.
We utilize a two-stage GARCH model to analyze the volatility spillover among various indices, incorporating same-day effects and estimating the partial coefficients 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� �,� is the return of the j th index at time t
� � is the error or unexpected return of the j th index,
� 2 is the variance – which presents the volatility– of the j th index.
Second stage: the residuals are then used in the GARCH equation of the other indices as follows
�� �� −1 where k: number of the indices open/close before the j th index l: number of the indices open/close after j th index.
The coefficients \( \beta_{kj} \) and \( \beta_{lj} \) represent the volatility spillover effects from markets k and l to market j, respectively The statistical significance and values of these coefficients offer valuable insights into the nature and extent of volatility spillovers among the markets.
The GARCH variance equation for the VNIndex incorporates the same-day residuals of three indices—Nikkei, SSE, and TWII—anticipating volatility spillovers within the same trading day In contrast, the equation utilizes one lag day residuals from six other indices—BES, HIS, JKSE, KLSE, PSE, and STI—since their opening and closing occur after the VNIndex, suggesting that any potential volatility spillovers would manifest on the following day.
Data Description, Results and Analysis of Results
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
Figure 1 illustrates the opening and closing times of various indices in UTC, which is crucial for assessing their potential influence on one another within the same day or the following day For instance, the Nikkei index, which opens and closes earlier than the VNIndex, may impact the VNIndex on the same day, while any effects from the VNIndex on the Nikkei index are likely to occur after a one-day delay.
Figure 1 Index timings by UTC Time
Tables 2, 3, and 4 display the descriptive statistics of the analyzed indices, revealing that all skewness and kurtosis values are notably elevated Additionally, the Jarque-Bera test statistics are highly significant at the 1% level, except for the VNIndex during the post-crisis period, suggesting that the price distributions are not normally distributed.
Table 2 Descriptive statistics of Indices in pre-crisis period
BSESN HIS JKSE KLSE N225 PSEI SSE STI TWII VNINDEX
Table 3 Descriptive statistics of Indices in crisis period
BSESN HIS JKSE KLSE N225 PSEI SSE STI TWII VNINDEX
Table 4 Descriptive statistics of Indices in post-crisis period
BSESN HIS JKSE KLSE N225 PSEI SSE STI TWII VNINDEX
4.1.3 Descriptive statistics of Indices’ return
Table 5, 6 and 7 present the descriptive statistics of the studied indices returns.
The analysis reveals that all skewness and kurtosis values are elevated, and the Jarque-Bera test statistics are highly significant at the 1% level, except for the VNIndex during the crisis period, suggesting that the distribution of all returns deviates from normality.
Table 5 Descriptive statistics of Indices’ return in pre-crisis period
BSESN HIS JKSE KLSE N225 PSEI SSE STI TWII VNINDEX
Table 6 Descriptive statistics of Indices’ return in crisis period
BSESN HIS JKSE KLSE N225 PSEI SSE STI TWII VNINDEX
Table 7 Descriptive statistics of Indices’ return in post-crisis period
BSESN HIS JKSE KLSE N225 PSEI SSE STI TWII VNINDEX
The crisis has negative impact on the return: during the crisis period almost all the indices have zero or negative mean return except JKSE.
The correlation matrix presented in Tables 8, 9, and 10 reveals the relationships between ten markets across three time frames Notably, the VNIndex shows low correlations with other Asian markets, peaking at 0.291 with the PSEI during the crisis period and dropping to 0.013 with the JKSE in the pre-crisis phase In contrast, stronger correlations are observed among other markets, such as the STI and HIS, which have correlations of 0.71 and 0.741 during the pre-crisis and crisis periods, respectively Additionally, the STI and Nikkei exhibit a correlation of 0.581 in the pre-crisis period, while in the post-crisis period, the STI and HIS maintain a correlation of 0.736, and the TWII and HIS show a correlation of 0.647.
In the crisis period all the correlations increase and this phenomenon indicates stronger linkage in term of return during the crisis period.
In the post-crisis period, the correlation between VNIndex and six other indices—BSE, JKSE, Nikkei, PSEI, SSE, and TWII—has diminished, while the correlations with three indices—HIS, KLSE, and STI—have continued to rise.
Generally the correlations are higher in the post-crisis in comparison with the pre-crisis So there is evidence of better integration of Vietnamese stock market with other market.
Table 8 Correlation Matrix between Indices' returns in pre-crisis period
BSESN HIS JKSE KLSE N225 PSEI SSE STI TWII VNINDEX
Table 9.Correlation Matrix between Indices' returns in crisis period
BSESN HIS JKSE KLSE N225 PSEI SSE STI TWII VNINDEX
Table 10.Correlation Matrix between Indices’ returns in post-crisis period
BSESN HIS JKSE KLSE N225 PSEI SSE STI TWII VNINDEX
Long-run interdependence
The Johansen cointegration techniques are utilized to analyze long-run interdependence in financial time series To begin this process, it is essential to conduct a unit root test to assess the stationarity of the time series data.
The ADF test results indicate a unit root in all indices under the assumption of drift without trend, while all return series are stationary This suggests that the indices exhibit one degree of integration, allowing for the proper application of Johansen's cointegration test.
Table 11 Unit root test result on Indices
Series Prob Lag Max Lag Obs Conclusion BSESN 0.4927 13 25 1986 Unit root
Table 12 Unit root test results on Indices' return
Series Prob Lag Max Lag Obs Conclusion
Tables 13, 14, and 15 present the test statistics, critical values, and probability values for both the trace test and the maximum eigenvalue test, assessing the long-run interdependence between the VNIndex and other indices Given that all series exhibit one degree of cointegration, we evaluate two null hypotheses: a) the absence of a co-integrating vector, and b) the presence of at most one co-integration vector Additionally, we provide conclusion markers for straightforward interpretation.
In next paragraphs, we examine in detail the Johansen’s cointegration test between VNIndex and STI in the crisis period for an example on how to make the conclusion.
The Trace test with m equal to 1(or 1 cointegrating vector)
H0: Rank(�) = 1 or there is 1cointegrating vector, versus
H1: Rank(�) > 1 or there is more than 1 cointegrating vector
Result: statistics value < critical value (2.177< 3.841); so we cannot reject the null hypothesis H0
The Trace test with m equal to 0 (or no cointegrating vector) H0:Rankversus H1: Rank(�) > 0 or there is more than 0 (�) = 0 or there is no cointegrating vector, cointegrating vector
Result: statistic value > critical value (18.454 > 15.495); so we can reject the null hypothesis H0
The max eigenvalue test with m equal to 1 (or 1 cointegrating vector) H0:RankH1:Rank(�) = 2 or there is 2 cointegrating vector(�) = 1 or there is 1 cointegrating vector, versus
Result: statistic value < critical value (2.177 < 3.841); so we cannot reject the null hypothesis H0.
The max eigenvalue test with m equal to 0 (or 0 cointegrating vector) H0:RankH1:Rank(�) = 1 or there is 1 cointegrating vector(�) = 0 or there is 0 cointegrating vector, versus
Result: statistic value > critical value (16.277 > 14.265); so we can reject the null hypothesis H0.
The above test results implicit that there is one cointegration vector, or there is Johansen’s cointegration between VNIndex and STI in the crisis period.
For simplicity we do not present the details of each Johansens’s cointegration test but only give the summary.
- There is no cointegration between VNIndex and other market at 5% significant level.
At a 5% significance level, the VNIndex shows cointegration with eight studied markets, excluding the Nikkei; however, a 10% significance level reveals cointegration between the VNIndex and the Nikkei Notably, there is strong evidence of cointegration during periods of crisis.
- In the post-crisis period: VNIndex is in cointegration with one index (Nikkei) at 5% significant level, and with two indices (Nikkei, SSE) at 10% level.
The analysis reveals two key findings: first, the crisis has enhanced the cointegration between the Vietnamese stock market and other markets; second, the VNIndex is increasingly showing stronger cointegration with these markets Despite this increased connection, the overall cointegration remains low, suggesting that there are potential long-term benefits to be gained from diversifying portfolios across different markets.
Table 13 Johansen's cointegration test for pre-crisis period
Eigen value Trace Max-Eigen Conclusion
Statistic Critical Val Prob Statistic Critical Val Prob
Table 14 Johansen's cointegration test for crisis period
Eigen value Trace Max-Eigen Conclusion
Statistic Critical Val Prob Statistic Critical Val Prob
Table 15.Johansen's cointegration test for post-crisis period
Eigen value Trace Max-Eigen Conclusion
Statistic Critical Val Prob Statistic Critical Val Prob
Short-run interdependence
Cointegration reveals a long-term relationship between stochastic variables; however, it is possible for two time series to lack long-term cointegration while still exhibiting short-term causal interrelationships.
We analyze short-run interdependence between Vietnamese markets and other markets through the Granger causality analysis and bi-variate model.
The Granger causality test, utilizing four lags, was conducted to analyze the relationship between VNIndex returns and other index returns The findings are detailed in Tables 16, 17, and 18, which present the F-statistics and probability values for each direction of causality.
We discuss in detail a specific Granger causality test to understand the results in next paragraphs.
Consider the 2-way causation Granger causality test applied to the VNIndex return and the STI return in pre-crisis period, for each way we have a null hypothesis
- H0: VNIndex Return does not Granger cause Index’s return, versus
- H1: VNIndex return Granger cause Index’s return
The F-statistics value is 0.5697 and the p-value is 0.6847, indicating that at the 5% significance level, we cannot reject the null hypothesis of no Granger causality Therefore, we conclude that VNIndex Return does not Granger cause the Index’s return.
- H0: STI return does not Granger cause VNIndex return, versus
- H1: STI return Granger cause VNIndex return
The F-statistics value of 4.71684 and a p-value of 0.009 indicate that, at a 5% significance level, we can reject the null hypothesis of no Granger causality Therefore, we conclude that STI returns Granger cause the returns of the VNIndex, suggesting that STI returns are useful for predicting VNIndex returns.
We summary the results of the entire Granger causality tests for nine pairs for all three periods as follows:
- 4 indices’ return(HIS, JKSE, KLSE, STI) Granger cause VNIndex return
- VNIndex return Granger causes PSEI’s return
- 7 Indices’ (BSE, HIS, JKSE, KLSE, Nikkei, STI, TWII ) Granger cause VNIndex return
- VNIndex return does not Granger cause any index return
- There is no Granger causality among Vietnamese market and other markets.
Table 16 Granger causality test results for pre-crisis period
VNIndex Return does not Granger cause Index's Return Index's Return does not Granger cause
Table 17 Granger causality test results for crisis period
VNIndex Return does not Granger cause Index's Return Index's Return does not Granger cause
Table 18 Granger causality test results for post-crisis period
VNIndex Return does not Granger cause Index's Return Index's Return does not Granger cause
4.3.2 VAR Model for estimation of return spill over
The Granger causality test, as discussed in the previous section, reveals the inter-dependence among endogenous variables; however, it does not measure the strength of these relationships or clarify whether the dependencies are negative or positive.
The VAR model is commonly employed to assess the strength and direction of cross-correlation among returns In this study, we implemented a bivariate VAR model with five lags to analyze the relationship between the returns of the VNIndex and those of other indices.
We can interpret in detail the results for the pair of VNIndex and KLSE with VNIndex return as the dependent variable in pre-crisis period as follow.
The equation of VNIndex return at time t is:
∗ ��������(�−4) + 0.135192 ∗ ��������(�−5) where ��������(�), ����(�) is the return of VNIndex and BSE at time t respectively.
The coefficients of the parameters in the equation are statistically significant at the 5% level, indicating that the VNIndex return at time t is influenced by the KLSE return at time t-1.
1 and the VNIndex return at time t-1; and that the return of KLSE does have impact on the return of VNIndex.
As supposed, the bivariate VAR model gives the same results as the Granger causality:
The returns of four indices—HIS, JKSE, KLSE, and STI—have a significant impact on the conditional return of the VNIndex Notably, the return spillover from these markets to the Vietnamese market is exclusively positive, indicating that positive or negative returns from other markets will similarly influence the Vietnamese market in a positive or negative manner.
- In the other side, Vietnamese market does not affect any market.
- 7 Indices’ (BSE, HIS, JKSE, KLSE, Nikkei, STI, TWII) significantly affect the conditional mean of VNIndex return And as in the pre-crisis period the return spillover is only positive.
- Vietnamese market does not affect any market.
- In this period, the return of Vietnamese stock market does not depend on any market and it does not have any impact on the return of other market.
The Granger causality test and VAR model reveal significant return spillovers from the studied markets to the Vietnamese stock market during the crisis period However, in the post-crisis phase, the returns of the Vietnamese market appear to be independent of any external market influences, with no evidence found for return spillovers from other markets.
Our findings reveal significant return spillover during crisis periods, aligning with previous research Johansson (2010) highlights increased financial market integration and heightened comovements during international financial turmoil in East Asia and Europe Similarly, Yilmaz (2010) notes that return spillovers in the East Asia region peaked during the global financial crisis of 2008.
Dependent Variable Parameter BSESN HIS JKSE KLSE NIKKEI PSEI SSE
Constant 0.001506 0.000972 0.001289 0.000562 0.000355 0.000866 0.001791 INDEX(-1) 0.069850 0.017874 0.079472 0.182415* 0.013077 0.063158 -0.007988 INDEX(-2) -0.047621 -0.102363 -0.055353 -0.046948 -0.047579 0.009260 -0.035962 INDEX(-3) -0.026521 0.118912 0.062134 0.102227 0.055249 -0.015830 0.057584 INDEX(-4) 0.044215 0.030787 0.014689 -0.034647 -0.048945 0.046173 0.061113 Index INDEX(-5) 0.005935 -0.069735 -0.009713 -0.086894 0.054467 -0.060741 -0.001040
VNINDEX(-1) -0.032774 -0.020200 -0.005666 -0.011817 -0.033183 -0.046970 -0.041528 VNINDEX(-2) 0.018690 0.031146 -0.000738 0.017217 0.043609 0.057801 0.016518 VNINDEX(-3) 0.012946 -0.033675 -0.005644 -0.006833 -0.017290 0.032282 0.023978 VNINDEX(-4) 0.003331 0.007732 -0.007485 -0.001650 0.004269 -0.081458 -0.025466 VNINDEX(-5) -0.037819 -0.007984 -0.049031 -0.022990 0.009311 0.021683 -0.015315
Constant 0.001143 0.001151 0.001044 0.001061 0.001175 0.001154 0.001080 INDEX(-1) 0.080009 0.152534* 0.134334* 0.274048* 0.072588 0.073553 0.041122 INDEX(-2) -0.033745 -0.093921 -0.079569 -0.082316 -0.078406 -0.113554 0.002782 INDEX(-3) 0.009854 0.006222 0.045404 -0.024840 0.074166 -0.000804 0.028995 INDEX(-4) -0.034979 -0.053098 -0.011836 0.016498 0.012999 0.025874 -0.011272 VNIndex INDEX(-5) 0.024822 0.049128 0.039276 0.074666 0.075648 0.071072 0.013234
VNINDEX(-1) 0.192895* 0.192183* 0.198050* 0.189254* 0.189438* 0.187981* 0.189665* VNINDEX(-2) -0.056545 -0.051854 -0.061801 -0.050970 -0.053126 -0.044113 -0.058253 VNINDEX(-3) -0.015027 -0.018653 -0.013958 -0.022831 -0.026566 -0.021988 -0.016813 VNINDEX(-4) 0.069593 0.076445 0.069364 0.069601 0.076704 0.069961 0.071838 VNINDEX(-5) 0.130306* 0.126846* 0.132583* 0.135192* 0.121569* 0.136764* 0.130605*
* denotes rejection significance at the 5% level
Table 20 Bivariate VAR Model (VNIndex and other Indices) estimates of model on indices return in crisis period
Dependent Variable Parameter BSESN HIS JKSE KLSE NIKKEI PSEI SSE STI TWII
Constant -0.000138 -0.000512 0.000139 -0.000143 -0.000846 -4.02E-05 -0.001038 -0.000259 -0.000271 INDEX(-1) 0.044814 -0.067020 0.140367* -0.376240* -0.003195 0.163599* -0.021007 -0.000147 0.049611 INDEX(-2) -0.022688 0.009662 0.060813 -0.134041* -0.111649 -0.015079 -0.006055 0.080394 0.062751 INDEX(-3) -0.042417 -0.095814 -0.049052 -0.038190 -0.063891 -0.031996 0.047696 -0.070482 -0.021820 INDEX(-4) 0.005771 -0.034770 -0.024590 0.008435 0.042625 -0.088001 0.064574 -0.033251 -0.019361 Index INDEX(-5) -0.050426 0.003119 -0.039375 0.042645 0.007617 -0.047412 -0.048515 0.040678 -0.032846
VNINDEX(-1) -0.007498 -0.002076 0.030588 0.008559 0.001044 -0.002020 -0.013636 -0.001509 -0.055144 VNINDEX(-2) 0.012843 0.069335 0.015961 0.019416 0.062940 0.008397 0.020007 0.039882 0.055477 VNINDEX(-3) 0.035812 -0.024407 -0.025594 0.016493 -0.090350 0.029692 -0.009526 0.015709 -0.080780 VNINDEX(-4) -0.009759 0.023554 0.092062 0.018210 0.119690 0.049244 0.043968 0.024708 0.028034 VNINDEX(-5) 0.058237 0.011786 -0.055077 0.015144 -0.089998 -0.009776 0.101793 -0.021880 -0.004011
Constant -0.000570 -0.000505 -0.000676 -0.000570 -0.000498 -0.000610 -0.000509 -0.000541 -0.000566 INDEX(-1) 0.181469* 0.167197* 0.151806* 0.164505* 0.110959* -0.007909 0.057384 0.199619* 0.148196* INDEX(-2) 0.038539 0.037957 0.141970* 0.074950 0.039757 0.055668 -0.049022 0.083105 0.019350 INDEX(-3) 0.012771 -0.002584 0.019924 0.015298 0.015451 0.072572 0.024042 0.012242 0.039756 INDEX(-4) -0.014198 0.005903 -0.050835 -0.091172 -0.012229 0.018713 0.043131 -0.035385 -0.030394 VNIndex INDEX(-5) 0.051131 0.092174 0.043044 0.032401 0.017868 0.069113 -0.003547 0.068384 0.061605
VNINDEX(-1) 0.303025* 0.298550* 0.292181* 0.319738* 0.297118* 0.333954* 0.334982* 0.302983* 0.313262* VNINDEX(-2) -0.051925 -0.046539 -0.068337 -0.049860 -0.053046 -0.071749 -0.047258 -0.055916 -0.049603 VNINDEX(-3) -0.013384 -0.024996 -0.019943 -0.005200 -0.017597 -0.029919 -0.023838 -0.018779 -0.023822 VNINDEX(-4) 0.115851 0.115789 0.135766* 0.127955* 0.143403* 0.132089* 0.130676* 0.120351* 0.144553* VNINDEX(-5) -0.024249 -0.031827 -0.031148 -0.032030 -0.035054 -0.041141 -0.026090 -0.031714 -0.031889
* denotes rejection significance at the 5% level
Table 21 Bivariate VAR Model (VNIndex and other Indices) estimates of model on indices return in post-crisis period
Dependent Variable Parameter BSESN HIS JKSE KLSE NIKKEI PSEI SSE STI TWII
Constant 1.05E-06 -5.76E-05 0.000691 0.000336 -0.000135 0.000990 -0.000221 0.000120 4.79E-05 INDEX(-1) 0.049360 0.006060 0.014331 0.084204 -0.019491 -0.120888 -0.037828 0.023076 0.065860 INDEX(-2) 0.052579 0.096060 0.048619 0.039264 0.050161 0.011004 0.035650 0.044360 -0.034293 INDEX(-3) -0.050458 -0.035367 -0.144113* -0.068433 -0.002487 -0.061155 -0.013609 -0.011282 -0.034342 INDEX(-4) -0.024977 -0.090863 -0.148597* 0.028788 -0.048501 -0.076739 -0.070252 -0.011604 -0.096157 Index INDEX(-5) 0.006604 0.013957 0.038974 0.027388 -0.102389 -0.039763 0.068263 -0.015073 0.039251
VNINDEX(-1) 0.007259 0.077311 -0.004852 0.033647 0.040974 0.083701 0.084609 0.029218 0.054327 VNINDEX(-2) 0.019881 -0.048097 -0.018381 -0.025480 -0.112480 -0.034919 0.022144 -0.032733 0.024364 VNINDEX(-3) 0.027918 -0.008937 -0.002938 -0.002283 0.008863 0.019783 -0.063328 -0.004915 -0.038308 VNINDEX(-4) 0.048129 0.037151 0.065172 -0.027085 0.061795 0.008735 0.102483 0.027372 0.050085 VNINDEX(-5) -0.040192 -0.059547 -0.076371 -0.016676 -0.027349 -0.057068 -0.010646 -0.002473 -0.016477
Constant -0.000350 -0.000341 -0.000392 -0.000387 -0.000338 -0.000382 -0.000323 -0.000380 -0.000353 INDEX(-1) 0.086900 0.074130 0.058174 0.095505 0.074054 0.048649 0.066694 0.114115 0.061527 INDEX(-2) -0.029271 0.072028 0.078259 0.122262 0.054317 -0.013261 0.058682 0.056040 0.059580 INDEX(-3) 0.037517 -0.038892 -0.032701 -0.138680 -0.077528 -0.010809 -0.076663 0.017279 0.025149 INDEX(-4) -0.037304 -0.034785 -0.059040 -0.007582 -0.049698 0.035523 -0.038557 -0.015918 -0.033588 VNIndex INDEX(-5) 0.037736 0.079342 0.043415 0.041352 0.053094 -0.012173 0.063615 0.058571 0.086187
VNINDEX(-1) 0.201526* 0.189446* 0.194443* 0.194848* 0.186204* 0.197547* 0.197277* 0.186459* 0.193553* VNINDEX(-2) 0.021565 0.019079 0.027988 0.020462 0.026021 0.025402 0.026375 0.018630 0.017915 VNINDEX(-3) -0.008663 -0.008702 -0.003358 -0.003093 0.010870 -0.007661 -0.012842 -0.008418 -0.017326 VNINDEX(-4) 0.033122 0.041730 0.040955 0.042196 0.047323 0.033174 0.043478 0.036075 0.036325 VNINDEX(-5) -0.053129 -0.058357 -0.055842 -0.049929 -0.068112 -0.048854 -0.052987 -0.054789 -0.055712
* denotes rejection significance at the 5% level
Volatility spill over
The parameters estimates of the BEKK Model which explain the volatility spillover between Vietnamese market and other market through 3 periods are presented in table 22, 23 and 24.
The bivariate BEKK model estimates for the time series [Index, VNIndex] reveal key parameters that illustrate volatility dynamics Notably, parameter \( \beta_{12} \) indicates the volatility spillover from the Index to the VNIndex, while \( \beta_{21} \) captures the volatility transfer from the VNIndex to the Index Additionally, parameters \( \beta_{11} \) and \( \beta_{22} \) reflect the influence of the residuals (ARCH component) on the conditional variance, highlighting the interconnectedness of these financial indices.
� 11 , � 22 that indicate the impact of the previous variance (volatility) to the conditional variance.
We summarize the results from the bivariate BEKK model as below:
The analysis reveals that the indices HIS, JKSE, and PSEI significantly influence the conditional volatility of the Vietnamese stock market, with a notable parameter (� 12) at a 5% significance level Specifically, the JKSE and PSEI indices exhibit a positive correlation, indicating that increased volatility in these markets leads to reduced volatility in Vietnam's stock market Conversely, the HIS index demonstrates a negative impact on this volatility.
(� 12 0) on JKSE and negative effect (� 21 0); but the effect is negative from SSE.
The volatility spillover from Vietnamese stocks market has positive affect to HIS and Nikkei; and negative affect to BESEN
- During this period, two indices PSEI and SSE affect the conditional volatility of Vietnamese markets: the parameter (� 12 ) is significant at 5%; and all the effects from these markets are negative (� 12 < 0).
- The volatility spillover from Vietnamese stocks market has positive affect to Nikkei (� 21 > 0).
During periods of crisis, the significance of volatility spillovers increases notably In the pre-crisis phase, the conditional variances of the Vietnamese stock market are influenced by three markets, while during the crisis, five markets play a role, and in the post-crisis period, four markets contribute This dynamic helps explain the conditional volatility of two markets in the pre-crisis phase, three markets during the crisis, and one market in the post-crisis period.
We also learn about the components of the conditional variance of markets - the ARCH and the GARCH:
The ARCH components, indicated by the A(1, 1) and A(2, 2) coefficients, represent the correlation of daily price variations with those of the previous day, highlighting the influence of historical innovations on current price movements.
- GARCH components (reflected via the B (1, 1) and B (2, 2) coefficient):the previous volatility.
Generally for all three periods; the volatilities show that the coefficient of
GARCH effect is much higher than the value of ARCH coefficient This indicates that the volatility depends more on its lags than on the innovation.
Table 22 Parameters estimates of BEKK model for pre-crisis period
BSESN HIS JKSE KLSE NIKKEI PSEI SSE STI TWII
* denotes rejection significance at the 5% level
Table 23 Parameters estimates of BEKK model for crisis period
BSESN HIS JKSE KLSE NIKKEI PSEI SSE STI TWII
* denotes rejection significance at the 5% level
Table 24 Parameters estimates of BEKK model for post-crisis period
BSESN HIS JKSE KLSE NIKKEI PSEI SSE STI TWII
* denotes rejection significance at the 5% level
The BEKK (1, 1) model for estimating volatility spillovers does not capture the partial effects of indices or same-day impacts To address this, we employ a univariate GARCH model to estimate these effects The findings from this analysis are detailed in Tables 25, 26, and 27, which present the results for three distinct periods.
Because of difference in opening and closing time, the volatility of Vietnamese stock market would depend on, if any:
- The same day residuals from BSE, HIS, JKSE, KLSE, PSEI, STI.
- The one lag day residuals from Nikkei, SSE, and TWI.
The GARCH analysis of the Vietnamese market reveals that its volatility is influenced by two key markets: a positive correlation with the STI and a negative correlation with the HIS Both market coefficients are statistically significant at the 5% level, indicating that increased volatility in the STI leads to heightened volatility in the Vietnamese stock market, while increased volatility in the HIS results in reduced volatility in the same market.
The VNIndex has only positive effect on the KLSE volatility.
During this period, volatility spillovers have intensified compared to the pre-crisis phase, with the VNIndex's volatility showing a negative correlation with KLSE, PSEI, and SSE, while exhibiting a positive correlation with TWII.
The results also indicate that the volatility spillovers from Vietnam have positive impact on HIS, JKSE and negative impact on BSE.
The volatility spillovers in this period decreases significantly: Vietnamese stock market now depends only on PSEI and has no impact on any other market.
Volatility spillovers become increasingly significant during crisis periods in the Vietnamese stock market Analysis reveals that during pre-crisis, crisis, and post-crisis phases, the conditional variances are influenced by 2, 4, and 1 markets, respectively Furthermore, these variances contribute to explaining the volatility of 1, 3, and 0 markets during the same periods.
Our results are similar with findings of other authors: the study of Andrew Stuart
Research by Alain (2011) highlights that global volatility linkages intensify during significant financial crises, specifically in Asia (1997-1998), Russia (1998), and the United States (2007-2008) Additionally, Indika, Abbas, and Martin (2010) discovered that the financial crises in Asia and globally during 1997-1998 and 2008-2009 led to a marked increase in stock return volatilities across four major markets: Australia and Singapore, among others.
UK, and the US Yilmaz (2010) argued that the volatility spillover index experiences significant bursts during major market crises, including theEast Asian crisis.
From the study of volatility spillover from the BEKK and VAR- GARCH model, we conclude some main points:
- The volatilities depends more on its lags than on the innovation.
- Vietnamese stock market has some integration with other markets in term of volatility spillover.
- The volatility spillovers are stronger in crisis period.
International investors can capitalize on the Vietnamese stock market for long-term portfolio diversification benefits The VNIndex exhibits low correlation with other studied markets, indicating minimal co-integration Additionally, there are limited return and volatility spillovers between Vietnam and other markets These factors enhance diversification benefits and help mitigate investment risks.
Table 25 Volatility spillover estimates of AR(1) GARCH(1,1) model for pre-crisis period
BSESN HIS JKSE KLSE NIKKEI PSEI SSE STI TWII VNIndex
* denotes rejection significance at the 5% level
Table 26 Volatility spillover estimates of AR(1) GARCH(1,1) model for crisis period
BSESN HIS JKSE KLSE NIKKEI PSEI SSE STI TWII VNIndex
* denotes rejection significance at the 5% level
Table 27 Volatility spillover estimates of AR(1) GARCH(1,1) model for post-crisis period
BSESN HIS JKSE KLSE N225 PSEI SSE STI TWII VNIndex
* denotes rejection significance at the 5% level
Conclusions
This thesis examines the interdependence between the Vietnam Index and nine other Asian indices, focusing on the return and volatility spillover effects across three distinct periods: pre-crisis, crisis, and post-crisis.
The Vietnamese stock market has shown an increasing correlation with other markets, particularly during periods of crisis when these correlations peak This trend highlights a growing linkage and integration of the Vietnamese stock market within the global financial landscape.
The Vietnamese stock market exhibited no cointegration with any other markets during the pre-crisis period However, it showed significant cointegration with nearly all markets amid the crisis and maintained this relationship with two additional markets in the post-crisis phase This observation highlights the crisis's profound impact on market interconnectivity.
The Granger causality test and VAR model reveal that return spillovers from the studied markets to the Vietnamese stock market were significant during the crisis period However, in the current period, the VNIndex returns show no dependence on any external market Additionally, there is no evidence of return spillovers originating from Vietnam in any timeframe.
A study on volatility spillovers reveals that market volatilities are more influenced by their past values than by new information The Vietnamese stock market shows some integration with other markets regarding volatility spillover effects Additionally, these spillovers tend to be more pronounced during periods of crisis.
The crisis significantly influences market interdependence, leading to increased integration among markets During these turbulent times, market correlations rise, resulting in greater cointegration and heightened spillover effects, both in terms of returns and volatility.
Foreign investors may find long-term benefits in diversifying their portfolios with Vietnamese stocks, as the VNIndex has shown independence from other studied markets This independence suggests minimal impact from return and volatility spillovers, indicating a unique investment opportunity in the current period.
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