Abstract Purpose - This thesis investigates the interdependence between the Vietnamese stock market and other nine Asian markets in terms of return and volatility spillovers during thre
Introduction
Background
The globalization of domestic markets is now an undeniable trend, attracting both domestic and international investors seeking to diversify their portfolios and reduce risk This international investment influx integrates domestic markets into the global economy, enabling quicker responses to international news and shocks As a result, domestic equity markets are becoming more interconnected with global markets, enhancing market efficiency and resilience.
Information transmission across markets has been extensively studied from two perspectives First, long-term interdependence and causality among markets serve as strong indicators of information flow Second, the study of volatility transmission has gained prominence, as it provides a key measure of risk in internationally diversified portfolios, aiding in asset diversification strategies.
The Vietnamese stock market, established a decade ago, has increasingly attracted significant investment Despite this growth, there remains a limited number of studies examining the connection between the Vietnamese equity market and international markets, particularly within Asia Understanding these linkages is crucial for investors seeking to optimize their portfolios and for policymakers aiming to strengthen market integration Exploring the influence of regional economic dynamics can provide valuable insights into Vietnam’s evolving role in the Asian financial landscape.
Purpose and scope
This study examines the interactions of price and volatility spillovers between the Vietnamese equity market and nine major Asian markets, including India, Hong Kong, Indonesia, Malaysia, Japan, the Philippines, China, Singapore, and Taiwan The research highlights the interconnectedness of these markets in terms of price movements and volatility transmission, providing valuable insights into regional financial integration Understanding these spillover effects is essential for investors and policymakers aiming to manage risk and optimize portfolio diversification within Asia The findings contribute to literature on regional market dynamics, emphasizing the importance of monitoring cross-market interactions for informed decision-making.
The study assesses return spillovers using Johansen co-integration analysis to capture long-term relationships and Granger causality tests to identify short-term spillovers Additionally, bivariate BEKK and AR-GARCH models are employed to evaluate volatility spillovers between markets.
This study analyzes return and volatility spillovers across three distinct periods: the pre-crisis phase (January 3, 2005 – December 31, 2007), the crisis phase (January 1, 2008 – June 30, 2010), and the post-crisis phase (July 1, 2010 – August 31, 2012) By evaluating these periods, the research aims to assess the impact of the financial crisis on the transfer of returns and volatility between the Vietnamese stock market and nine other Asian markets The findings highlight how crisis conditions influence market interconnectedness and risk transmission across the region.
The markets are presented by their Indices as following:
Table 1 Indices and their origination
The selected markets represent the key developed and emerging economies within Asian stock markets, highlighting their significant potential impact on the Vietnamese stock market Additionally, the chosen indices serve as widely recognized benchmark indicators, ensuring comprehensive and reliable analysis.
Hong Kong and Japan are recognized as two of the most mature financial centers in Asia, playing a vital role in the regional economy These markets are characterized by high transaction volumes and significant influence over other Asian markets, underscoring their importance in the region’s financial landscape.
China is currently the fastest-growing economy in the world, solidifying its strong position in global financial markets Additionally, Vietnam shares a border with China, leading to significant trade relations between the two countries that contribute to economic growth and regional cooperation.
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
VNIndex Vietnam Index Vietnam portion of the Vietnamese international trading, so we expect information transmission among China and Vietnam
ASEAN, the ninth largest economy globally, includes Vietnam along with other key markets such as Indonesia, Malaysia, the Philippines, and Singapore As part of this integrated Southeast Asian organization, these countries benefit from increased economic collaboration and proven regional integration, driving sustainable growth across the region.
Basic definition
A stock market index is a vital tool that measures the overall performance of a specific section of the stock market by calculating the weighted average of selected stock prices Investors and financial managers rely on these indexes to assess market trends, gauge the health of the economy, and compare the returns of various investments By providing a snapshot of market activity, stock indices enable informed decision-making and strategic planning in the financial sector.
Most financial studies involve returns, instead of prices, of assets Campbell et al
In 1996, two primary reasons were highlighted for utilizing returns in financial analysis Firstly, for average investors, returns provide a comprehensive and scale-independent summary of an asset’s investment potential Secondly, return series are more manageable than price series due to their favorable statistical properties, making them easier to analyze and interpret for better decision-making.
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:
𝑃 𝑡−1 = ln(𝑃 𝑡 )−ln (𝑃 𝑡−1 ) where 𝑃 𝑡 is the price/index value at time t, and 𝑟 𝑡 is the log return
Volatility is a key statistical measure that quantifies the dispersion of returns for a specific security or market index It is typically calculated using standard deviation or variance to assess how much returns fluctuate over time Generally, higher volatility indicates a riskier investment, as greater price swings can lead to increased uncertainty in future returns Understanding volatility is essential for investors aiming to manage risk and make informed decisions in the financial markets.
Return spillover refers to the phenomenon where the return of one index influences the returns of other indices, causing them to either increase or decrease This interconnectedness highlights how fluctuations in one market can significantly impact other financial instruments Understanding return spillover is essential for investors aiming to manage risk and optimize portfolio performance in a highly interconnected financial landscape.
Volatility spillover refers to the phenomenon where the volatility of one market index's returns influences the volatility of another, potentially increasing or decreasing the targeted index's return volatility This interconnectedness highlights how shocks in one financial asset can transmit across markets, affecting overall market stability and risk levels Understanding volatility spillover is crucial for investors and risk managers aiming to assess and mitigate systemic risks in interconnected financial systems.
Time series consist of a sequence of data points recorded at successive, evenly spaced time intervals In this context, daily closing indices and their corresponding daily returns are analyzed as time series to identify trends and patterns over time.
Time series analysis involves methods for examining time series data to extract valuable statistics and insights This approach helps identify patterns, trends, and characteristics essential for informed decision-making In this study, we utilize time series analysis techniques to address specific research questions, providing a comprehensive understanding of the data's behavior over time.
Time series analysis often encounters challenges such as the presence of a unit root, which can lead to invalid statistical inferences if not properly addressed Ordinary Least Squares (OLS) is commonly used to estimate autoregressive model coefficients, but it assumes that the underlying stochastic process is stationary When the process is non-stationary or contains a unit root, OLS may produce spurious regression results characterized by high R² values and t-ratios that lack economic significance, as highlighted by Granger and Newbold (1974).
When two or more time series are identified as cointegrating, it indicates that they share common stochastic trends and tend to move together over the long term This long-run equilibrium relationship is a key concept in time series analysis For a detailed explanation of cointegration and the corresponding cointegration tests, please refer to Chapter Three.
The Granger causality test, developed by Granger in 1969 and refined in 1988, is a statistical hypothesis test used to determine if one time series can predict another A time series X is said to Granger-cause Y when tests, such as t-tests and F-tests on lagged values of X and Y, show that past values of X provide statistically significant information about future values of Y This method helps identify causal relationships between time series data in forecasting models.
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 review of existing literature relevant to the research topic, highlighting key findings and gaps Chapter three details the methodology used in the study, ensuring transparency and rigor In chapter four, the results are presented and thoroughly discussed, offering insights and implications Finally, chapter five concludes the thesis with a summary of key findings and recommendations.
Literature review
Market integration through price spillover among equity markets has been extensively examined Studies such as Grubel (1968) highlighted the co-movement and correlation between markets from a US perspective to enhance international diversification strategies Eun & Shim (1989) uncovered substantial multi-lateral interactions and transmission mechanisms influencing global stock market movements King & Wadhwani (1990) developed a model suggesting that contagion occurs as rational agents interpret price changes across markets, supported by empirical data Jon (2003) demonstrated the transmission of information from the U.S and Japan to Korean and Thai markets between 1995 and 2000 Additionally, Berben & Jansen (2001) analyzed shifts in correlation patterns among international equity returns at both market and industry levels in Germany, Japan, the UK, and the US from 1980 to 2000, highlighting evolving inter-market dynamics.
The volatility spillovers also gained focus of various authors Hamao, Masulis &
Ng (1990) identified price volatility spillovers from New York to Tokyo, London to Tokyo, and New York to London before October 1987, with no spillover effects detected in other directions Karolyi (1995) analyzed short-term return and volatility dynamics between the New York and Toronto stock exchanges using a multivariate GARCH model, revealing that the perceived magnitude and persistence of return innovations depend significantly on how cross-market volatility dynamics are modeled.
Chelley-Steeley (2000) examined the volatility of global equity markets and found that the correlation between the conditional variances of major equity markets has increased significantly over the past two decades, indicating greater market interconnectedness This rise in market correlation highlights the importance of understanding international equity market dynamics for investors and policymakers seeking to manage risk effectively.
(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
Research by Johnson & Soenen (2002) indicates significant integration among Asian equity markets, particularly highlighting that Australia, China, Hong Kong, Malaysia, New Zealand, and Singapore are highly interconnected with Japan, with this integration increasing notably since 1994 Tatsuyoshi (2003) found that the US market has a substantial impact on Asian market returns, whereas Japan's influence is minimal; however, Japanese market volatility has a stronger impact on Asian markets than the US, and there is evidence of adverse volatility spillovers from Asian markets to Japan.
Singh, Kumar & Pandey (2010) studied price and volatility spillovers across 15 global stock markets using VAR and AR-GARCH models They found that spillover primarily flows from the US to Japanese and Korean markets, then to Singapore and Taiwan, followed by Hong Kong and European markets, before returning to the US The Japanese, Korean, Singapore, and Hong Kong markets were identified as the most influential within the Asian markets.
According to Worthington & Higgs (2004), significant positive mean and volatility spillovers exist between three developed markets (Hong Kong, Japan, Singapore) and six emerging markets (Indonesia, Korea, Malaysia, Philippines, Taiwan, Thailand) However, the spillovers from developed to emerging markets are uneven across the emerging markets, with own-volatility spillovers generally exceeding cross-market spillovers, 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 & Tswei (2007) found that the interdependence of equity market volatility is high among six East Asian markets, with Japan being the most influential in transmitting volatility to others Lee (2009) identified significant volatility spillover effects among six Asian country stock markets—including India, Hong Kong, South Korea, Japan, Singapore, and Taiwan—using the VAR(p)-GARCH(1,1) model, highlighting the interconnected nature of regional stock market volatilities.
Research by Sariannidis, Konteos, and Drimbetas (2010) highlights significant volatility linkages among the Asian stock markets of India, Singapore, and Hong Kong between July 1997 and October 2005, demonstrating strong GARCH effects and high market integration that influence both returns and volatility Giampiero and Edoardo (2008) investigated volatility transmission mechanisms across markets using a Markov Switching bivariate model, revealing long-term market interdependencies characterized by spillovers from Hong Kong to Korea and Thailand, interconnectedness with Malaysia, and synchronized movements with Singapore.
Research by Jang & Sul (2002), In et al (2001), Yilmaz (2010), Alethea et al (2012), Matthew, Wai-Yip Alex & Lu (2010), and Indika, Abbas & Martin (2010) highlights the significant interdependence and volatility spillover among financial markets during periods of financial crisis These studies emphasize how market interconnectedness intensifies during turbulent times, leading to increased risk transmission across assets Understanding these dynamics is crucial for effective risk management and financial stability during crises.
In their 2001 study, et al examined dynamic interdependence, volatility transmission, and market integration across selected Asian stock markets during the 1997-1998 financial crisis using the VAR-EGARCH model The findings revealed that Hong Kong significantly contributed to volatility transmission among Asian markets, highlighting its influential role The study also indicated a high level of market integration, with each market responding to both local and regional news, especially adverse information, during periods of financial turmoil.
Alethea et al (2012) utilized graphical modeling to analyze the spillover effects of returns and volatility among the S&P 500, Nikkei 225, and FTSE 100 stock market indices Their study examined how these major global indices interacted before, during, and after specific market periods, providing insights into cross-market risk transmission and interconnectedness in international financial markets This approach highlights the significance of understanding volatility spillovers for investors and policymakers seeking to manage risk and enhance market stability across different regions.
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 & Lu (2010) examined the spillover effects of financial crises by analyzing the correlation dynamics between eleven Asian and six Latin American stock markets and the US stock market Their study provided evidence of contagion from the US to both regions during the global financial turmoil Despite differing economic, political, and institutional characteristics, the magnitude of contagion affecting both Asia and Latin America was found to be similarly significant during the crisis.
Indika, Abbas, and Martin (2010) explored the relationship between stock market returns and volatility during the Asian (1997-98) and global financial crises (2008-09), focusing on Australia, Singapore, the UK, and the US using the MGARCH model Their study found no significant impact of these crises on stock returns in these markets but revealed that both crises markedly increased stock return volatility across all four markets.
Yilmaz (2010) examined the degree of contagion and interdependence among East Asian equity markets from the early 1990s onward, comparing the ongoing crisis with previous episodes The study highlights that the behavior of return and volatility spillover indices has varied significantly over time Specifically, while the return spillover index indicates increasing integration among East Asian markets, the volatility spillover index shows sharp peaks during major crises, such as the East Asian crisis Both indices reaching their highest levels during the current global financial crisis underscore the severe impact of this episode on regional markets.
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's significant volatility greatly impacted other global markets Notably, the volatility interactions among China, Hong Kong, and Taiwan were more pronounced than those observed among Chinese, Western, and broader Asian markets, highlighting regional differences in market responsiveness during periods of financial turbulence.
Methodology
Data
The index values for the studied markets were obtained from Yahoo! Finance, including daily open and close prices Using these raw data, daily returns were calculated based on the methodology described in the first chapter The analysis covers the period from January 3rd, 2005, to August 30th, 2012.
The model and methods
Before discussing in details each testing method, we present here some of their basic characteristics and their rationales
An Augmented Dickey-Fuller (ADF) unit-root test was conducted to assess the presence of a unit root in all indices and their returns This test is essential because subsequent analyses, such as the Johansen co-integration test, require the data to have the same degree of integration The results confirm whether the time series are stationary or non-stationary, ensuring the validity of further econometric procedures.
- Long-run integration is tested through Johansen co-integration techniques
When two or more time series are found to be cointegrated, it indicates that they share common stochastic trends, meaning they tend to move together in the long run Despite this long-term relationship, these series may experience short-term divergence Recognizing cointegration helps in understanding long-term equilibrium relationships among financial and economic variables, making it a crucial concept in time series analysis and forecasting.
This study examines short-run dynamics using the Granger causality test and the Vector Autoregressive (VAR) model While the Granger causality test identifies directional relationships between variables, it does not specify whether these relationships are positive or negative, which is why the VAR model is employed for further analysis Additionally, the VAR model is used to assess return spillover effects, providing a comprehensive understanding of how returns influence each other in the short run.
- 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
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
Y is considered to be Granger-caused by X when X aids in predicting Y, indicated by statistically significant coefficients on lagged X variables Two-way causation often occurs, where X Granger causes Y and Y also Granger causes X, highlighting the bidirectional relationship between the variables.
Granger causality indicates the precedence and informational relationship between variables but does not necessarily imply a direct cause-and-effect relationship It's essential to understand that the phrase “Granger causes” does not mean that one variable directly results from or is the effect of the other Instead, Granger causality assesses whether past values of one variable can predict future values of another, emphasizing informational content rather than establishing true causality.
To determine whether x Granger-causes y, both being stationary time series, the first step is to identify the appropriate lagged values of y to include in a univariate autoregression of y This process involves testing the null hypothesis that x does not have a causal influence on y, which is fundamental in Granger causality analysis Proper selection of lag lengths ensures accurate assessment of the causal relationship between the two time series data.
Next, the auto regression is augmented by including lagged values of x:
In this regression, all lagged values of x that are individually significant based on their t-statistics are retained, provided they collectively improve the model's explanatory power as determined by an F-test (which tests whether the X variables, together, add significant explanatory power) The null hypothesis that x does not Granger-cause y is accepted only if no lagged values of x are included in the regression, indicating a lack of predictive influence.
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 used method for forecasting interconnected time series and analyzing how random shocks dynamically affect the system of variables This approach simplifies modeling by avoiding the need for structural assumptions, instead modeling each endogenous variable as a function of past values of all variables within the system The VAR(p) model captures these relationships through specific mathematical formulations, enabling comprehensive analysis of multivariate time series data.
𝑦 𝑦 = 𝐴 1 𝑦 𝑡−1 + + 𝐴 𝑝 𝑦 𝑡−𝑝 + 𝐵𝑥 𝑡 + 𝜀 𝑡 where 𝑦𝑡is a k vector of endogenous variables,
𝑥 𝑡 is a d vector of exogenous variables, p is the number of lag,
𝐴 1 , … ,𝐴𝑝 and B are matrices of coefficients, and 𝜀 𝑡 is a vector of innovation
The VAR (k)-BEKK (1, 1) model to estimate volatility spillover is explained below
Let endogenous 𝑌 𝑡 is Nx1 vector with the mean equation:
𝑌 𝑡 =𝐶+𝐵 1 𝑌 𝑡−1 + … + 𝐵 1 𝑌 𝑡−𝑘 +𝐸 𝑡 The error term has multinomial normal distribution as
𝐸 𝑡 |𝜓𝑡−1~ 𝑁(0,𝐻𝑡) The BEKK(p,q) representation of the variance of error term 𝐻 𝑡
Where 𝐴 𝑖 and 𝐵 𝑖 are kxk parameter matrix
𝐶0is kxk upper trangular matrix
Based on the symmetric parameterization of the model, 𝐻𝑡 is almost surely positive definitive provided that A’A is positive definitive The BEKK (p,q) with k variables requires 𝑘 2 (𝑞+𝑞) + 𝑘(𝑘+ 1)/2 parameters which increases rapidly with p and q
The BEKK (p=1, q=1) model with 10 variables requires estimating 255 parameters, making the optimization process complex and time-consuming To simplify the analysis, we employ a bivariate BEKK (1, 1) model, which only requires 11 parameters and is suitable for examining volatility spillovers between two markets The approach for analyzing volatility spillovers among multiple markets is detailed in section 3.2.7.
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 𝑎21 indicates the volatility spillover from Market 2 to Market 1, while 𝑎12 represents the spillover from Market 1 to Market 2 The statistical significance of these parameters highlights the presence and strength of volatility spillovers between the two markets, providing valuable insights into inter-market risk transmission and interconnectedness Understanding these spillover effects is essential for investors and risk managers seeking to optimize portfolio diversification and manage systemic risk effectively.
We utilize a two-stage GARCH model to analyze volatility spillover across all stock market indices, incorporating same-day effects to capture immediate inter-market influences This approach enables us to estimate the partial coefficients of key parameters, providing a deeper understanding of dynamic volatility transmission among global indices.
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
𝜎 𝑗𝑡 2 = 𝛼0+ 𝛼𝑗 ∗ 𝜀𝑡−1 2 + 𝛽𝑗 ∗ 𝜎𝑡−1 2 where𝑟𝑗,𝑡 is the return of the j th index at time t
𝜀 𝑗 is the error or unexpected return of the j th index,
𝜎 𝑗𝑡 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
𝑛=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 𝜑𝑗𝑘 and 𝜑𝑗𝑙 represent the volatility spillover effects from markets k and l to market j These coefficients, along with their statistical significance, are essential indicators of the extent and impact of volatility transmissions between markets Analyzing these values helps identify the strength and direction of volatility spillovers, providing valuable insights for investors and policymakers aiming to understand market interconnectedness and improve risk management strategies.
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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
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Figure 1 Index timings by UTC Time
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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
All skewness and kurtosis values are elevated, and the J-B test statistics are highly significant at the 1% level, except for the VNIndex during the crisis period These results indicate that the return distributions of all assets deviate from normality, highlighting the presence of non-normal distribution characteristics in the data.
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 correlation analysis across ten markets over three different time frames reveals generally low relationships between the VNIndex returns and other Asian markets, with the highest correlation (0.291) observed between VNIndex and PSEI during the crisis period, and the lowest (0.013) between VNIndex and JKSE in the pre-crisis period In contrast, significant correlations are identified among other markets, such as a strong relationship between STI and HIS (0.71) in the pre-crisis period, an increased correlation between STI and HIS (0.741) during the crisis period, and high correlations between STI and HIS (0.736) as well as TWII and HIS (0.647) in the post-crisis period.
In the crisis period all the correlations increase and this phenomenon indicates stronger linkage in term of return during the crisis period
During the post-crisis period, the correlations between VNIndex and six major markets—BSE, JKSE, Nikkei, PSEI, SSE, and TWII—have decreased, indicating a divergence in their movements Conversely, the correlations with the remaining three indices—HIS, KLSE, and STI—continued to increase, suggesting a stronger interconnectedness in these markets after the crisis.
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
Long-run interdependence among financial time series is analyzed using Johansen’s cointegration techniques Before applying cointegration tests, it is essential to perform a unit root test to assess the stationarity of the data This initial step ensures the validity of the subsequent cointegration analysis, which helps identify long-term relationships between variables Properly confirming stationarity is crucial for accurate and reliable results in financial time series analysis.
The ADF test results reveal a unit root in all indices under the assumption of drift without trend, indicating non-stationarity, while the return series are stationary These findings confirm that the indices have one degree of integration, enabling the appropriate application of Johansen’s cointegration test for further analysis.
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
The results presented in Tables 13, 14, and 15 show the test statistics, critical values, and p-values for both the trace test and maximum eigenvalue test, evaluating the long-term interdependence between VNIndex and other indices Since all series exhibit one degree of cointegration, two null hypotheses are tested: (a) no cointegrating vector and (b) at most one cointegrating vector The analysis confirms the presence of at least one long-run equilibrium relationship, with clear conclusions marked for easy interpretation, highlighting significant interdependence among the indices.
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(𝛑) = 1 or there is 1cointegrating vector, versus
H1: Rank(𝛑) > 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:Rank(𝛑) = 0 or there is no cointegrating vector, versus
H1: Rank(𝛑) > 0 or there is more than 0 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:Rank(𝛑) = 1 or there is 1 cointegrating vector, versus
H1:Rank(𝛑) = 2 or there is 2 cointegrating vector
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:Rank(𝛑) = 0 or there is 0 cointegrating vector, versus
H1:Rank(𝛑) = 1 or there is 1 cointegrating vector
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 demonstrates cointegration with eight of the studied markets, excluding the Nikkei, which shows cointegration only at the 10% significance level During the crisis period, evidence indicates a strong tendency toward cointegration between the VNIndex and these markets, highlighting their interconnected movements in turbulent times.
- 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: firstly, the recent crisis has strengthened cointegration between the Vietnamese stock market and other international markets Secondly, the VNIndex is exhibiting increased cointegration with global markets Despite these developments, current cointegration levels remain relatively low, indicating potential long-term benefits from diversifying investment portfolios across different markets.
Table 13 Johansen's cointegration test for pre-crisis period
Hypothesized Eigen value Trace Max-Eigen Conclusion
No of CE(s) Statistic Critical Val Prob Statistic Critical Val Prob
Table 14 Johansen's cointegration test for crisis period
Hypothesized Eigen value Trace Max-Eigen Conclusion
No of CE(s) Statistic Critical Val Prob Statistic Critical Val Prob
Table 15.Johansen's cointegration test for post-crisis period
Hypothesized Eigen value Trace Max-Eigen Conclusion
No of CE(s) Statistic Critical Val Prob Statistic Critical Val Prob
Short-run interdependence
Cointegration signifies a long-term relationship between stochastic variables, highlighting their tendency to move together over time However, even if two time series are not cointegrated in the long run, there may still be short-term causal inter-relationships that influence their dynamics Understanding both long-term cointegration and short-term causality is essential for comprehensive time series analysis and forecasting.
We analyze short-run interdependence between Vietnamese markets and other markets through the Granger causality analysis and bi-variate model
The Granger causality test was conducted with four lags to examine the relationship between VNIndex returns and other index returns The results, detailed in Tables 16, 17, and 18, present the F-statistics and corresponding probability values for each direction of causality, providing insights into whether past values of one index can predict future returns of the other.
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 with a p-value of 0.6847, indicating that at the 5% significance level, we cannot reject the null hypothesis of no Granger causality Therefore, the results suggest that VNIndex returns do not Granger cause the index’s returns.
- 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 the 5% significance level, we can reject the null hypothesis of no Granger causality These results demonstrate that STI returns significantly Granger cause Index returns, suggesting that STI returns can be used to predict 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
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
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 discussed earlier reveals the interdependence among endogenous variables but does not measure the strength of these relationships Additionally, it does not indicate whether the dependencies are positive or negative, highlighting the need for further analysis to understand the nature of these interactions.
The VAR model is widely used to assess the strength and direction of cross-correlation between financial returns In this study, a bivariate VAR model with five lags was applied to analyze the relationship between VNIndex returns and those of other market indices The results provide insights into the dynamic interactions and potential influence of different indices on VNIndex performance, highlighting the importance of VAR analysis for understanding market interconnectedness.
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:
∗ 𝑅𝑉𝑁𝐼𝑛𝑑𝑒𝑥(𝑡−4) + 0.135192 ∗ 𝑅𝑉𝑁𝐼𝑛𝑑𝑒𝑥(𝑡−5) where 𝑅𝑉𝑁𝐼𝑛𝑑𝑒𝑥(𝑡), 𝑅𝐵𝑆𝐸(𝑡) is the return of VNIndex and BSE at time t respectively
The coefficients of the parameters RKLSE(t−1) and RVNIndex(t−1) in the regression model are statistically significant at the 5% level This indicates that the VNIndex return at time t is influenced by the KLSE return at time t−1, highlighting a significant dependence between these two indices.
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 four indices—HIS, JKSE, KLSE, and STI—significantly influence the conditional return of the VNIndex Return spillovers from these markets to the Vietnamese market are exclusively positive, indicating that positive or negative returns in these markets tend to have a corresponding positive or negative impact on the VNIndex This highlights the interconnected nature of regional markets and their effect on Vietnam's stock market performance.
- 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 findings reveal that both the Granger causality test and the VAR model identify significant return spillovers from the studied markets to the Vietnamese stock market, particularly during periods of crisis However, in the post-crisis period, the Vietnamese market's returns show no dependence on other markets, indicating a decoupling Additionally, there is no evidence of return spillovers from Vietnam to other markets, suggesting that the Vietnamese stock market operates independently in the analyzed timeframe.
Our study’s finding of high return spillover during the crisis period aligns with existing research, such as Johansson (2010), who identified increased financial market integration and heightened comovements during international financial turmoil in East Asia and Europe Additionally, Yilmaz (2010) reported that return spillovers in the East Asia region peaked during the 2008 global financial crisis, highlighting the intensified interconnectedness of global markets during periods of crisis.
Table 19 Bivariate VAR Model (VNIndex and other Indices) estimates of model on indices return in pre-crisis period
Dependent Variable Parameter BSESN HIS JKSE KLSE NIKKEI PSEI SSE STI TWII
Constant 0.001506 0.000972 0.001289 0.000562 0.000355 0.000866 0.001791 0.000631 0.000445 INDEX(-1) 0.069850 0.017874 0.079472 0.182415* 0.013077 0.063158 -0.007988 -0.026952 0.017970 INDEX(-2) -0.047621 -0.102363 -0.055353 -0.046948 -0.047579 0.009260 -0.035962 -0.074538 -0.012054 INDEX(-3) -0.026521 0.118912 0.062134 0.102227 0.055249 -0.015830 0.057584 0.047531 0.107811 INDEX(-4) 0.044215 0.030787 0.014689 -0.034647 -0.048945 0.046173 0.061113 0.060276 -0.045720 INDEX(-5) 0.005935 -0.069735 -0.009713 -0.086894 0.054467 -0.060741 -0.001040 -0.040077 -0.043910 VNINDEX(-1) -0.032774 -0.020200 -0.005666 -0.011817 -0.033183 -0.046970 -0.041528 -0.014677 -0.009130 VNINDEX(-2) 0.018690 0.031146 -0.000738 0.017217 0.043609 0.057801 0.016518 0.031753 0.004124 VNINDEX(-3) 0.012946 -0.033675 -0.005644 -0.006833 -0.017290 0.032282 0.023978 0.002327 -0.008732 VNINDEX(-4) 0.003331 0.007732 -0.007485 -0.001650 0.004269 -0.081458 -0.025466 0.011549 0.018352 VNINDEX(-5) -0.037819 -0.007984 -0.049031 -0.022990 0.009311 0.021683 -0.015315 -0.002848 -0.001218
Constant 0.001143 0.001151 0.001044 0.001061 0.001175 0.001154 0.001080 0.001098 0.001176 INDEX(-1) 0.080009 0.152534* 0.134334* 0.274048* 0.072588 0.073553 0.041122 0.230860* 0.150622 INDEX(-2) -0.033745 -0.093921 -0.079569 -0.082316 -0.078406 -0.113554 0.002782 -0.068305 -0.070297 INDEX(-3) 0.009854 0.006222 0.045404 -0.024840 0.074166 -0.000804 0.028995 0.002958 -0.004298 INDEX(-4) -0.034979 -0.053098 -0.011836 0.016498 0.012999 0.025874 -0.011272 -0.027774 -0.028555 INDEX(-5) 0.024822 0.049128 0.039276 0.074666 0.075648 0.071072 0.013234 0.062251 0.055901 VNINDEX(-1) 0.192895* 0.192183* 0.198050* 0.189254* 0.189438* 0.187981* 0.189665* 0.188460* 0.183938* VNINDEX(-2) -0.056545 -0.051854 -0.061801 -0.050970 -0.053126 -0.044113 -0.058253 -0.050324 -0.050007 VNINDEX(-3) -0.015027 -0.018653 -0.013958 -0.022831 -0.026566 -0.021988 -0.016813 -0.021747 -0.015864 VNINDEX(-4) 0.069593 0.076445 0.069364 0.069601 0.076704 0.069961 0.071838 0.066343 0.071228 VNINDEX(-5) 0.130306* 0.126846* 0.132583* 0.135192* 0.121569* 0.136764* 0.130605* 0.129499* 0.126155*
* 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(-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 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(-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 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 [Index, VNIndex] highlight key parameters that capture volatility dynamics The parameter 𝑎12 represents the volatility spillover from the Index to VNIndex, while 𝑎21 reflects the volatility transfer from VNIndex to the Index Additionally, parameters 𝑎11 and 𝑎22 measure the impact of residuals (ARCH effects) on the conditional variance, indicating how recent shocks influence volatility Parameters 𝑏11 and 𝑏22 denote the influence of past variances (GARCH effects) on current conditional volatility, emphasizing the persistence of volatility over time.
We summarize the results from the bivariate BEKK model as below:
The three indices HIS, JKSE, and PSEI significantly influence the conditional volatility of Vietnamese markets, with the parameter (𝑎 12) being significant at the 5% level A positive relationship exists between JKSE and PSEI and Vietnamese market volatility, indicating that higher volatility in JKSE and PSEI tends to reduce volatility in the Vietnamese stock market Conversely, the HIS index exhibits a negative effect on Vietnamese market volatility, highlighting different market dynamics These findings underscore the interconnectedness of regional markets and their impact on Vietnam's stock market stability.
(𝑎 12 0) on JKSE and negative effect (𝑎 21 0), indicating that increases in these indices lead to higher volatility in Vietnamese markets Conversely, the SSE index exhibits a negative effect on Vietnamese market volatility, suggesting an inverse relationship These findings highlight the interconnectedness of Asian and global markets and their impact on Vietnam's financial stability.
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 (𝑎 12 ) is significant at 5%; and all the effects from these markets are negative (𝑎 12 < 0)
- The volatility spillover from Vietnamese stocks market has positive affect to Nikkei (𝑎 21 > 0)
During crisis periods, volatility spillovers become increasingly significant, impacting the Vietnamese stock market differently across timeframes Specifically, the conditional variance of the Vietnamese stock market is influenced by three markets pre-crisis, five markets during the crisis, and four markets post-crisis Additionally, these spillovers help explain the conditional volatility of two markets pre-crisis, three markets during the crisis, and one market post-crisis, highlighting the evolving interconnectedness and risk transmission in Vietnamese equities throughout different stages of economic turbulence.
We also learn about the components of the conditional variance of markets - the ARCH and the GARCH:
The ARCH components, reflected through the A(1,1) and A(2,2) coefficients, represent the residuals that capture the relationship between current price variations and past price movements This component highlights how past innovations influence present-day price fluctuations, emphasizing the persistence of volatility over time.
- 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
Volatility spillovers estimated through BEKK (1,1) do not capture the partial effects of indices nor account for same-day impacts To address this, we utilize a univariate GARCH model to estimate the partial effects of indices and the same-day effect, as discussed previously The parameter estimation results for these effects across three different periods are systematically presented in Tables 25, 26, and 27.
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 model analysis of the Vietnamese stock market reveals that its volatility is influenced by two external markets: a positive effect from the Singapore STI index and a negative effect from the HIS market Both coefficients are statistically significant at the 5% level, indicating a strong relationship Specifically, increased volatility in the STI tends to elevate Vietnamese market volatility, while higher volatility in the HIS market has a dampening effect This insight underscores the interconnected nature of regional markets and their impact on Vietnam's stock market stability.
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 influenced by four key markets It shows a negative dependence on the KLSE, PSEI, and SSE, while demonstrating a positive correlation with TWII, highlighting increased interconnectedness among these markets.
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 more significant during crisis periods, with the Vietnamese stock market's conditional variances influenced by two markets pre-crisis, four during the crisis, and one post-crisis These spillovers contribute to explaining the volatility across one market pre-crisis, three during the crisis, and none post-crisis, highlighting the increased interconnectedness and impact of external shocks during turbulent times.
Our results are similar with findings of other authors: the study of Andrew Stuart
Global volatility linkages tend to intensify during major financial crises, as highlighted by Alain (2011), who notes particularly strong connections during the Asian financial crisis (1997-1998), the Russian crisis (1998), and the U.S financial crisis (2007-2008) Additionally, Indika, Abbas, and Martin (2010) found that the Asian and global financial crises of 1997-1998 and 2008-2009 significantly increased stock return volatility across multiple markets, including Australia and Singapore These crises underscore the heightened interconnectedness of global markets during turbulent periods, leading to increased uncertainty and risk across financial systems worldwide.
UK, and the US Yilmaz (2010) argued that the volatility spillover index experiences significant bursts during major market crises, including the East 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
This chapter highlights that international investors can leverage the Vietnamese stock market to achieve long-term diversification benefits The VNIndex demonstrates low correlation and weak co-integration with other studied markets, indicating limited interconnectedness Additionally, there are minimal return and volatility spillovers between Vietnam and other markets, which enhances diversification advantages These factors collectively help reduce investment risks and optimize portfolio performance in the long run.
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 study examines the interdependence between the Vietnam Index and nine other Asian Indices, focusing on return and volatility spillover effects across three periods: pre-crisis, crisis, and post-crisis The analysis highlights how market shocks influence the interconnectedness of Asian financial markets over different economic phases Findings reveal significant spillover effects in both returns and volatility, underscoring the importance of understanding these linkages for investors and policymakers in maintaining financial stability The results contribute to the literature on regional market integration and provide insights for risk management during times of economic turbulence.
Although the correlations between the Vietnamese stock market and other markets remain relatively low, they have been gradually increasing over time During times of crisis, these correlations reach their peak, highlighting a stronger linkage and greater integration of the Vietnamese stock market with global markets This trend suggests an evolving interconnectedness that amplifies during market downturns.
During the pre-crisis period, the Vietnamese stock market was not cointegrated with any other markets However, during the crisis period, it became cointegrated with almost all markets, highlighting increased interconnectedness In the post-crisis period, Vietnam's stock market continued to show strong relationships, co-integrating with two additional markets These findings demonstrate that the crisis significantly impacts market integration, causing the Vietnamese market to become more interconnected with global markets during turbulent times.
The Granger causality test and the VAR model demonstrate significant return spillovers from the studied markets to the Vietnamese stock market, particularly during periods of financial crisis However, in the current period, the VNIndex returns appear independent and are not influenced by other markets Additionally, there is no evidence of return spillovers originating from Vietnam to other markets in any period analyzed, indicating a shift in market dynamics over time.
The study on volatility spillovers reveals that market volatility is more influenced by its past lags than by new innovations Vietnamese stock market shows some level of integration with other markets regarding volatility spillovers Additionally, volatility spillovers tend to be stronger during crisis periods, indicating heightened interconnectedness during times of financial stress.
During a crisis, market interdependence significantly increases as markets become more integrated, exhibiting higher correlations and stronger cointegration This heightened integration leads to increased spillover effects, both in terms of returns and volatility, highlighting the interconnected nature of global markets during turbulent periods.
Foreign investors recognize that Vietnam's stock market demonstrates long-term independence in post-crisis periods, indicating potential benefits from portfolio diversification into Vietnamese stocks The VNIndex has shown minimal long-term movement correlation with studied markets, suggesting limited return and volatility spillover from other markets This decoupling makes Vietnamese stocks an attractive diversification opportunity for global investors seeking to mitigate risks and enhance long-term returns.
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