Introduction
Background
The globalization of domestic markets is increasingly evident, as equity markets now attract capital from both domestic and international investors seeking to mitigate risk through diversification This trend diminishes the isolation of domestic markets, enabling them to respond swiftly to international news and economic shocks.
Information transmission in markets has been extensively analyzed from two key perspectives Firstly, the long-term interdependence and causality among markets serve as significant indicators of information flow Secondly, the increasing focus on volatility transmission highlights its importance as a crucial 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 become an attractive destination for valuable investments Despite its growth, there is a notable lack of research on the connections between the Vietnamese equity market and international markets, particularly within Asia.
Purpose and scope
This study explores the price and volatility spillover interactions between the Vietnamese equity market and nine other Asian markets, including India, Hong Kong, Indonesia, Malaysia, Japan, the Philippines, China, Singapore, and Taiwan.
This study analyzes return spillovers utilizing Johansen co-integration to assess long-term spillovers and the Granger causality test for short-term spillovers Additionally, the bivariate BEKK and AR-GARCH models are employed to evaluate volatility spillovers effectively.
This study examines 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 analyzing these timeframes, the research aims to highlight the impact of the financial crisis on 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
The selected markets represent both developed and emerging Asian economies, highlighting their potential impact on the Vietnamese stock market Additionally, the chosen indices serve as widely recognized benchmark indices.
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, solidifying its position in the financial market Additionally, Vietnam, sharing a border with China, has seen significant growth in trade relations with its neighbor.
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
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 is a crucial tool for measuring the value of a segment of the stock market, calculated from the prices of selected stocks, often using a weighted average Investors and financial managers utilize stock indices to assess market performance and compare the returns on specific investments.
Most financial studies involve returns, instead of prices, of assets Campbell et al
In 1996, it was highlighted that returns serve two primary purposes for average investors Firstly, the return of an asset provides a comprehensive and scalable overview of the investment opportunity Secondly, return series are simpler to analyze compared to price series, as they exhibit more favorable 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:
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 quantifies the variation in returns for a specific security or market index, serving as a key statistical indicator It is typically assessed through standard deviation or variance, reflecting the degree of dispersion in the returns associated with that security or index.
Commonly, the higher the volatility, the riskier the security
Return spillover refers to the phenomenon where the return of one index influences the returns of other indices, potentially causing them to increase or decrease.
Volatility spillover refers to the phenomenon where the fluctuations in one index's returns can influence the volatility of another index's returns, potentially leading to an increase or decrease in the targeted index's volatility.
Time series refers to a sequence of data points collected at uniform time intervals This thesis focuses on analyzing daily indices' closing values and their corresponding daily returns as time series data.
Time series analysis involves various techniques for examining time series data to derive significant statistics and insights This research delves into time series analysis to address the questions outlined in this section.
Time series analysis presents challenges, particularly with the presence of unit roots, which can hinder accurate statistical inference if not properly addressed The ordinary least squares (OLS) method is commonly employed to estimate slope coefficients in auto-regressive models, but its effectiveness depends on the stochastic process being stationary When the process is non-stationary or exhibits a unit root, OLS may yield invalid estimates, leading to unreliable conclusions.
Newbold (1974) called such estimates spurious regression results: high R2 values and high t-ratios yielding results with no economic meaning
When multiple series exhibit cointegration, they share common stochastic trends, indicating that they will move together over the long term A detailed exploration of cointegration and its testing methods is provided in Chapter Three.
The Granger causality test, developed by Granger in 1969 and further refined in 1988, is a statistical hypothesis test used to assess whether one time series can predict another Specifically, a time series X is considered to Granger-cause Y if it can be demonstrated, typically through t-tests and F-tests involving lagged values of both X and Y, that past values of X offer statistically significant insights into the 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 review of 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 co-movement and correlation among 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 multilateral interactions among national markets King & Wadhwani (1990) developed a model illustrating how "contagion" occurs as rational agents interpret price changes in other markets, providing evidence for these contagion effects through diverse data sources Jon (2003) demonstrated the transmission of information from the U.S and Japan to Korean and Thai equity markets from 1995 to 2000 Berben & Jansen (2001) analyzed shifts in correlation patterns among international equity returns at both market and industry levels across Germany, Japan, the UK, and the U.S from 1980 to 2000.
The volatility spillovers also gained focus of various authors Hamao, Masulis &
Ng (1990) identified significant price volatility spillovers from New York to Tokyo, London to Tokyo, and New York to London during the pre-October 1987 period, while no spillover effects were detected in the opposite directions.
Karolyi (1995) analyzed the short-run dynamics of returns and volatility for stocks on the New York and Toronto stock exchanges using a multivariate GARCH model The study revealed that the magnitude and persistence of return innovations from one market to the other are significantly influenced by the modeling of cross-market volatility dynamics.
Chelley-Steeley (2000) conducted a study on equity market volatility across different countries, revealing a significant increase in the correlation of conditional variances among major equity markets over the past two decades.
(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
In their study, Johnson & Soenen (2002) revealed that equity markets in Australia, China, Hong Kong, Malaysia, New Zealand, and Singapore are highly integrated with Japan's stock market, showing increased integration since 1994 Additionally, Tatsuyoshi (2003) found that while the US significantly impacts returns in seven Asian equity markets, Japan's influence is negligible However, Japanese market volatility has a more substantial effect on Asian markets than the US, and there is a negative volatility spillover from Asian markets to Japan.
Singh, Kumar, and Pandey (2010) conducted a study on price and volatility spillovers among 15 stock markets across North America, Europe, and Asia, utilizing a VAR model for returns and an AR-GARCH model for volatility Their findings revealed that the primary direction of return and volatility spillovers originated from the US market, affecting the Japanese and Korean markets first, followed by Singapore and Taiwan, and then extending to Hong Kong and Europe before cycling back to the US Notably, they identified the Japanese, Korean, Singapore, and Hong Kong markets as the most influential within the Asian region.
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 spillovers from developed to emerging markets vary across the latter, and generally, the own-volatility spillovers exceed cross-volatility 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, and Tswei (2007) explored the interdependence of volatility in 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 Similarly, Lee (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) investigated the volatility linkages among the Indian, Singaporean, and Hong Kong stock markets from July 1997 to October 2005, revealing a strong GARCH effect and high market integration, with information impacting both mean returns and volatility Giampiero and Edoardo (2008) examined the transmission mechanisms of volatility using a Markov Switching bi-variate model, finding significant long-term market dynamics, including spillovers from Hong Kong to Korea and Thailand, interdependence with Malaysia, and co-movement with Singapore.
Other authors including Jang & Sul (2002), In et al (2001), Yilmaz (2010), Alethea et al (2012), Matthew, Wai-Yip Alex & Lu (2010), Indika, Abbas &
Martin (2010), concentrated the interdependence and volatility spillover during financial crisis periods
In their 2001 study, et al analyzed dynamic interdependence, volatility transmission, and market integration among selected stock markets during the Asian financial crisis of 1997-1998 using the VAR-EGARCH model Their findings revealed that Hong Kong significantly influenced volatility transmission to other Asian markets Additionally, the data demonstrated that market integration was present, 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, focusing on the spillover effects of returns and volatility among these significant global markets during three distinct periods: before, during, and after a financial event.
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 in relation to the US stock market Their study revealed a significant contagion effect from the US market to both regions during the global financial crisis Notably, the intensity of this contagion was comparable for both regions, despite their distinct economic, political, and institutional characteristics.
Indika, Abbas, and Martin (2010) investigated the relationship between stock market returns and volatility during the Asian and global financial crises of 1997-98 and 2008-09, specifically analyzing 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 significantly impact stock returns in these markets However, both crises led to a notable increase in 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 a significant difference in the behavior of return and volatility spillover indices over time While the return spillover index indicates increased integration among East Asian markets, the volatility spillover index shows sharp spikes during major crises, including the East Asian crisis Notably, both indices peaked during the ongoing global financial crisis, underscoring its severity.
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 in global markets, particularly affecting China, Hong Kong, and Taiwan The interactions of volatility among these Asian markets were more pronounced compared to those observed between Chinese markets and their Western and other Asian counterparts.
Methodology
Data
The index values for the analyzed markets were sourced from Yahoo! Finance, encompassing the opening and closing 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 essential for assessing the existence of unit roots in all indices and their returns This test is crucial because subsequent tests, such as the Johansen co-integration test, necessitate a consistent degree of integration.
- 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 will move together over the long term, although they may diverge in the short term.
The study analyzes short-run dynamics using the Granger causality test and the Vector Autoregressive (VAR) model While the Granger causality test identifies relationships between endogenous variables, it does not specify their directionality; this aspect is further explored through the VAR model Additionally, return spillover effects are assessed using the VAR framework.
- 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 said to be Granger-caused by X if X helps in the prediction of Y, or equivalently if the coefficients on the lagged X’s are statistically significant
Two-way causation is frequently the case; X Granger causes Y and Y Granger causes X
The phrase "Granger causes" should not be misunderstood as implying a direct effect or outcome Instead, Granger causality focuses on the precedence and information content between variables, and 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.
𝑦 𝑡 =𝑎 0 +𝑎 1 𝑦 𝑡−1 + 𝑎 2 𝑦 𝑡−2 + … + 𝑎 𝑚 𝑦 𝑡−𝑚 + 𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝑡 Next, the auto regression is augmented by including lagged values of x:
In this regression analysis, only the lagged values of x that show individual significance based on their t-statistics are retained, as long as they collectively enhance the regression's explanatory power according to an F-test The null hypothesis, which states that x does not Granger-cause y, is accepted 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 variable systems By treating 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(p) is:
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, which involves 10 variables, necessitates 255 parameters, making optimization challenging To simplify this process, we utilize the bivariate BEKK (1, 1) model, which only requires 11 parameters to analyze volatility spillovers between two markets Further details on managing volatility spillovers among multiple markets can be found 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 model of volatility, the parameter \( a_{21} \) represents the volatility spillover from market 2 to market 1, while \( a_{12} \) indicates the spillover from market 1 to market 2 The statistical significance of these parameters reveals the extent of volatility spillover between the two markets.
We utilize a two-stage GARCH approach to analyze 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
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 from markets k and l to market j, respectively The values and statistical significance of these coefficients offer valuable insights into the dynamics of volatility spillovers among the markets.
The GARCH variance equation for the VNIndex incorporates same-day residuals from three indices—Nikkei, SSE, and TWII—anticipating volatility spillovers within the same trading day In contrast, six other indices—BES, HIS, JKSE, KLSE, PSE, and STI—open and close after the VNIndex, suggesting that any potential volatility spillovers would manifest the following day; thus, the equation utilizes one lagged day residuals from these indices.
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 essential for assessing their potential impact on one another within the same day or the following day For instance, the Nikkei index, which opens and closes earlier than the VNIndex, can influence the VNIndex on the same day Conversely, any effects of the VNIndex on the Nikkei may be observed with a one-day delay.
Figure 1 Index timings by UTC Time
BSESNHISJKSEKLSENikkei 225PSEISSESTITWIIVNIndex
Tables 2, 3, and 4 display the descriptive statistics for the analyzed indices, revealing that skewness and kurtosis values are notably high The Jarque-Bera (J-B) test results are highly significant at the 1% level, except for the VNIndex during the post-crisis period, suggesting that the price distributions of all indices deviate from normality.
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 measures of skewness and kurtosis show elevated values, and the Jarque-Bera (J-B) test statistics are highly significant at the 1% level, except for the VNIndex during the crisis period This indicates that the distribution of returns for all assets 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 correlation matrix across ten markets in three distinct time frames reveals low correlations between the VNIndex returns and those of other Asian markets Notably, the highest correlation of 0.291 occurs with the PSEI during the crisis period, while the lowest correlation of 0.013 is observed with the JKSE in the pre-crisis periods.
In the pre-crisis period, strong correlations were observed between various markets, with the STI and HIS showing a correlation of 0.71, while the STI and Nikkei had a correlation of 0.581 During the crisis, the correlation between STI and HIS increased to 0.741 In the post-crisis period, the STI and HIS maintained a correlation of 0.736, and the TWII and HIS exhibited 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 the VNIndex and six major indices—BSE, JKSE, Nikkei, PSEI, SSE, and TWII—has diminished, while correlations with three other indices, HIS, KLSE, and STI, continue 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 Initially, a unit root test is conducted to assess the stationarity of the time series data, which is a crucial step in the analysis process.
The ADF test results indicate that while all indices exhibit a unit root under the assumption of drift without trend, all return series are stationary This suggests a single degree of integration in the indices, allowing for the appropriate 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, examining the long-run interdependence between the VNIndex and other indices Given that all series exhibit one degree of cointegration, we test two null hypotheses: a) the absence of a co-integrating vector and b) the existence of at most one co-integration vector Additionally, conclusions are highlighted 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(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 shows cointegration with eight analyzed markets, excluding the Nikkei; however, cointegration between the VNIndex and Nikkei is observed at a 10% significance level Notably, there is a strong tendency for 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: firstly, the crisis has enhanced the cointegration between the Vietnamese stock market and other global markets; secondly, the VNIndex is increasingly showing stronger cointegration with these markets Despite this improvement, the overall cointegration remains relatively low, suggesting that there are potential long-term advantages in 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; however, two time series may not be cointegrated in the long run while still exhibiting a short-term causal interrelationship.
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 a 5% significance level, we cannot reject the null hypothesis of no Granger causality Consequently, 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 is 4.71684 with a p-value of 0.009, indicating that at a 5% significance level, we can reject the null hypothesis of no Granger causality Consequently, we conclude that STI returns Granger cause the returns of the VNIndex, suggesting that STI returns are useful in 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
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, as discussed in the previous section, highlights the interdependence of endogenous variables; however, it does not measure the strength of these relationships or specify whether the dependencies are positive or negative.
The VAR model is commonly utilized 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:
∗ 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) 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, return spillover from these markets to the Vietnamese market is exclusively positive, indicating that both positive and 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 various markets to the Vietnamese stock market during crisis periods However, in the post-crisis phase, the returns of the Vietnamese market are independent of other markets Additionally, there is no evidence of return spillovers originating from Vietnam to other markets.
Our research reveals significant return spillover during crisis periods, aligning with findings from Johansson (2010), who noted heightened financial market integration and increased comovements in East Asia and Europe amid international financial turmoil Additionally, Yilmaz (2010) highlighted that return spillovers in the East Asia region peaked during the global financial crisis of 2008.
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 provides estimates for the two time series, Index and VNIndex, highlighting key parameters that influence volatility dynamics The parameters 𝑎12 and 𝑎21 are crucial, with 𝑎12 indicating the volatility spillover from the Index to VNIndex, while 𝑎21 captures the volatility transmission from VNIndex to the Index Additionally, parameters 𝑎11 and 𝑎22 reflect the impact of residuals on conditional variance, and parameters 𝑏11 and 𝑏22 demonstrate how previous volatility affects the current conditional variance.
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 ) showing significance at 5% Notably, both JKSE and PSEI have a positive effect on Vietnamese market volatility, indicating that increased volatility in these markets leads to reduced volatility in Vietnam Conversely, the HIS index exhibits a negative effect on the Vietnamese stock market's volatility.
(𝑎 12 0) on JKSE and negative effect (𝑎 21 0), while the SSE demonstrates a negative effect on the volatility.
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 periods of crisis, the significance of volatility spillovers increases, with the conditional variances of the Vietnamese stock market being influenced by three, five, and four markets in the pre-crisis, crisis, and post-crisis phases, respectively Additionally, these markets contribute to explaining the conditional volatility of two, three, and one markets during the same periods.
We also learn about the components of the conditional variance of markets - the ARCH and the GARCH:
The ARCH components, represented by the coefficients A(1, 1) and A(2, 2), illustrate the relationship between today's price variation and that of the previous day, highlighting the influence of past innovations on current price movements.
- GARCH components (reflected via the B (1, 1) and B (2, 2) coefficient): the previous volatility
In all three periods analyzed, the GARCH effect coefficient significantly exceeds the ARCH coefficient, suggesting that volatility is more influenced by its past values than by new information.
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 volatility spillovers analyzed using the BEKK (1, 1) model fail to capture the partial effects of indices and the same-day impact To address this, we employ a univariate GARCH model to estimate these effects The detailed results of the parameters are displayed in Tables 25, 26, and 27 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 analysis of the GARCH equation for 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 coefficients are statistically significant at the 5% level, indicating that increased volatility in the STI leads to higher volatility in the Vietnamese stock market, while increased volatility in the HIS results in decreased 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 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 are notably more significant during crisis periods In the Vietnamese stock market, the conditional variances are influenced by two markets before the crisis, four markets during the crisis, and one market after the crisis Additionally, these variances contribute to explaining the volatility of one market pre-crisis, three markets during the crisis, and none post-crisis.
Our results are similar with findings of other authors: the study of Andrew Stuart
Global volatility linkages were notably heightened during significant financial crises, including those in Asia (1997-1998), Russia (1998), and the United States (2007-2008), as highlighted by Alain (2011) Research by Indika, Abbas, and Martin (2010) revealed that both the Asian financial crisis of 1997-1998 and the global crisis of 2008-2009 led to a substantial increase in stock return volatilities across four key 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 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
The findings indicate that international investors can benefit from investing in the Vietnamese stock market for long-term portfolio diversification The VNIndex shows a low correlation with returns from other studied markets, and there are minimal co-integrations between them Additionally, the low return and volatility spillovers between the Vietnamese market and others further enhance diversification benefits, ultimately reducing investment risk.
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 increasing correlations with other markets, particularly during crisis periods 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 other markets during the pre-crisis period; however, it became cointegrated with nearly all markets during the crisis and maintained cointegration with two additional markets in the post-crisis phase This trend highlights the significant influence of the crisis, which prompted increased interconnectedness among the markets.
The Granger causality test and VAR model reveal significant return spillovers from various markets to the Vietnamese stock market, particularly during crisis periods However, in the current timeframe, the VNIndex returns appear to be independent of any external market influences Additionally, there is no evidence of return spillovers originating from Vietnam in any observed period.
The study on volatility spillovers reveals that market volatilities are more influenced by their past values than by new information Additionally, the Vietnamese stock market shows some integration with other markets regarding volatility spillover effects Notably, these spillovers tend to intensify during periods of crisis.
The crisis significantly enhances market interdependence, leading to greater integration among markets During such periods, correlations increase, resulting in higher cointegration and intensified 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 appears to operate independently from other studied markets This independence suggests minimal influence from return and volatility spillovers, indicating a unique investment opportunity in the current economic landscape.
Alethea, R., William, R., Marco, R & Carl, S 2012, A comparison of Spillover Effects before, during and after the 2008 Financial Crisis, University of Canterbury,
Department of Economics and Finance
Andrew Stuart, D & Alain, K 2011, Global Financial Crises and Time-varying
Volatility Comovement in World Equity Markets, Economic Research Southern
Baele, L 2003, 'Volatility Spillover Effects in European Equity Markets'
Berben, R.P & Jansen, W.J 2001, 'Comovement in International Equity Markets: a
Campbell, J.Y., Lo, A.W., MacKinlay, A.C & Lo, A.Y 1996, The Econometrics of
Financial Markets Princeton University Press
Chelley-Steeley, P.L 2000, 'Interdependence of International Equity Market Volatility',
Applied Economics Letters, vol 7, no 5, pp 341-45
Chuang, I.Y., Lu, J.-R & Tswei, K 2007, 'Interdependence of international equity variances: Evidence from East Asian markets', Emerging Markets Review, vol
Dickey, D.A & Fuller, W.A 1979, 'Distribution of the Estimators for Autoregressive
Time Series With a Unit Root', Journal of the American Statistical Association, vol 74, no 366
Eun, C.S & Shim, S 1989, 'International Transmission of Stock Market Movements',
Journal of Financial and Quantitative Analysis, vol 24, no 02, pp 241-56
Gamini, P & Lakshmi, B 2004, 'Stock Market Volatility: Examining North America,
Giampiero, G & Edoardo, O 2008, 'Volatility spillovers, interdependence and comovements: A Markov Switching approach', Computational Statistics & Data
Granger, C.W.J 1969, 'Investigating Causal Relations by Econometric Models and
Cross-Spectral Methods', Econometrica, vol 37, no 3, pp 424-38
Granger, C.W.J 1988, 'Some recent development in a concept of causality', Journal of
Granger, C.W.J & Newbold, P 1974, 'Spurious regressions in econometrics', Journal of Econometrics, vol 2, no 2, pp 111-20
Grubel, H 1968, 'Internationally Diversified Portfolios: Welfare Gains and Capital
Flows', American Economic Review, no 58, pp 1299-314
Hamao, Y., Masulis, R.W & Ng, V 1990, 'Correlations in Price Changes and Volatility across International Stock Markets', Review of Financial Studies, vol 3, no 2, pp 281-307
In, F., Kim, S., Yoon, J.H & Viney, C 2001, 'Dynamic interdependence and volatility transmission of Asian stock markets: Evidence from the Asian crisis',
International Review of Financial Analysis, vol 10, no 1, pp 87-96
Indika, K., Abbas, V & Martin, O.B 2010, 'Financial Crises And International Stock
Market Volatility Transmission', Australian Economic Papers, vol 49, no 3, pp
Jang, H & Sul, W 2002, 'The Asian financial crisis and the co-movement of Asian stock markets', Journal of Asian Economics, vol 13, no 1, pp 94-104
Johansen, S 1988, 'Statistical analysis of cointegration vectors', Journal of Economic
Dynamics and Control, vol 12, no 2-3, pp 231-54.