Introduction
Background
The globalization of domestic markets is a clear trend, as equity markets draw capital from both local and international investors seeking risk reduction through diversification This shift diminishes the isolation of domestic markets, enabling them to respond swiftly to global news and economic shocks.
Information transmission in markets is primarily examined through two key aspects Firstly, the long-term interdependence and causality between markets serve as significant indicators of information flow Secondly, the rising focus on volatility transmission highlights its importance as a measure of risk for internationally diversified portfolios, which is crucial for developing effective asset diversification strategies.
The Vietnamese stock market, established a decade ago, has become a significant destination for investment Despite its growth, there is a notable lack of research exploring the connections between the Vietnamese equity market and international markets, particularly within Asia.
Purpose and scope
This study explores the price and volatility spillover effects 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 investigates return spillovers using the Johansen co-integration method for long-term analysis and the Granger causality test for short-term dynamics Additionally, the bivariate BEKK and AR-GARCH models are employed to assess volatility spillovers.
This study analyzes return and volatility spillovers across three distinct periods: the pre-crisis period (January 3, 2005, to December 31, 2007), the crisis period (January 1, 2008, to June 30, 2010), and the post-crisis period (July 1, 2010, to August 31, 2012) By evaluating these periods, the research aims to assess the impact of the financial crisis on 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 encompass both developed and emerging Asian economies, significantly influencing 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 a significant increase in trade relations, enhancing economic collaboration between the two nations.
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
The VNIndex, also known as the Vietnam Index, plays a crucial role in reflecting the performance of Vietnam's stock market Recent developments in international trading between China and Vietnam highlight the importance of effective information transmission between the two countries This exchange is vital for enhancing trade relations and fostering economic growth in the region.
Vietnam is part of the ASEAN (Association of Southeast Asian Nations) organization, which includes other 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 financial metric that gauges the value of a specific segment of the stock market, calculated from the prices of chosen stocks, often using a weighted average This essential tool aids investors and financial managers in analyzing market trends and comparing the performance of individual 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, they provide a comprehensive and scale-free summary of investment opportunities, and secondly, return series are more manageable than price series due to their superior statistical properties.
There are several definitions of an asset return, and in this thesis, we use the word ‘return’ in means of continuously compounded return
The natural logarithm of the simple gross return of an asset is called the continuously compounded return or log return:
The equation \( P_{t-1} = \ln(P_t) - \ln(P_{t-1}) \) represents the relationship between the price or index value at time \( t \) and the log return \( r_t \) Here, \( P_t \) denotes the current price or index value, while \( P_{t-1} \) indicates the previous price or index value This formula is essential for analyzing financial data and understanding market trends through log returns.
Volatility is a key statistical indicator that reflects the degree of variation in returns for a specific security or market index It can be quantified through standard deviation or variance, providing insights into the risk and potential price fluctuations associated with that investment.
Commonly, the higher the volatility, the riskier the security
Return spillover refers to the phenomenon where the performance of one index influences the returns of other indices, potentially causing them to rise or fall in response to changes in the first index's performance.
Volatility spillover refers to the phenomenon where the fluctuations in the returns of one financial index 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 series of data points collected or recorded at consistent time intervals In this study, we analyze daily closing indices and their corresponding daily returns as time series data.
Time series analysis involves techniques for examining time series data to derive significant statistics and insights This research investigates time series analysis to address the questions outlined in this section.
Time series analysis often encounters challenges, such as the presence of a unit root, which can hinder statistical inference if not properly addressed The ordinary least squares (OLS) method is commonly employed to estimate the slope coefficients of auto-regressive models; however, this approach assumes that the stochastic process is stationary When dealing with non-stationary processes or those exhibiting a unit root, relying on OLS may lead to inaccurate estimates.
Newbold (1974) called such estimates spurious regression results: high R2 values and high t-ratios yielding results with no economic meaning
When two or more series exhibit cointegration, they share common stochastic trends and are likely to move together over the long term A detailed discussion of cointegration and the associated testing methods can be found in Chapter Three.
The Granger causality test, developed by Granger in 1969 and expanded in 1988, is a statistical method used to assess whether one time series can predict another Specifically, a time series X is considered to Granger-cause another series Y if lagged values of X, when analyzed through t-tests and F-tests alongside lagged values of Y, yield statistically significant insights into 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:
This study investigates the return spillover effects between Vietnam's financial market and other global markets It aims to analyze the interconnectedness of these markets, providing insights into how fluctuations in one market may influence returns in another Understanding these spillover effects is crucial for investors and policymakers in making informed decisions.
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, highlighting the interconnectedness of global stock markets Grubel (1968) explored co-movement and correlation among markets, emphasizing the benefits of international diversification from a U.S perspective Eun & Shim (1989) examined the international transmission of stock market movements, revealing significant multi-lateral interactions among national markets King & Wadhwani (1990) developed a model illustrating how "contagion" occurs as rational agents interpret price changes across 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 between 1995 and 2000 Additionally, 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 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.
In a 1995 study, Karolyi 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 key finding revealed that the magnitude and persistence of return innovations from one market to another are significantly influenced by the modeling of cross-market volatility dynamics.
Chelley-Steeley (2000) examined the volatility of equity markets across different countries and discovered a significant increase in the correlation of conditional variances among major equity markets over the past twenty years This trend highlights the growing interconnectedness of global financial markets.
(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 2002 study, Johnson & Soenen analyzed the integration of 12 Asian equity markets, revealing a strong connection between the stock markets of Australia, China, Hong Kong, Malaysia, New Zealand, Singapore, and Japan, with increasing integration noted since 1994 Tatsuyoshi's 2003 research highlighted that while the US significantly impacts returns in Asian markets, Japan's influence on returns is negligible However, volatility in Asian markets is more significantly affected by Japan than the US, indicating a reciprocal negative effect of Asian market volatility on Japan's market.
Singh, Kumar, and Pandey (2010) conducted a study on price and volatility spillovers across 15 stock markets in North America, Europe, and Asia using a VAR model for returns and an AR-GARCH model for volatility Their findings revealed that the US market primarily influences returns and volatility, impacting 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, the Japanese, Korean, Singapore, and Hong Kong markets demonstrated the strongest influencing power among Asian markets.
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, with emerging markets generally exhibiting higher own-volatility spillovers compared to cross-volatility spillovers.
Lakshmi (2004) pointed a high degree of volatility co-movement between Singapore, US, UK and Hong Kong market
Chuang, Lu & Tswei (2007) explored the interdependence of volatility across six East Asian markets using the VAR-BEKK model, revealing a high level of conditional variance interdependence, with Japan being the primary source of volatility transmission Similarly, Lee (2009) employed the VAR(p)-GARCH(1,1) model to analyze volatility spillover effects among the stock markets of India, Hong Kong, South Korea, Japan, Singapore, and Taiwan, finding significant spillover effects within these markets.
Sariannidis, Konteos, and Drimbetas (2010) examined the volatility linkages among the stock markets of India, Singapore, and Hong Kong from July 1997 to October 2005, finding a strong GARCH effect and high market integration that responds to information affecting both mean returns and volatility Similarly, Giampiero and Edoardo (2008) utilized a Markov Switching bi-variate model to investigate volatility transmission mechanisms, revealing long-term market characteristics, including spillover effects 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 explored dynamic interdependence, volatility transmission, and market integration in selected stock markets during the Asian financial crisis of 1997 and 1998 using the VAR-EGARCH model They highlighted Hong Kong's crucial role in transmitting volatility to other Asian markets The findings revealed that market integration was evident, with each market responding to both local news and information from other markets, especially in reaction to negative news.
Alethea et al (2012) utilized graphical modeling to analyze the S&P 500, Nikkei 225, and FTSE 100 stock market indices, examining the spillover effects of returns and volatility among these significant global markets before, during, and after a specified period.
2008 financial crisis Authors found that the depth of market integration changed significantly between the pre-crisis period and the crisis and post- crisis period
Matthew, Wai-Yip Alex & 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 research revealed significant evidence of contagion from the US stock market to both regions during the global financial crisis Notably, the intensity of the contagion effect 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 in 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 ones The study highlights a significant divergence in the behavior of return and volatility spillover indices over time While the return spillover index indicates increased integration among these markets, the volatility spillover index shows sharp spikes during major crises, such as the East Asian crisis Notably, both indices reached their highest levels during the recent global financial crisis, underscoring the severity of the current economic situation.
Zhou, Zhang & Zhang (2012) proposed measures of the directional volatility spillovers between the Chinese and world equity markets It was found that the
During the subprime mortgage crisis, the US market significantly influenced volatility across global markets, particularly impacting China, Hong Kong, and Taiwan Notably, the volatility interactions among these Asian markets were more pronounced than those observed between Chinese markets and their Western or other Asian counterparts.
Methodology
Data
The index values for the analyzed markets were sourced from Yahoo! Finance, encompassing the daily open and close prices Using this raw data, we calculated daily returns as outlined in the first chapter The analysis covers the period from January 3, 2005, to August 30, 2012.
The model and methods
Before discussing in details each testing method, we present here some of their basic characteristics and their rationales
The ADF unit-root test is conducted to determine the presence of a unit root in all indices and their returns This test is essential because subsequent tests, such as the Johansen co-integration test, necessitate the same level of integration.
- Long-run integration is tested through Johansen co-integration techniques
When two or more time series exhibit cointegration, they share common stochastic trends, indicating that they will move together over the long term, despite potential short-term divergences.
The short-run dynamics are analyzed using the Granger causality test and the Vector Autoregressive (VAR) model While the Granger causality test identifies potential relationships between endogenous variables, it does not specify whether these relationships are positive or negative; this aspect is further explored through the VAR model Additionally, return spillover effects are assessed using the VAR model to provide a comprehensive understanding of these dynamics.
- 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 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)
The hypothesis test examines the rank of the matrix 𝛑, where the null hypothesis (H0) posits that the rank is 0, indicating no cointegrating vectors, while the alternative hypothesis (H1) asserts that the rank is 1, suggesting the presence of one 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 of the studied markets, excluding the Nikkei; however, cointegration is observed between the VNIndex and Nikkei at a 10% significance level Notably, there is strong evidence of cointegration during periods of crisis.
- In the post-crisis period: VNIndex is in cointegration with one index (Nikkei) at 5% significant level, and with two indices (Nikkei, SSE) at 10% level
The analysis reveals two key findings: firstly, the crisis has strengthened the cointegration between the Vietnamese stock market and other global markets; secondly, the VNIndex is increasingly becoming more cointegrated with these markets Despite this, the current level of cointegration remains low, suggesting that there are potential long-term benefits for investors seeking portfolio diversification 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
At most 1 0.00335 2.188 3.841 0.139 2.188 3.841 0.139 tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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, yet two time series may not exhibit cointegration over the long run while still demonstrating 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 was conducted with four lags 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.
In this article, we will explore a specific Granger causality test in detail to better understand the subsequent results.
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, and the p-value is 0.009, indicating that at a 5% significance level, we can reject the null hypothesis of no Granger causality Therefore, we conclude that STI returns Granger cause the returns of the VNIndex, suggesting that STI returns are useful for predicting VNIndex returns.
We summary the results of the entire Granger causality tests for nine pairs for all three periods as follows:
- 4 indices’ return(HIS, JKSE, KLSE, STI) Granger cause VNIndex return
The VNIndex return has a Granger causality effect on the PSEI’s return, indicating a significant relationship between the two indices This suggests that fluctuations in the VNIndex can predict changes in the PSEI, highlighting the interconnectedness of these financial markets.
- 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
TWII 0.12911 0.9718 2.35663 0.0522 tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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 in the previous section, highlights the interdependence of endogenous variables; however, it does not quantify the strength of their inter-relationships or clarify 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:
The RVNIndex at time t is calculated using the formula RVNIndex(t-4) + 0.135192 * RVNIndex(t-5), where RVNIndex(t) represents the returns of the VNIndex and BSE at a specific time t This formula highlights the relationship between past RVNIndex values to derive current returns, emphasizing the importance of historical data in financial analysis.
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, the return spillover from these markets to Vietnam is exclusively positive, indicating that both positive and negative returns from other markets will similarly influence the Vietnamese market in a corresponding 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, particularly during crisis periods However, in the post-crisis phase, the returns of the Vietnamese market show independence from other markets Additionally, there is no evidence of return spillovers originating from Vietnam to other markets.
Our findings reveal significant return spillover during crisis periods, aligning with previous studies Johansson (2010) observed increased financial market integration and heightened comovements during international financial turmoil in East Asia and Europe Similarly, Yilmaz (2010) reported that return spillovers in East Asia peaked during the 2008 global financial 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 time series [Index, VNIndex] are detailed in the tables, highlighting key parameters such as 𝑎 12 and 𝑎 21 The parameter 𝑎 12 quantifies the volatility spillover from the Index to the VNIndex, while 𝑎 21 measures the volatility transfer from the VNIndex to the Index Additionally, parameters 𝑎 11 and 𝑎 22 reflect the influence of the residuals (ARCH component) on the conditional variance, and 𝑏 11 and 𝑏 22 illustrate the effect of prior volatility on the current conditional variance.
We summarize the results from the bivariate BEKK model as below:
The analysis reveals that three indices—HIS, JKSE, and PSEI—significantly influence the conditional volatility of Vietnamese markets, with the parameter (𝑎 12) being significant at 5% Specifically, both JKSE and PSEI exhibit a positive correlation (𝑎 12 > 0), indicating that increased volatility in these markets leads to reduced volatility in the Vietnamese stock market Conversely, the HIS index shows a negative correlation (𝑎 12 < 0), suggesting that heightened volatility in the HIS market corresponds to decreased volatility in Vietnam's stock market.
The Vietnamese markets significantly influence both the JKSE and KLSE, with a positive effect on JKSE and a negative effect on KLSE at a 5% significance level.
Recent analysis indicates that five indices—BSE, JKSE, Nikkei, SSE, and TWII—significantly influence the conditional volatility of Vietnamese markets, with the parameter (𝑎 12) showing significance at the 5% level Specifically, the BSE, JKSE, Nikkei, and TWII exhibit a positive impact (𝑎 12 > 0), whereas the SSE demonstrates a negative effect on market 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 times of crisis, the significance of volatility spillovers increases In the pre-crisis, crisis, and post-crisis phases, the conditional variances of the Vietnamese stock market are influenced by three, five, and four markets, respectively Additionally, these markets contribute to explaining the conditional volatility of two, three, and one markets during these three 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), highlight the correlation of today's price variations with those of the previous day, illustrating the impact of past innovations on current market behavior.
The GARCH model incorporates key components, specifically the B(1, 1) and B(2, 2) coefficients, which reflect previous volatility Understanding these elements is crucial for analyzing financial time series data and forecasting future market behavior.
In all three periods analyzed, the GARCH effect coefficient significantly exceeds the ARCH coefficient, suggesting that volatility is more influenced by its historical values than by new information or innovations.
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
The significance of rejection is indicated at the 5% level, highlighting the importance of thorough evaluation in academic research For the latest updates on graduate thesis downloads, please refer to the provided email.
Table 24 Parameters estimates of BEKK model for post-crisis period
BSESN HIS JKSE KLSE NIKKEI PSEI SSE STI TWII
* denotes rejection significance at the 5% level
The BEKK (1, 1) model for estimating volatility spillovers does not capture the partial effects of indices or same-day effects To address this, we employ a univariate GARCH model to assess these aspects The findings from this analysis are detailed in Tables 25, 26, and 27, which cover three distinct periods.
Because of difference in opening and closing time, the volatility of Vietnamese stock market would depend on, if any:
- The same day residuals from BSE, HIS, JKSE, KLSE, PSEI, STI
- The one lag day residuals from Nikkei, SSE, and TWI
The GARCH analysis of the Vietnamese market reveals that its volatility is influenced by two key markets: a positive correlation with the STI and a negative correlation with the HIS Both coefficients are statistically significant at the 5% level, indicating that increased volatility in the STI leads to greater volatility in the Vietnamese stock market, while heightened volatility in the HIS results in reduced volatility in Vietnam's market.
The VNIndex has only positive effect on the KLSE volatility
During this period, volatility spillovers have intensified compared to the pre-crisis era, with the VNIndex's volatility exhibiting a negative correlation with the KLSE, PSEI, and SSE, while showing a positive correlation with the 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 in the Vietnamese stock market are notably more significant during crisis periods Analysis reveals that in the pre-crisis phase, the market's conditional variances are influenced by two other markets, while during the crisis, this number increases to four In the post-crisis period, only one market affects the conditional variances Consequently, these relationships contribute to explaining the market's volatility, with one market contributing in the pre-crisis phase, three during the crisis, and none in the post-crisis period.
Our results are similar with findings of other authors: the study of Andrew Stuart
Global volatility linkages intensify during major financial crises, as highlighted by Alain (2011) in the cases of Asia (1997-1998), Russia (1998), and the United States (2007-2008) Research by Indika, Abbas, and Martin (2010) reveals that the financial crises of 1997-1998 and 2008-2009 led to significant increases in stock return volatilities across key markets, including Australia and Singapore.
Yilmaz (2010) highlighted that the volatility spillover index shows notable spikes during significant market crises, such as the East Asian crisis, affecting both the UK and the US markets.
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
Conclusions
This thesis examines the interdependence between the Viet Nam 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 low correlations with other markets, but these correlations are increasing over time During crisis periods, the correlations reach their peak, highlighting a stronger 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 showed significant cointegration with nearly all markets during the crisis and with two additional markets in the post-crisis phase This pattern highlights the crisis's influence in fostering market interconnections.
The Granger causality test and VAR model reveal that during the crisis period, there are return spillovers from various markets to the Vietnamese stock market However, in the current period, the VNIndex returns show no dependence on any external market Additionally, there is no evidence of return spillovers originating from Vietnam in any timeframe.
The study on volatility spillovers reveals that market volatilities are influenced more by their past values than by new information Additionally, the Vietnamese stock market shows some degree of integration with other markets regarding volatility spillovers Notably, these spillovers tend to be more pronounced during periods of crisis.
The crisis significantly enhances market interdependence, leading to increased integration among markets During such periods, the correlations between markets rise, resulting in greater cointegration and heightened spillover effects, both in terms of returns and volatility.
From a foreign investor's perspective, the long-term independence observed in post-crisis results suggests potential benefits from diversifying into Vietnamese stocks The VNIndex appears to operate independently of the studied markets, showing minimal influence from return and volatility spillovers, which could enhance investment opportunities in Vietnam's stock market.
VNINDEX tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
VNINDEX tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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
The article published in Dynamics and Control, volume 12, issues 2-3, pages 231-254, explores advanced topics in dynamics and control systems It provides insights into recent research findings and methodologies relevant to the field For further information or to access the full content, please reach out via email.