Return and volatility spillovers Vietnamese and some Asian markets

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY -o0o - EÂ1 NGUYỄN VĨNH NGHIÊM RETURN AND VOLATILITY SPILLOVERS VIETNAMESE AND SOME ASIAN MARKETS MAJOR: BUSINESS ADMINISTRATION MAJOR CODE: 60.34.05 MASTER THESIS SUPERVISOR: Dr VÕ XUÂN VINH HO CHI MINH CITY, 2012 i Acknowledgement Foremost, I would like to express my sincere gratitude to my advisor Dr Võ Xuân Vinh for the continuous support of my thesis, for his patience, motivation, enthusiasm, and immense knowledge His guidance helped me in all the time of research and writing of this thesis I would like to thank professors at Faculty of Business Administration and Postgraduate Faculty, University of Economics Ho Chi Minh City for their teaching, their guidance and support during my MBA course I wish to thank my family for the love, support and constant encouragement I have got over the years ii Abstract Purpose - This thesis investigates the interdependence between the Vietnamese stock market and other nine Asian markets in terms of return and volatility spillovers during three periods: pre-crisis, crisis and post-crisis Methodology - Long run and short run integration are examined through Johansen cointegration and Granger causality test respectively Vector autoregressive model is used to estimate the conditional return spillover among these indices Volatility spillover is studied through BEKK and AR-GARCH Model Findings - We find evidence of the integration of Vietnamese market with statically significant correlation, cointegration, return spillover and volatility spillover with other markets The crisis has strong impacts to market interdependence with higher correlation, cointegration and spillovers In the current period, there may be long run benefits from portfolio diversification to Vietnamese stocks Originality/Value - The thesis points out the return and volatility between Vietnamese stock market and other nine Asian Markets and suggests potential benefits from diversification Key words - Return spillover, Volatility spillover, VAR, BEKK, VAR-GARCH, Cointegration, Granger causality iii Contents Acknowledgement i Abstract ii Contents iii List of Figures v List of Tables vi Chapter Introduction 1.1 Background 1.2 Purpose and scope 1.3 Basic definition 1.3.1 Stock index 1.3.2 Return 1.3.3 Volatility 1.3.4 Return spillover 1.3.5 Volatility spillover 1.3.6 Time series 1.3.7 Cointegration 1.3.8 Granger causality 1.4 Research questions 1.5 Structure Chapter Literature review Chapter Methodology 12 3.1 Data 12 3.2 The model and methods 12 3.2.1 Introduction 12 3.2.2 Unit root and stationary test 13 3.2.3 Johansen’s cointegration techniques 14 3.2.4 Granger causality analysis 16 3.2.5 VAR Model 18 3.2.6 Bivariate BEKK Model 18 3.2.7 GARCH Model 20 Chapter Data Description, Results and Analysis of Results 22 4.1 Descriptive statistics and correlation matrix 22 4.1.1 Opening and closing time of Indices 22 iv 4.1.2 Descriptive statistics of Indices 23 4.1.3 Descriptive statistics of Indices’ return 24 4.1.4 Correlation matrix 25 4.2 Long-run interdependence 26 4.2.1 Unit root test 26 4.2.2 Johansen’s cointegration 27 4.3 Short-run interdependence 31 4.3.1 Granger causality analysis 31 4.3.2 VAR Model for estimation of return spill over 34 4.4 Volatility spill over 40 4.4.1 BEKK model 40 4.4.2 VAR – GARCH model 43 Chapter Conclusions 49 Figure 51 References 53 v List of Figures Figure Index timings by UTC Time 22 Figure Index closing price 51 Figure Index return 52 vi List of Tables Table Indices and their origination Table Descriptive statistics of Indices in pre-crisis period 23 Table Descriptive statistics of Indices in crisis period 23 Table Descriptive statistics of Indices in post-crisis period 23 Table Descriptive statistics of Indices’ return in pre-crisis period 24 Table Descriptive statistics of Indices’ return in crisis period 24 Table Descriptive statistics of Indices’ return in post-crisis period 24 Table Correlation Matrix between Indices' returns in pre-crisis period 25 Table 9.Correlation Matrix between Indices' returns in crisis period 26 Table 10.Correlation Matrix between Indices’ returns in post-crisis period 26 Table 11 Unit root test result on Indices 27 Table 12 Unit root test results on Indices' return 27 Table 13 Johansen's cointegration test for pre-crisis period 30 Table 14 Johansen's cointegration test for crisis period 30 Table 15.Johansen's cointegration test for post-crisis period 31 Table 16 Granger causality test results for pre-crisis period 33 Table 17 Granger causality test results for crisis period 33 Table 18 Granger causality test results for post-crisis period 34 Table 19 Bivariate VAR Model (VNIndex and other Indices) estimates of model on indices return in pre-crisis period 37 Table 20 Bivariate VAR Model (VNIndex and other Indices) estimates of model on indices return in crisis period 38 Table 21 Bivariate VAR Model (VNIndex and other Indices) estimates of model on indices return in post-crisis period 39 Table 22 Parameters estimates of BEKK model for pre-crisis period 42 Table 23 Parameters estimates of BEKK model for crisis period 42 Table 24 Parameters estimates of BEKK model for post-crisis period 43 Table 25 Volatility spillover estimates of AR(1) GARCH(1,1) model for precrisis period 46 Table 26 Volatility spillover estimates of AR(1) GARCH(1,1) model for crisis period 47 Table 27 Volatility spillover estimates of AR(1) GARCH(1,1) model for post-crisis period 48 Chapter 1.1 Introduction Background Currently, the globalization of domestic market becomes an evident trend The equity markets attract capital not only from domestic but also from international investors who expect to reduce the risk via diversification This trend would reduce the isolation of domestics markets and the markets can react quickly to international news and shocks The information transmission across market has been widely studied in two different faces First, the long term interdependence and causality among markets are considered as strong signal of information transmission And secondly, the volatility transmission across markets gets more studies these days because it becomes important as a good measure of the risk of internationally diversified portfolio which very helpful in deciding the asset diversification strategy Vietnamese stock market was formed a decade ago and now attracts valuable investment However, there have been relatively few studies on the linkages of Vietnamese equity market with international markets, especially the Asian markets 1.2 Purpose and scope This study attempts to investigate interactions in terms of price and volatility spillover amongst Vietnamese equity market and other nine Asian markets (India, Hong Kong, Indonesia, Malaysia, Japan, Philippines, China, Singapore and Taiwan) The return spillovers are examined with Johansen co-integration (for long term spillovers) and Granger causality test (for short term spillovers) Meanwhile, the bivariate BEKK and AR-GARCH model is used to evaluate the volatility spillovers Both the return spillovers and volatility spillovers are considered through three periods: the pre-crisis period (from 03rd January 2005to 31st December 2007), the crisis period (from 01st January 2008 to 30th June 2010) and the post-crisis period (from 1st July 2010 to 31st August 2012) The evaluation based on these three periods would indicate the effect of financial crisis to the return and volatility spillovers between Vietnamese stock market and other nine Asian markets The markets are presented by their Indices as following: Table Indices and their origination Index BSESN HIS JKSE KLSE Nikkei 225 PSEI SSE STI TWII VNIndex Index name BSE Sensex Index Hang Seng Index Jakarta Composite Index FTSE Bursa Malaysia Nikkei 225 Index Philippines Stock Exchange PSEi index SSE Composite Index Straights Times Index TSEC weighted index Vietnam Index Country India Hong Kong Indonesia Malaysia Japan Philippines China Singapore Taiwan Vietnam The reason for selecting these markets is that they represent the developed and emerging economies of Asian stock markets and they have potential effect to Vietnamese stock market Moreover the chosen indices are widely accepted benchmark indices - Hong Kong and Japan are regarded as one of the mature financial centers in Asia and play important role in the regional economy with high transaction volume and high influences to other markets - China is the fastest developing economy in the world and gains stronger position today in financial market; furthermore Vietnam shares same border with China and the trade among Vietnam and China gets large portion of the Vietnamese international trading, so we expect information transmission among China and Vietnam - Other markets (Indonesia, Malaysia, Philippines and Singapore) are in the same ASEAN (Association of Southeast Asian Nations) organization as Vietnam ASEAN is the ninth largest economy in the world and is growing with more and proven integration between its members 1.3 Basic definition 1.3.1 Stock index A stock index or stock market index is a method of measuring the value of a section of the stock market It is computed from the prices of selected stocks (sometimes a weighted average) It is a tool used by investors and financial managers to describe the market, and to compare the return on specific investments 1.3.2 Return Most financial studies involve returns, instead of prices, of assets Campbell et al (1996) give two main reasons for using returns First, for average investors, return of an asset is a complete and scale-free summary of the investment opportunity Second, return series are easier to handle than price series because the former have more attractive 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 Continuously compounded return The natural logarithm of the simple gross return of an asset is called the continuously compounded return or log return: 𝑟𝑡 = 𝑙𝑛 𝑃𝑡 = ln(𝑃𝑡 ) − ln(𝑃𝑡−1 ) 𝑃𝑡−1 40 4.4 Volatility spill over 4.4.1 BEKK model The parameters estimates of the BEKK Model which explain the volatility spillover between Vietnamese market and other market through periods are presented in table 22, 23 and 24 Each column in these tables gives value of the estimate from the bivariate BEKK model for the two time series [Index, VNIndex] Two most important parameters are 𝑎12 and 𝑎21 : parameter 𝑎12 explains the volatility spillover from the Index to VNIndex; and 𝑎21 represents the volatility from the VNIndex to the studied index Other remarkable parameters are 𝑎11 and 𝑎22 that present the effect of the residuals (the ARCH component) to the conditional variance; and 𝑏11 , 𝑏22 that indicate the impact of the previous variance (volatility) to the conditional variance We summarize the results from the bivariate BEKK model as below: Pre-crisis period: Three indices HIS, JKSE, PSEI affect the conditional volatility of Vietnamese markets: the parameter (𝑎12 ) is significant at 5% With JKSE and PSEI, the effect from these markets is positive (𝑎12 > 0) This implies that high volatility in JKSE or PSEI market creates lesser volatility in the Vietnamese stock market However, with HIS, the effect is negative (𝑎12 0) on JKSE and negative effect (𝑎21 0); but the effect is negative from SSE The volatility spillover from Vietnamese stocks market has positive affect to HIS and Nikkei; and negative affect to BESEN Post-crisis period: - During this period, two indices PSEI and SSE affect the conditional volatility of Vietnamese markets: the parameter (𝑎12 ) is significant at 5%; and all the effects from these markets are negative (𝑎12 < 0) - The volatility spillover from Vietnamese stocks market has positive affect to Nikkei (𝑎21 > 0) In the crisis period, the volatility spillovers get more significance: during precrisis, crisis and post crisis, the conditional variances of Vietnamese stock market is affected from 3, 5, and markets respectively; and it contribute to the explain of the conditional volatility of 2, and markets in these three periods We also learn about the components of the conditional variance of markets - the ARCH and the GARCH: - ARCH components (reflected via the A (1, 1) and A (2, 2) coefficient): the residuals which correlates the price variation of the present day to the price variation of the previous day; this component shows the effect of past innovation - GARCH components (reflected via the B (1, 1) and B (2, 2) coefficient): the previous volatility 42 Generally for all three periods; the volatilities show that the coefficient of GARCH effect is much higher than the value of ARCH coefficient This indicates that the volatility depends more on its lags than on the innovation Table 22 Parameters estimates of BEKK model for pre-crisis period BSESN HIS JKSE KLSE NIKKEI PSEI SSE C(1,1) 0.00245* C(2,1) -0.00011 0.00092* 0.00477* 0.00026 -0.00099* C(2,2) -0.00107* -0.00100* A(1,1) -0.27838* -0.19429* A(2,1) 0.02230 A(1,2) 0.07137 A(2,2) -0.45151* B(1,1) 0.93968* STI TWII 0.00097* 0.00168* 0.00268* 0.01484* -0.00091* 0.00190* -0.00085 0.00049* -0.00033 -0.00082 -0.00051* -0.00026* -0.00002 0.00089 -0.00101 0.00088* 0.00000 0.00097* -0.00111* -0.41640* 0.28569* -0.22428 -0.26963* -0.18761* -0.25467* -0.21417* 0.04021 0.06654* -0.08041* -0.01471 0.03275 0.03801 0.01333 0.06965 -0.06170* 0.07277* -0.01440 -0.04156 0.05920* 0.22662 0.03820 0.01063 -0.47515* -0.44978* -0.45482* -0.46094 -0.43599* -0.48505* -0.45099* -0.46288* 0.97366* 0.82636* 0.94806* 0.96187 0.93802* 0.26764 0.96092* 0.95834* B(2,1) 0.00596 0.00185 0.06370* 0.03870* -0.01111 0.00738 0.09381* -0.01200 0.01043* B(1,2) 0.02919* -0.02443* 0.03034* -0.00348* -0.01443 0.02974* 0.18698* 0.01801* 0.01090* B(2,2) 0.90857* 0.90012* 0.90555* * denotes rejection significance at the 5% level 0.90223* 0.90446* 0.91520* 0.87964* 0.90992* 0.90261* Table 23 Parameters estimates of BEKK model for crisis period BSESN HIS JKSE KLSE NIKKEI PSEI SSE STI TWII C(1,1) 0.00124 0.00257* 0.00286* -0.00025 0.00336* 0.00252* 0.01999* 0.00117* 0.00138 C(2,1) -0.00370* 0.00146* -0.00138 -0.00932* -0.00087 0.00056 -0.00414* 0.00168 -0.00034 C(2,2) 0.00000 0.00332* -0.00345* 0.00000 0.00222* 0.00362* 0.00282 0.00294* 0.00399* A(1,1) -0.26882* 0.30235* -0.25194* 0.28377* 0.26474* -0.23061* 0.28898* -0.20981* -0.20748* A(2,1) -0.16065* 0.13470* -0.06327 -0.02825 0.22918* 0.02911 -0.02995 -0.00886 -0.07380 A(1,2) 0.12249* -0.06134 0.13148* -0.03283 0.08696* 0.01828 -0.22605* 0.01192 0.12308* A(2,2) -0.38233* -0.44787* -0.40439* -0.83907* -0.37507* -0.45240* -0.39238* -0.40742* -0.44458 B(1,1) 0.93968* 0.95107* 0.93448* 0.16785* 0.92003* 0.94778* -0.04002* 0.97537* 0.95802* B(2,1) -0.07884* -0.01337 -0.02190 0.78455* -0.02165 -0.02632 0.14147* 0.01023 0.01396 B(1,2) 0.11995* -0.01584 0.07841* -0.66908* 0.05870* 0.03347 0.29204* -0.00529 0.04814* B(2,2) 0.89425* 0.88691* 0.89725* * denotes rejection significance at the 5% level -0.08012 0.92628* 0.88554* 0.85893* 0.90118* 0.87286* 43 Table 24 Parameters estimates of BEKK model for post-crisis period C(1,1) BSESN HIS JKSE KLSE NIKKEI PSEI SSE STI TWII 0.00133* 0.00126 0.00205* 0.00082* 0.00427* 0.00942* 0.00792* 0.00104 0.00067 C(2,1) -0.00116 0.00125 0.00320 0.00331 -0.00112* -0.00075 -0.00078 -0.00004 0.00851* C(2,2) 0.00710* 0.00804* 0.00905* 0.00726* 0.00592* 0.00634* 0.00585* 0.00807* 0.00000 A(1,1) 0.18226* -0.23294 -0.34025* -0.25818 0.30182* 0.36976* 0.24009* -0.24868* -0.27662* A(2,1) -0.06586 -0.06587 0.03282 -0.02293 0.12621* 0.01205 0.03189 -0.11033 0.01281 A(1,2) -0.04712 0.04324 0.02055 -0.01536 0.02381 -0.11862* -0.19775* -0.03239 0.06024 A(2,2) -0.49520* -0.50567* -0.55996* -0.48324* -0.40554* -0.43115* -0.42510* -0.49848* -0.52301* B(1,1) 0.97594* 0.95919* 0.92922* 0.95907* 0.84415* 0.49135* 0.65189* 0.96447* 0.94709* B(2,1) 0.03026 0.02024 0.07521 0.03186 -0.03335 0.11373 0.03953 0.04188 -0.00016 B(1,2) -0.01211 0.04820 -0.11314 -0.01862 0.13936 0.00812 -0.03553 -0.03291 0.06453 B(2,2) 0.66746* 0.58929* 0.40005* * denotes rejection significance at the 5% level 0.62416* 0.79461* 0.74133* 0.78187* 0.59461* 0.56178* 4.4.2 VAR – GARCH model Volatility spillovers estimated through BEKK (1, 1) not provide the partial effect of indices and also not consider same day effect We estimate the partial effect of indices and same day effect using univariate GARCH model as discussed earlier The results of the parameters are presented in table 25, 26, 27 for three 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 Pre-crisis period: From the GARCH equation of the Vietnamese market, we discover that the volatility of Vietnamese market depends on two markets: the positive effect from STI and negative effect from HIS The coefficients from these two markets are all statically significant at 5% level Or more specific, the higher volatility from STI/HIS, the higher/less volatility in Vietnamese stock market 44 The VNIndex has only positive effect on the KLSE volatility Crisis period: In this period, the volatility spillovers become stronger in comparison with the pre-crisis period: the VNIndex volatility depends on markets: negative dependence on KLSE, PSEI, SSE and positive dependence on TWII The results also indicate that the volatility spillovers from Vietnam have positive impact on HIS, JKSE and negative impact on BSE Post-crisis period: The volatility spillovers in this period decreases significantly: Vietnamese stock market now depends only on PSEI and has no impact on any other market It is interesting that the volatility spillovers get more significance in the crisis period: during pre-crisis, crisis and post crisis, the conditional variances of Vietnamese stock market is affected from 2, 4, and market respectively; and it make contribution to the explain of the volatility of 1, and markets respectively Our results are similar with findings of other authors: the study of Andrew Stuart & Alain (2011) indicate that global volatility linkages are particularly strong during the financial crises in Asia (1997-1998), Russia (1998), and the United States (2007-2008) Indika, Abbas & Martin (2010) found that the Asian and global financial crises of 1997-1998 and 2008-2009 significantly increased the stock return volatilities across all of the four markets Australia, Singapore, the UK, and the US Yilmaz (2010) argued that the volatility spillover index experiences significant bursts during major market crises, including the East Asian crisis 45 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 important implication from the findings of this chapter is that international investors can invest to Vietnamese stock market to gain potential long run benefits from portfolio diversification in this period In one hand, the VNIndex return still have low correlation with the studied markets’ return; the cointegrations between them are low In another hand, there are low return and volatility spillovers between Vietnamese and other markets All these facts help increase the benefits of diversification and reduce the investment risk 46 Table 25 Volatility spillover estimates of AR(1) GARCH(1,1) model for pre-crisis period BSESN Intercept 2.75E-05* HIS JKSE 6.86E-06* 4.75E-05* KLSE 1.50E-05* NIKKEI 6.09E-05* PSEI 0.000113 SSE 6.69E-06* STI TWII 2.61E-05* 7.78E-05* VNIndex -1.66E-06 ARCH 0.149784* 0.050184* 0.093601* ‘0.072081 0.011676 0.09237 0.044813 -0.072331 0.123715 0.316434* GARCH 0.226279* 0.1154818* -0.076469 0.339391* 0.365431* 0.461978* 0.921222* 0.247949* 0.509264* 0.671353* -0.015236* -0.017849 -0.008007* 0.018673 -0.025754 0.008667 -0.004556 0.021171 -0.043718* -0.008192 0.060631* -0.011461 -0.041713* JKSE(-1) 0.011653 0.004166 -0.00285 -0.011225 0.005005 KLSE(-1) 0.011027 0.028611 0.136811 0.005034 0.02261 BES(-1) HIS(-1) -0.00883 NIKKEI(-1) -0.01306 PSEI(-1) -0.016314* SSE(-1) 0.001741 STI(-1) -0.024733 0.014027 -0.000821 -0.008424 -0.005003 0.011214 -0.106355* 0.000618 0.007319 -0.023447 0.067836* TWII(-1) VNIndex(-1) -0.013665 BES HIS 0.151746 JKSE 4.04E-02 0.018226 KLSE -7.11E-02 0.282045* NIKKEI 0.238163* 0.0151 0.170222* -0.000851 0.051741* 0.34113 0.170505* 0.181505* 0.074968* 0.101914 -0.020232* PSEI 0.017277 -0.005924 0.056922 -0.010751* SSE -0.002738 0.018413* -0.015161* 0.001615 0.004653 9.28E-05 STI 0.373536* 0.508687* 0.431152* 0.129939* 0.011278 0.055854 0.13701 0.190979* -0.037434 0.013484 -0.007296 -0.005439 0.024806 -0.004166* TWII VNIndex * denotes rejection significance at the 5% level -0.00227 -0.040929* 0.013929 0.017698 0.006852 0.174485* -0.001605 -0.035317 0.000555 0.05083 -0.005468 47 Table 26 Volatility spillover estimates of AR(1) GARCH(1,1) model for crisis period BSESN Intercept HIS JKSE KLSE NIKKEI 0.000104* 2.04E-05* 1.03E-05 1.11E-05* ARCH 0.036425 -0.030335 0.074078* 0.245765* 0.056842 GARCH 0.058124 0.04508 0.019083 -0.010631 0.681706* 0.001545 0.004937 0.013775 -0.004558 BES(-1) 4.08E-06 PSEI 3.12E-05* SSE STI 0.000181* 1.72E-05* 0.049153 0.113611* 0.037473 0.283067* 0.079154* 0.028774 TWII VNIndex 1.36E-05* 0.000161* -0.040316* 0.02898 0.210389* 0.062859* 0.724896* 0.062602* 0.121746* 0.014597 HIS(-1) 0.257809* 0.063146 0.026593 0.002193 -0.025861 0.002995 JKSE(-1) 0.157658 0.03883 0.016682 -0.054534* -0.025507* 0.0156 KLSE(-1) -0.001796 0.007095 0.003048 -0.007937* 0.004521 -0.006768* NIKKEI(-1) 0.127072 PSEI(-1) -0.035803 -0.056973 SSE(-1) -0.022438 0.003115 STI(-1) 0.459322* -0.007308 TWII(-1) VNIndex(-1) -0.041232* 0.054891 0.084033* -0.057299* -0.004097 0.000165 -0.069639 0.056255 -0.014006 0.018752 0.003427 0.119876 -0.061178* -0.014899 BES HIS 0.056499 JKSE 0.117384* KLSE -0.00223* NIKKEI 0.047805 0.305211* 0.088849* 0.123585* 0.003152 0.000378 0.219947* 0.061221 0.001522 PSEI 0.042063 0.123911 0.033795 SSE 0.062824* 0.028374 0.013238 STI 0.806386* 0.516657* 0.161111* TWII 0.202395* 0.173918* 0.08004 VNIndex 0.085806* 0.042074* -0.000956 * denotes rejection significance at the 5% level 0.151639* 0.065256 0.028129 0.031997 -0.021608 0.24915* 0.005909 -0.018991* -0.032851* 0.184073* 0.123146* 0.090428* 0.01698 0.307495 0.011016 48 Table 27 Volatility spillover estimates of AR(1) GARCH(1,1) model for post-crisis period BSESN HIS JKSE 2.37E-05* N225 PSEI SSE STI 0.000139* 3.01E-05* 8.57E-05* 1.53E-05* TWII 2.20E-06 VNIndex Intercept 4.25E-05 1.17E-04* ARCH 0.008301 -0.077422* 0.136217* 0.144764 0.134678* 0.130528* -0.011128 -0.002595 0.03776 0.179035* GARCH 0.268203 0.399419* 0.494712* 0.561235* 0.555812* 0.347637* 0.382414 0.007409 0.92249* 0.670697* -0.099447* -0.020777 -0.032275* -0.032514 -0.040657 -0.012014 0.02693 0.0125 0.009051 BES(-1) 1.16E-05* KLSE 2.47E-05 HIS(-1) 0.062585 -0.026246 0.007688 -0.040951* -0.011297 -0.012085 JKSE(-1) 0.070662 -0.016849 0.130652* -0.003088 -0.013902 -0.010134 KLSE(-1) 0.040572 -3.79E-06 2.156177 -0.000311 1.883219 -4.76794 NIKKEI(-1) -0.004696 PSEI(-1) -0.002663 -0.003721 SSE(-1) 0.006721 -0.035765 STI(-1) 0.333347* -0.03035 TWII(-1) 0.026902 VNIndex(-1) 0.001879 -0.010408* 0.002739 0.001881 0.000231* -0.008939 -0.055307 -0.027233 -0.009122 0.088224 -0.019707 0.003213 -0.005922 BES HIS 0.019543 JKSE 0.072446 KLSE 5.82E-06 NIKKEI 0.003406 0.266978* 0.011414 0.101566* 0.000165 0.000342 -0.011623 0.018728 -0.002705* PSEI -0.007226* 0.000891 -0.002109 SSE -0.017497 0.025865 -0.018003* STI 0.248023* 0.164558* 0.005827 0.110555 0.099584* 0.003833 -0.082388 -0.015396 -0.017154 TWII VNIndex * denotes rejection significance at the 5% level 0.020333 0.006497 0.009717 -0.021411 -0.000964 0.019373 -0.014513 0.055368 0.01628 0.051667 -0.005655 0.003766 0.093757* 0.049813 -0.009985 49 Chapter Conclusions This thesis study the interdependence between the Viet Nam Index and other nine Asian Indices in terms of return and volatility spillover effect during periods: pre-crisis, crisis and post-crisis Although the correlations between Vietnamese stock market and other markets are still low but the correlations get increase; in the crisis period the correlations are strongest; this indicates stronger linkage and integration of Vietnamese stock market Vietnamese stock market is not cointegrated with any market in the pre-crisis period, but cointegrated with almost all markets in the crisis period and with two others in the post-crisis period Again we observe the impact of the crisis that makes the market co-integrate together Both the Granger causality test and the VAR model indicate the return spillovers from studied markets to Vietnamese stock market especially in the crisis period; however in the current period, the VNIndex return does not depend on any market Beside that we not find any evidence of the return spillovers from Vietnam in any period The study on volatility spillovers discovers that the volatilities of markets depend 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 periods The impact of the crisis on the market interdependence is clear: the markets get more integration during the crisis period, their correlations are higher with more cointegration and more spillover in both term of return spillover and volatility spillover From the perspective of foreign investors, the overall long-term independence in post-crisis results implies that there may be long run benefits from portfolio 50 diversification to Vietnamese stocks; because VNIndex seems not to move in long-time with the studied markets in the current period with little effect from the return and volatility spillover from other markets 51 Figure Figure Index closing price BSESN HIS 26,000 22,000 24,000 20,000 22,000 18,000 20,000 16,000 14,000 2010M07 18,000 2011M01 2011M07 2012M01 2012M07 16,000 2010M07 2011M01 JKSE 2011M07 2012M01 2012M07 2012M01 2012M07 2012M01 2012M07 2012M01 2012M07 KLSE 4,400 1,700 1,600 4,000 1,500 3,600 1,400 3,200 2,800 2010M07 1,300 2011M01 2011M07 2012M01 2012M07 1,200 2010M07 2011M01 Nikkei 225 2011M07 PSEI 11,000 5,500 10,500 5,000 10,000 4,500 9,500 4,000 9,000 3,500 8,500 8,000 2010M07 2011M01 2011M07 2012M01 2012M07 3,000 2010M07 2011M01 SSE 2011M07 STI 3,200 3,400 3,000 3,200 2,800 3,000 2,600 2,800 2,400 2,600 2,200 2,000 2010M07 2011M01 2011M07 2012M01 2012M07 2,400 2010M07 2011M01 TWII 2011M07 VNINDEX 9,500 550 9,000 500 8,500 450 8,000 400 7,500 350 7,000 6,500 2010M07 2011M01 2011M07 2012M01 2012M07 300 2010M07 2011M01 2011M07 2012M01 2012M07 52 Figure Index return HIS BSESN 06 04 04 02 02 00 00 -.02 -.02 -.04 -.06 2010M07 -.04 2011M01 2011M07 2012M01 2012M07 -.06 2010M07 2011M01 JKSE 03 025 02 000 01 -.025 00 -.050 -.01 -.075 -.02 2011M01 2011M07 2012M01 2012M07 2012M01 2012M07 2012M01 2012M07 2012M01 2012M07 KLSE 050 -.100 2010M07 2011M07 2012M01 2012M07 -.03 2010M07 2011M01 N225 2011M07 PSEI 15 08 10 04 05 00 00 -.04 -.05 -.08 -.12 2010M07 -.10 2011M01 2011M07 2012M01 2012M07 -.15 2010M07 2011M01 SSE 2011M07 STI 06 04 04 02 02 00 00 -.02 -.02 -.04 -.06 2010M07 2011M01 2011M07 2012M01 2012M07 -.04 2010M07 2011M01 TWII 2011M07 VNINDEX 06 04 04 02 02 00 00 -.02 -.02 -.04 -.04 -.06 2010M07 2011M01 2011M07 2012M01 2012M07 -.06 2010M07 2011M01 2011M07 2012M01 2012M07 53 References Alethea, R., William, R., Marco, R & Carl, S 2012, A comparison of Spillover Effects before, during and after the 2008 Financial 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