Relationship between trading volume and stock return in viet nam

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY - ĐỖ NGỌC HOÀNG YẾN RELATIONSHIP BETWEEN TRADING VOLUME AND STOCK RETURN IN VIETNAM’S STOCK MARKET Major : FINANCE – BANKING Code : 60.31.12 MASTER THESIS Instructor: Dr HỒ VIẾT TIẾN HO CHI MINH CITY, SEPTEMBER 2011 ABSTRACT This thesis investigated the relationship between return and trading volume in the Vietnam’s stock market in the context of Granger causality test and GARCH model test The sample, including two market indices and thirty seven largest market capitalization listed companies during the period since they firstly traded through July 2011, was used The dynamic relation as marked by lead –lag relationship from return to volume was confirmed at both market level and firm level I also found the evidences supported the interaction between two exchanges in Vietnam When testing the mixture distribution hypothesis, the results indicated that volume was not a good proxy for information arrival in the stock market due to the persistence of volatility remained in most of the cases This finding was similar to other emerging markets which less agreed with the mixture distribution hypothesis i CONTENTS Abstract i Contents ii List of Tables iv Chapter 1: Introduction 1.1 Introduction 1.2 Research background 1.3 Problem statement 1.4 Research objectives and questions 1.5 Research methodology and scope 1.6 Thesis structure Chapter 2: Literature Review 2.1 Theoretical background 2.2 Empirical studies 2.2.1 Studies on volume- price change relation 2.2.2 Studies on volume- volatility relation 11 Chapter 3: Research Methodology 3.1 Hypotheses 15 3.2 Data Description 15 3.2 Econometric Methodology 3.2.1 Stationary and Unit Root test 16 3.2.2 Cointegration 17 ii 3.2.3 Granger Causality tests 19 3.2.4 ARCH models 21 3.2.5 GARCH models 23 3.2.6 Threshold GARCH models 23 Chapter Empirical results 4.1 Market level analysis 4.1.1 Descriptive statistic for markets 25 4.1.2 Unit root test and Granger causality test 26 4.1.3 GARCH(1,1) test and TGARCH (1,1) test 27 4.2 Firm level analysis 4.2.1 Descriptive statistic 29 4.2 2Granger causality test 30 4.2.3 Restricted and unrestricted GARCH(1,1), TGARCH (1,1) test 33 Chapter Conclusion and Implication 5.1 Main findings 35 5.2 Implications 35 References 37 Appendix 41 iii LIST OF TABLE Table Descriptive statistics of two market indices 25 Table Stationary test for market indices 26 Table Cointegration test (Unit root test for residuals) 26 Table 4 Granger causality test at market level 27 Table ARCH effect test for indices 28 Table GARCH (1,1) model and TGARCH (1,1) model for indices 29 Table 4.7 Granger causality test at firm level 31 Table 4.8 ARCH effect test for firms 33 Table A1 Descriptive statistics of firms 41 Table A2 Unit root test for return and volume of firms 43 Table A3 Cointegration test at firm level 44 Table A4 GARCH (1,1) model with and without volume for firms 45 Table A5 TGARCH(1,1) model with and without volume for firms 47 Table A6 List of 37 sample firms with their symbol 48 iv CHAPTER 1: INTRODUCTION 1.1 INTRODUCTION This chapter explains why the link of volume, return and volatility is worth investigating in the case of the Vietnamese stock market In particular, this chapter divides into six sections The first section summarizes the structure of the chapter The second one provides evidences that tell us why the return – volume relationship becomes a concern for market participants and policy makers From this background information, the third section raises the problem necessary to make clear for the case of Vietnam The fourth section covers the research objectives and research questions The fifth section describes the methodology and scope The last one ends with description about the structure of the thesis 1.2 RESEARCH BACKGROUND The relationship between return, volatility, and volume has met the interest of many researchers over the past years The motivation comes from the attempt to measure and model the volatility of financial assets return Volume is evidenced to be an important part of pricing financial assets under influence of information arrival Due to new information arrival, investors may adjust their expectations and this is the main source for price and return movements However, the stock return may remain unchanged if some investors recognize the information as good news whereas others find it to be bad news Clearly, it is necessary to examine the dynamics of stock return, volatility and trading volume so that it would improve the understanding of the microstructure of the stock market and then help the participants and policy makers in their own strategies Most previous researches followed two leading theories (hypotheses), the mixture of distribution hypothesis (MDH) and the sequential information arrival hypothesis (SAI), to examine the information arrival process in financial markets In general, both MDH and SAI hypotheses support a contemporaneous and positive relationship between volume and absolute return and assume a symmetric effect for price changes As pointed out in MDH, volume of trade can be a proxy of new arrivals [Clark (1973), Epps and Epps (1976)] Clark (1973) implies that the value of price change and trading volume are distributed independently from each other Also, the number of information arrivals per time period varies Lamoureux and Lastrapes (1990) shows that a serially correlated mixing variable measuring the rate at which information arrives to the market helps explain the generalized autoregressive conditional heteroskedasticity (GARCH) effect in the return According to them, volume that is considered as an explanatory in the conditional variance equation eliminates the GARCH effects Sharma et al (1996) extend Lamoureux and Lastrapes (1990) work by bringing out two main forms: (1) the ability of daily trading volume data to fully capture the information flow on the market return would partly rest on the degree of market efficiency, and (2) both firm – specific factors and market – wide factors (which affect volume) can generate volatility This makes volume a good or poor proxy for news arrival that contributes to conditional heteroskedasticity However, Najand and Yung (1991) and Bessembider and Seguin (1992, 1993) present evidence against MDH In addition, Bessembider and Seguin (1992, 1993) suggest that the volatility –volume relation in the financial markets depends on the type of trade On the other hand, the sequential arrival of information hypothesis (SAI) suggests gradual popularization of information According to Grammatikos and Saunders (1986, p.326), the implication of SAI is that the information is sequentially observed by each trader in the market Under SAI framework, McMillan and Speight (2002) argue that past absolute return provides information on current volume, and past volume contains information on current absolute return In other words, this dynamic relationship is helpful and important to forecast return and volatility by using trading volume information 1.3 THE PROBLEM STATEMENT The stock market in Vietnam, which is supervised and managed by the State Securities Commission, has developed rapidly since established in July 2000 With 289 firms listed on Hochiminh Stock Exchange and 384 firms listed on Hanoi Stock Exchange up to May 2011, the market is considered as a channel for companies to raise medium and long capital Regarding capitalization value, it is recorded to grow considerably from VND270 billions in 2000 (approximate 0.28% GDP) to VND740,433 billion in 2010, approximate 45.2 percent of Vietnam GDP The number of securities trading accounts has reached at 1,103,184 at April 30th 2011, increasing 25.4 percent compared to one year before On average, total trading volume of two exchange is 81,312,559 shares and fund certificates and VND2,534.93 billions trading value per day is recorded in 2010 During ten years, Vietnam‟s stock market has shown the ups and downs of a developing market In the first five years, the market did not attract the public attention and made very little distribution to the economy due to the lack of merchandise and unattractive small listed companies Since 2006, it has attracted more foreign and domestic investors with lively trading activities in two listed exchanges It showed an excellent performance in 2006 when the market capitalization increased fifteen times, the Vnindex of Hochiminh Stock Exchange (HOSE) grew 144% and the Hnindex of Hanoi Stock Exchange (HNX) grew 152.6% only in one year After reaching the highest peak of 1170.67 points in March 2007, Vnindex went down rapidly under effects of the global recession The index fell as low as 239.69 points in February 2009 Since then, the index rose nearly 2.6 times to 530 points at the beginning of 2010 As being affected by the changes of the world finance and the difficulties inside the economy, the stock market of Vietnam continues to perform quietly during the 2010 and the first half of 2011 For a young stock market, Vietnam‟s market clearly contains weaknesses Firstly, this is an immature market with a weak legal environment and lack of capital The Government strictly controls the rules and actively intervenes in stock trading Accordingly, investors tend to speculate, and thus cause high market volatility Secondly, the lack of transparency is widely known as a biggest problem facing the traders Reporting requirements are not well-defined and public information disclosure is not clear and unreliable From that reasons, it is harder for investors to build up a good portfolio in an inefficient market which contains lots of confusing information It is the fact that while most of previous studies focused on developed markets, little empirical evidences for emerging markets have been found, especially in Vietnam This analysis allows us to answer the important question of whether the linkage of volume, return and volatility in the case of Vietnam market at both market level and firm level exists 1.4 RESEARCH OBJECTIVES AND QUESTIONS To solve the research problem, this study has following objectives:  To explore the causal relationship of stock return and trading volume  To find out the trading volume effect on the return volatility The research problem defined above leads to the following research questions:  Is there any long run relationship between the trading volume and stock return in Vietnam?  Does the causal relationship between stock return and trading volume exist in Vietnam‟s stock market? If yes then, what is direction and extent of relationship between these variables?  Does ARCH effect exist in stock return of two indices? If yes then, is this ARCH effect weaker when trading volume is added as an explanatory variable in GARCH equation?  Does ARCH effect exist in individual stock return? If yes then, does this effect reduce when trading volume is included as an explanatory variable in GARCH equation? 1.5 RESEARCH METHODOLOGY AND SCOPE The Granger causality and ARCH/GARCH effect tests are employed to test the proposed hypotheses Theoretically, these tests are only appropriate when the variables analyzed, including stock price, the index and trading volume, are stationary and co-integrated Therefore, it becomes necessary to conduct various prior tests of integration and cointegration In so doing, this thesis will apply the unit root test (specifically the augmented Dickey-Fuller tests) Following previous studies, this thesis will employ the Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) to determine the optimal lag lengths Data used in Granger causality and GARCH models are collected from two official sources, namely, Hochiminh Stock Exchange and Hanoi Stock Exchange during the May 2006 to July 2011 period I also use the stock price of 37 large size (sorted in market capitalization) companies as my sample Similar to most previous studies, this thesis will use the daily data to meet the required observations in GARCH models More details in handling variables will be discussed in chapter three 1.6 THESIS STRUCTURE In terms of structure, the thesis has five chapters After defining the research problem, questions for the study in chapter one, chapter two reviews previous researches related to relationship between volume and price change Chapter three discusses in detail about the methodology including the data collection and analysis methods, and CHAPTER 5: CONCLUSION AND IMPLICATION 5.1 MAIN FINDINGS This thesis examines the interaction of stock return, volatility and volume for selected big market capitalization companies and exchanges in Vietnam Applying the GARCH models and Granger causality test, my analysis produces some major findings First, trading volume would not be a good proxy of information flows at both market level and firm level Thus, the finding would less agree with the mixture distribution hypothesis and earlier studies on developed stock markets, which suggested volume reflects information flows in predicting return However, it confirms the weak positive association between volume, return and volatility Second, this paper contributes to the literature with the spill – over effect between two exchanges in Vietnam Third, we discover that there is causality from return to volume in almost cases at both market level and firm level Finally yet importantly, in terms of trading volume degree, I find the thinner traded stocks return is more affected by volume than more actively traded stocks In short, the finding of this dissertation less supports to the mixture distribution hypothesis but it is consistent with previous studies on this issue in emerging markets 5.2 IMPLICATIONS Following the analysis and conclusion presented above, some suggestions for participants‟ concerns on stock market may be proposed In terms of regulations, the policy makers should pay attention on the issue mentioned above when making rules for the markets The findings indicate that Vietnam‟s stock market is inefficient and lack of transparency A part of listed companies, which have big market capitalization, affect considerably to the market volatility through their trading activity every day Besides the change in building an appropriate calculation method for the index, regulators should strengthen the 35 management and supervising system on the public information disclosures to get a fair markets for investors On another hand, the inference from this paper is useful for investors‟ consideration in making a good portfolio and suitable strategy It is noted that there is a long run relationship between volume and return and the correlation is positive Investors would notice more about thin traded stocks, which volatile more than the actively traded stocks and affected by volume By understanding the dependence of return on past return and the relation between volume and return, volatility in different stocks , they will benefit in modeling and forecasting return and volatility in short –term It is also helpful in risk management and portfolio selection Besides, the spill- effect of two HOSE and HNX should be concerned Finally, the thesis has the scope that remains for further studies I only investigate the biggest market capitalization companies in two exchanges as sample for my thesis It is interesting to widen the sample in terms of industry or trading activity of all listed firms on the market The issue remains for other companies Concerning about spill-over effect, the relationship between Vietnam‟s market and other markets would be worth to research 36 REFERENCE Aargo,Vincent and Nieto, Luisa, 2005, Heteroskedasticity in the returns of the main world stock exchange indices: volume versus GARCH effects, Journal of International Financial Market, Institution and Money, Vol 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Finance, Vol 17.1, 3290-3329 40 APPENDIX Table A1 Descriptive statistics of firms Panel A: Return analysis Firm KLS PVX STB VCG SSI ACB SHB EIB HAG ITA HPG PVS REE DPM KBC PVF SJS VCB CTG FPT PPC PVI VSH DIG CII PVD VIC VNM BCI GMD BVH AGR PHR DHG DPR MSN VPL Observation 856 471 1243 712 1121 1150 558 425 635 1154 904 946 2563 912 386 671 1247 508 496 1133 1266 976 1298 472 1281 1140 944 1358 581 2273 511 387 473 1122 892 417 851 Percentage of market volume 18.00% 17.46% 11.97% 10.76% 8.26% 6.67% 6.60% 5.32% 3.64% 3.26% 3.02% 2.79% 2.73% 2.68% 2.22% 2.07% 2.05% 1.90% 1.81% 1.79% 1.63% 1.61% 1.56% 1.36% 1.33% 1.21% 1.20% 1.11% 1.06% 0.84% 0.76% 0.59% 0.49% 0.39% 0.25% 0.25% 0.17% Mean -0.000307 -0.000581 -0.000191 -0.000472 -0.000318 -0.000229 -0.000236 -0.000421 0.000335 -7.96E-05 -0.00038 -0.000737 0.000192 -0.000501 -0.001067 -0.000132 0.000394 -0.000563 -0.000232 -0.000382 -0.000567 -0.000724 -7.77E-05 -0.000936 -0.000131 -8.91E-05 0.000585 0.000469 -0.000134 0.000015 0.0004 -0.000583 -0.000305 2.95E-05 -0.000238 0.000869 -0.000215 Std Deviation Skewness Kurtosis 0.016485 0.01473 0.010431 0.0166 0.013187 0.012453 0.01226 0.006542 0.010993 0.012864 0.011462 0.014764 0.009429 0.011342 0.011495 0.013765 0.013059 0.010005 0.009433 0.011656 0.013363 0.015024 0.012064 0.011578 0.011909 0.011324 0.011869 0.01055 0.012312 0.009746 0.012727 0.010682 0.009083 0.01003 0.01157 0.012127 0.012366 0.120812 0.263724 0.175541 0.091004 0.209488 0.189972 0.348398 0.374912 0.035573 0.03384 0.045584 -0.27033 0.041799 0.043423 0.093812 -0.01689 -0.02147 -0.236 0.182327 0.085269 2.980588 0.133638 0.189852 0.188423 0.015057 -0.00386 -0.06377 0.043905 -0.02429 0.038707 -0.05742 0.140124 0.086576 0.03448 -0.02191 -0.06113 -0.03117 2.438215 2.729793 2.727375 2.396451 2.810698 4.654125 4.022553 5.429166 2.672306 2.135841 2.41412 5.068015 3.496984 2.46103 2.411411 1.917109 1.981831 5.584994 3.37573 2.409158 45.79302 2.969235 3.195218 2.438606 2.367029 2.410637 2.328076 2.762671 2.224444 3.353979 2.262747 2.614268 3.481038 2.934537 2.384358 2.325828 2.244882 41 Panel B: Volume analysis volume Daily average percentage /market volume 2,872,963 2,787,346 3,109,295 1,717,139 2,145,783 1,065,196 1,053,062 1,380,818 944,564 846,777 784,034 445,726 708,609 695,139 575,708 536,547 531,780 492,415 469,394 465,005 422,470 257,073 405,913 352,973 345,589 314,783 310,902 288,917 276,456 217,930 197,454 153,763 127,098 102,203 65,942 65,250 44,499 18.00% 17.46% 11.97% 10.76% 8.26% 6.67% 6.60% 5.32% 3.64% 3.26% 3.02% 2.79% 2.73% 2.68% 2.22% 2.07% 2.05% 1.90% 1.81% 1.79% 1.63% 1.61% 1.56% 1.36% 1.33% 1.21% 1.20% 1.11% 1.06% 0.84% 0.76% 0.59% 0.49% 0.39% 0.25% 0.25% 0.17% Average Firm KLS PVX STB VCG SSI ACB SHB EIB HAG ITA HPG PVS REE DPM KBC PVF SJS VCB CTG FPT PPC PVI VSH DIG CII PVD VIC VNM BCI GMD BVH AGR PHR DHG DPR MSN VPL Std Mean 0.000369 -0.00151 -0.000196 -0.001108 -0.000153 0.000282 -0.001095 -0.003594 0.00258 0.000549 -0.00145 -0.001122 0.001056 -0.001771 -0.003866 0.000245 -0.000844 0.000431 -0.003278 -0.000226 -0.000806 -0.001194 -0.000267 0.002765 -0.000329 0.000171 -0.000647 -0.00059 -0.000874 0.000566 -0.002888 0.006454 0.000592 -0.000595 -0.00212 -0.000603 -0.001121 Deviation Skewness Kurtosis 0.500412 0.295591 0.545394 0.329715 0.588271 0.340732 0.269166 0.265588 0.51764 0.409537 0.469772 0.401804 0.405031 0.440395 0.350439 0.320039 0.411736 0.426929 0.428099 0.426483 0.347274 0.410825 0.389458 0.474339 0.469678 0.381988 0.410182 0.369781 0.606809 0.537273 0.504758 0.457762 0.521134 0.555068 0.661201 0.71188 0.561266 0.674017 0.539573 0.020406 0.856511 0.055183 0.18439 0.718223 0.036886 0.433881 0.098176 0.497947 0.49732 0.680216 0.936327 -0.0222 0.189955 0.396492 0.058608 0.134452 0.378614 0.257539 0.596777 0.513047 0.016549 0.237728 0.481609 0.357698 0.741843 -0.35571 -0.02104 0.204436 1.144761 -0.13279 0.225024 0.233148 -0.15776 -0.37394 11.60732 14.5013 80.45403 12.86968 61.08013 11.67763 6.333454 5.517494 65.12602 9.521789 18.03751 14.13992 9.001014 19.89794 40.22731 5.030059 8.007733 29.80402 26.36793 17.03156 9.921272 9.176733 9.49171 24.10446 13.66339 13.39762 7.314644 10.87917 10.9644 7.335712 19.25666 9.054912 15.71837 8.989014 9.774757 6.589499 12.85166 42 Table A2 Unit root test for return and volume of firms ADF t- statistic VARIABLE ACB AGR BCI BVH CII CTG DHG DIG DPM DPR EIB FPT GMD HAG HPG ITA KBC KLS MSN PHR PPC PVD PVF PVI PVS PVX REE SHB SJS SSI STB VCB VCG VIC VNM VPL VSH RETURN -29.6336 -18.51642 -19.36128 -18.76311 -28.00039 -18.04128 -20.31749 -16.93818 -26.05947 -25.30687 -18.83388 -25.84321 -36.10545 -20.66027 -23.96678 -26.42949 -18.12331 -22.03007 -13.55661 -19.75814 -29.25299 -27.4023 -21.12328 -26.6631 -28.1105 -18.15619 -19.58714 -22.93482 -24.76388 -12.7757 -27.71156 -21.0953 -21.62095 -23.07993 -25.95992 -21.1102 -29.69312 VOLUME -18.75547 -14.14515 -20.20924 -19.29629 -17.54903 -20.10908 -16.99744 -12.96806 -26.69056 -16.39716 -18.98074 -17.02642 -22.0314 -24.10447 -15.86046 -18.53722 -18.69024 -17.99479 -14.80393 -19.60197 -22.78491 -15.08672 -15.93173 -17.18254 -18.98759 -16.81047 -28.82972 -14.51319 -21.32483 -15.57565 -19.31478 -12.24813 -19.79981 -21.47057 -15.79859 -15.64875 -32.30522 Conclusion at 1% I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) 43 Table A3 Cointegration test at firm level Firm ADF t-statistic Station ary AGR BCI BVH CTG DHG FPT DIG KLS MSN PHR PVS PVX SSI VCB VIC VNM ACB CII DPM DPR EIB GMD HAG HPG ITA KBC PPC PVD PVF PVI REE SHB SJS STB VCG VPL VSH -18.61023 -19.3361 -18.48585 -18.0406 -26.45821 -25.85948 -16.92031 -21.92141 -13.61603 -19.7737 -28.06066 -18.05771 -12.79442 -17.01814 -23.08237 -25.95915 -29.47147 -28.04306 -26.07162 -24.82304 -18.85162 -36.10874 -20.67688 -23.97465 -26.22386 -18.14253 -29.39442 -27.4882 -21.21772 -26.46678 -19.61976 -22.50353 -24.74443 -27.68945 -21.51021 -21.07762 -29.76356 I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) I(0) Cointegrat ion at 1% Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 44 Table A4 GARCH (1,1) model with and without volume for firms Panel A: Restricted GARCH(1,1) model + Firm Constant  KLS 0.0000131*** 0.155082 0.794213 0.949295*** PVX 0.0000146** 0.16485 0.765386 0.930236*** STB 0.00000609*** 0.207531 0.732758 0.940289*** VCG 0.0000147*** 0.177793 0.770499 0.948292*** SSI 0.00000889*** 0.230453 0.717752 0.948205*** ACB 0.00000495*** 0.234517 0.753408 0.987925*** SHB 0.0000063*** 0.156394 0.798326 0.95472*** EIB 0.00000108*** 0.213869 0.778961 0.99283*** HAG 0.00000492*** 0.143606 0.81208 0.955686*** ITA 0.00000698*** 0.186647 0.769022 0.955669*** HPG 0.00000508*** 0.192698 0.769405 0.962103*** PVS 0.0000102*** 0.171623 0.792814 0.964437*** REE 0.000000363*** 0.234983 0.783123 1.018106*** DPM 0.0000153*** 0.230787 0.651836 0.882623*** KBC 0.0000162*** 0.225737 0.653142 0.878879*** PVF 0.0000133 0.135812 0.788854 0.924666*** SJS 0.00000437*** 0.228258 0.75759 0.985848*** VCB 0.0000339*** 0.374352 0.265381 0.639733*** CTG 0.00000682*** 0.235616 0.682205 0.917821*** FPT 0.00000795*** 0.201277 0.73654 0.937817*** PPC 0.00000986*** 0.260433 0.692413 0.952846*** PVI 0.0000173*** 0.209459 0.71849 0.927949*** VSH 0.0000086*** 0.259426 0.684649 0.944075*** CII 0.00000526*** 0.180098 0.784296 0.964394 PVD 0.00000798*** 0.192568 0.745227 0.937795*** VIC 0.00000718*** 0.162944 0.782046 0.94499*** VNM 0.0000014*** 0.162031 0.832295 0.994326*** BCI 0.0000031** 0.123051 0.855125 0.978176*** GMD 0.00000147*** 0.208511 0.784454 0.992965*** AGR 0.0000137* 0.138229 0.72475 0.862979*** PHR 0.00000351*** 0.142254 0.812928 0.955182*** DHG 0.00000293*** 0.151333 0.819283 0.970616*** DPR 0.00000721*** 0.193112 0.755056 0.948168*** MSN 0.0000114 0.121078 0.789032 0.91011*** VPL 0.0000111*** 0.21577 0.710146 0.925916*** ***, ** & * indicate statistical significance at 1%, 5% and 10% level respectively 45 Panel B Unrestricted GARCH (1,1) model + Firm Constant  Volume KLS 1.48E-05 0.152912 0.787776 0.940688*** 0.0000409*** PVX 0.000128 0.076314 0.481241 0.557555 0.000136 STB 5.02E-06 0.194535 0.755261 0.949796*** 0.0000234*** VCG 7.94E-06 0.198212 0.778885 0.977097*** 0.00012*** SSI 6.93E-06 0.232982 0.730899 0.963881*** 0.0000141*** ACB 3.72E-06 0.147323 0.825819 0.973142*** 0.0000425*** SHB 3.65E-05 0.208798 0.54814 0.756938*** 0.00014*** EIB 1.42E-06 0.194467 0.778037 0.972504*** 0.00000604** HAG 7.76E-05 0.124232 0.542425 0.666657*** 0.0000446** ITA 8.59E-06 0.320838 0.669297 0.990135*** 0.0000213*** HPG 5.60E-06 0.208304 0.750002 0.958306*** 0.0000186*** PVS 6.15E-06 0.14058 0.836732 0.977312*** 0.0000693*** REE 5.90E-07 0.305537 0.720895 1.026432*** 0.00000253*** DPM 1.12E-05 0.232344 0.68423 0.916574*** 0.0000281*** KBC 1.06E-05 0.227113 0.679671 0.906784*** 0.0000557*** PVF 1.13E-05 0.143178 0.788463 0.931641*** 0.000128*** SJS 5.67E-06 0.204362 0.760027 0.964389*** 0.0000186*** VCB 1.52E-05 0.410761 0.468426 0.879187*** 0.0000786*** CTG 4.97E-06 0.214901 0.713324 0.928225*** 0.000037*** FPT 8.31E-06 0.217123 0.717007 0.93413*** 0.0000342*** PPC 7.46E-06 0.253988 0.714312 0.9683*** 0.000027*** PVI 1.59E-05 0.22646 0.708386 0.934846*** 0.0000588*** VSH 3.98E-05 0.141784 0.511021 0.652805*** 0.0000398*** CII 4.87E-06 0.179292 0.787224 0.966516*** 0.0000197*** PVD 7.82E-06 0.203557 0.734728 0.938285*** 0.0000189*** VIC 6.75E-06 0.162949 0.785139 0.948088*** 0.0000211** VNM 6.67E-06 0.377213 0.590968 0.968181*** 0.0000299*** BCI 3.43E-06 0.195066 0.804337 0.999403*** 0.0000297*** GMD 1.53E-06 0.208462 0.782635 0.991097*** 0.00000108** AGR 1.60E-05 0.161307 0.671333 0.83264*** 0.0000314*** PHR 4.63E-05 0.131217 0.551965 0.683182 0.0000541*** DHG 1.35E-05 0.243012 0.601114 0.844126*** 0.0000162*** DPR 8.51E-06 0.215007 0.721856 0.936863*** 0.00000767* MSN 1.11E-05 0.122255 0.790344 0.912599*** 0.0000289*** VPL 1.69E-05 0.296693 0.597531 0.894224*** 0.0000224*** ***, ** & * indicate statistical significance at 1%, 5% and 10% level respectively 46 Table A5 TGARCH(1,1) model with and without volume for firms Panel A Restricted TGARCH(1,1) Firm KLS PVX STB VCG SSI ACB SHB EIB HAG ITA HPG PVS REE DPM KBC PVF SJS VCB CTG FPT PPC PVI VSH CII PVD VIC VNM BCI GMD AGR PHR DHG DPR MSN VPL  0.0000134*** 0.087631 0.000014** 0.041984 0.0000063*** 0.049891 0.0000127*** 0.076452 0.00000851*** 0.084817 0.00000499*** 0.085032** 0.0000063*** -0.00124 0.000000885*** 0.137817** 0.00000417*** 0.040434 0.00000694*** 0.034134 0.00000457*** 0.106072* 0.0000102*** -0.012559 0.000000364*** 0.022626 0.0000149*** 0.068647 0.0000146*** 0.064621 0.0000132*** 0.005496 0.00000438*** 0.026427 0.000034*** -0.084905 0.00000652*** 0.032773 0.00000772*** 0.062234 0.00000987*** 0.002097 0.0000175*** 0.047297 0.00000842*** 0.108236* 0.00000528*** 0.018038 0.00000784*** 0.029989 0.0000071*** 0.053488 0.00000143*** 0.028048 0.00000316** -0.018699 0.00000151*** 0.037527 0.0000146** -0.109554 0.0000037*** 0.072575 0.00000312*** 0.038801 0.00000684*** 0.060207 0.000011 -0.005078 0.0000106*** 0.063509 Constant Panel B Unrestricted TGARCH (1,1) Firm Constant KLS PVX STB VCG SSI ACB SHB EIB HAG ITA HPG PVS REE DPM KBC PVF SJS VCB CTG FPT PPC PVI VSH CII PVD VIC VNM BCI GMD AGR PHR DHG DPR MSN VPL 0.0000678*** 0.000129*** 0.00000506*** 0.00000688* 0.00000595*** 0.00000397*** 0.0000338*** 0.000006*** 0.00000276*** 0.00000732*** 0.00000494*** 0.000006*** 0.00000416 0.0000105*** 0.00000824*** 0.0000112 0.0000056*** 0.0000138*** 0.00000383*** 0.00000684*** 0.00000678* 0.0000156*** 0.00000573*** 0.00000492*** 0.00000742*** 0.0000293* 0.00000698* 0.00000361*** 0.00000157*** 0.0000208*** 0.0000227** 0.0000158*** 0.00000894*** 0.0000117* 0.0000163***  0.08744 -0.001958 0.054687 0.130005* 0.119382* 0.062017* -0.011156 0.050946 0.058814 0.045103 0.128087* 0.021255 0.096231*** 0.063848 0.090671 0.016441 -0.004884 -0.042948 0.08946 0.087114 0.047459 0.063798 0.130073*** 0.024018 0.050418 0.056622 0.122045** -0.046598 0.038109 -0.121683* 0.05778 0.077459 0.078854 0.020492 0.078427 Volume 0.0000664*** 0.000134*** 0.0000236*** 0.000128*** 0.0000178*** 0.0000424*** 0.000131*** 0.000012*** 0.0000408*** 0.0000273*** 0.0000197*** 0.0000718*** 0.0000102*** 0.0000274*** 0.0000541*** 0.000127*** 0.0000185*** 0.0000733*** 0.0000353*** 0.0000336*** 0.0000282*** 0.0000598*** 0.0000349*** 0.0000197*** 0.0000198*** 0.0000423*** 0.0000322*** 0.0000295*** 0.0000011** 0.0000423*** 0.0000378*** 0.0000174*** 0.00000931* 0.0000293*** 0.0000214*** ***, ** and * indicate statistical significance at 1%, 5%, and 10% level respectively 47 Table A6 List of 37 sample firms with their symbol Name` Symbol Market capitalization (billion dong) Proportion to the index (at 21/05/ 2011) Asia Comercial Bank ACB 20,441.29 19.05% Agribank Securities Joint Stock Corporation AGR 2,196.48 2.05% Binh Chanh Construction Investment Shareholding Company BCI 1,647.69 0.29% Bao Viet Holdings Hochiminh City Infrastructure Investment Joint Stock Company BVH 55,458.42 9.64% 2,350.04 0.41% 48,551.33 8.44% CII Vietnam Joint Stock Commercial bank for Industry and Trade CTG Hau Giang Pharmaceutical Joint Stock Company Development Investment Construction Joint Stock Corporation DHG 3,364.12 0.58% DIG 2,142.44 0.37% Petrovietnam Fertilizer and Chemical Joint Stock Company DPM 13,028.85 2.27% Dong Phu Rubber Joint Stock Company DPR 2,429.50 0.42% Vietnam Export Import Commercial Joint Stock Bank EIB 15,840.10 2.75% FPT Corporation FPT 9,940.50 1.73% General Forwarding & Agency Corporation GMD 2,767.76 0.48% Hoang Anh Gia Lai Corporation HAG 16,354.82 2.84% Hoa Phat Corporation HPG 10,806.89 1.88% Tan Tao Investment Industry ITA 3,781.77 0.66% Kinh Bac City Development Share Holding Corporation KBC 6,200.87 1.08% Kim Long Securities Corporation KLS 1,883.25 1.75% Masan Group Corporation MSN 52,557.77 9.14% Phuoc Hoa Rubber Joint Stock Company PHR 2,447.24 0.43% Pha Lai Thermal Power Joint Stock Company PPC 2,779.13 0.48% VietNam Driling & Well Services Joint Stock Company PVD 10,801.62 1.88% 48 PetroVietnam Finance Joint Stock Corporation PVF 14,400.00 2.50% PVI Holdings PVI 2,460.00 2.29% PetroVietnam Technical Services Corporation Petro Vietnam Construction Joint Stock Corporation PVS 5,479.56 5.11% PVX 2,950.00 2.75% REE 2,049.21 0.36% SHB 4,522.18 4.21% Refrigeration Electrical Engineering Corporation Saigon-Hanoi Commercial Joint Stock Bank Song Da Urban & Industrial Zone Investment and Development Joint Stock Company SJS 3,238.67 0.56% Saigon Securities Incorporation SSI 6,648.93 1.16% STB 11,382.25 1.98% VCB 55,049.00 9.57% VCG 5,460.00 5.09% VIC 41,402.69 7.20% VNM 40,284.15 7.00% VPL 12,329.91 2.14% VSH 1,981.96 0.34% VN index VNI 575,084.21 Hn index HNX 107,309.98 SaiGon Thuong Tin Commercial Joint Stock Bank Joint Stock Commercial Bank For Foreign Trade Of Vietnam Vietnam Construction And Import - Export Joint Stock Corp VINCOM Joint Stock Company Vietnam Dairy Products Joint Stock Company Vinpearl Joint Stock Company Vinh Son – Song Hinh Hydropower Joint Stock Company 49 ... thesis investigated the relationship between return and trading volume in the Vietnam’s stock market in the context of Granger causality test and GARCH model test The sample, including two market indices... positive relationship between trading volume and price volatility, and negative relationship between price volatility and lagged trading volume Applying the model of Bessembinder and Seguin (1993),... including stock price, the index and trading volume, are stationary and co-integrated Therefore, it becomes necessary to conduct various prior tests of integration and cointegration In so doing,