(Luận văn) management of market risk, case study of modelling volatility in vietnam stock market

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY t to ng    hi ep w n lo MASTER OF BUSINESS ADMINISTRATION ad ju y th yi pl al n ua MANAGEMENT OF MARKET RISK: CASE STUDY OF MODELLING VOLATILITY IN VIETNAM STOCK MARKET n va fu ll    oi m at nh z z ht vb BY k jm LAM VAN BAO DAN om l.c gm n a Lu n va y te re HO CHI MINH CITY – 2012 t to ng MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY FALCULTY OF BUSINESS ADMINISTRATION hi ep    w n lo ad ju y th MASTER OF BUSINESS ADMINISTRATION yi pl ua al n MANAGEMENT OF MARKET RISK: CASE STUDY OF MODELLING VOLATILITY IN VIETNAM STOCK MARKET n va ll fu oi m    at nh z z k jm LAM VAN BAO DAN ht vb BY om VO XUAN VINH l.c gm SUPERVISOR n a Lu n va 2012 y te re A thesis submitted in partial fulfillment of the requirements for the degree of Master of Business Administration Master of Business Administration Lam Van Bao Dan t to CERTIFICATION ng hi ep “I certify that the substance of this thesis has not already been submitted for any degree and is not being currently submitted for any other degree w n lo I certify that, to the best of my knowledge, any help received in preparing this thesis, ad and all sources used have been acknowledged in this thesis” ju y th yi pl n ua al LAM VAN BAO DAN va n Date: 25th April, 2012 ll fu oi m at nh z z ht vb k jm om l.c gm n a Lu n va y te re K17-EMBA Page 2012 Master of Business Administration Lam Van Bao Dan t to Abstract ng hi ep The thesis concerns with market risk management It has implications for businesses and investors, especially those hold investment in stocks In particular, the thesis w n investigates the technique to model stock volatility in Vietnam stock market lo ad The rapid growth of Vietnam stock market recently has received a great attraction of y th local and global investors However, like other emerging stock markets, this growth ju has accompanied with high risk Over the past thirty years, a huge number of articles yi pl have discussed the volatility of stock returns in developed and emerging capital ua al markets Unfortunately, even though Vietnam stock market has started trading from n 2000, there has been relatively little work done on modelling and forecasting the return va volatility in Vietnam stock market n ll fu This thesis employ the GARCH type models, both symmetric and asymmetric oi m including ARCH (1), GARCH (1,1), GARCH-M (1,1), EGARCH (1,1) and TGARCH nh (1,1) to examine the sufficient models for capturing the characteristics of the return at volatility in Vietnam stock market The data set of VN-Index over nine year period z z from March, 2002 to December, 2011 which divided into four periods including before vb crisis, crisis, recovering and whole period The findings suggest the sufficiency of ht k jm ARCH (1), GARCH (1,1) and GARCH-M (1,1) models in capturing properties of gm conditional variance in Vietnam stock market The results also provide the indicator of l.c the risk-reward relationship and show the weak evidence of asymmetry in the return om series in Vietnam stock market n a Lu n va y te re K17-EMBA Page 2012 Master of Business Administration Lam Van Bao Dan t to Table of Contents Page ng hi ep INTRODUCTION 1.1 Background of the Thesis 1.2 Research Questions and Objectives 11 I w Research Objectives and Implications 11 lo Research Questions 11 ad n 1.2.1 1.2.2 y th Vietnam Stock Market Overview 11 pl VN-Index 16 ua al 1.3.2 Introduction 11 yi 1.3.1 ju 1.3 Outline of the Thesis 20 II LITERATURE REVIEW 21 2.1 Volatility Definition 21 2.2 The Characteristics of Volatility in Financial Market 22 2.3 Literature Review 23 III DATA AND METHODOLOGY 35 3.1 Data 35 3.2 Descriptive Statistics 37 n 1.4 n va ll fu oi m at nh z z ht vb Histogram and Statistics Definition 37 3.2.2 Descriptive Statistics of Return Series for the Period before Crisis 39 3.2.3 Descriptive Statistics of Return Series for Crisis Period 40 3.2.4 Descriptive Statistics of Return Series for Recovering Period 41 3.2.5 Descriptive Statistics of Return Series for the Whole Period 42 3.2.6 Conclusions 43 k jm 3.2.1 om l.c gm n a Lu Methodology 44 3.3.3 GARCH Models 46 K17-EMBA Page 2012 y Testing for ARCH Effects 45 te re 3.3.2 n va 3.3 Master of Business Administration Lam Van Bao Dan t to ng hi ep IV EMPIRICAL RESULTS 53 4.1 Testing for ARCH Effect 53 4.2 Empirical Results of Different Periods 54 w n 4.2.1 Empirical Results of the Period before Crisis 54 4.2.2 Empirical Results of the Crisis Period 57 lo ad 4.2.3 Empirical Results of the Recovering Period 58 y th 4.2.4 Empirical Results of the Whole Period of Vietnam Stock Market 59 ju SUMMARY AND IMPLICATIONS 62 5.1 Summary and Implications 62 5.2 Limitations and Recommendations for Further Research 63 VI APPENDIX 65 6.1 Appendix-1: Testing for ARCH Effect 65 yi V pl n ua al n va fu Before Crisis Period (From March, 2002 to December, 2007) 65 6.1.2 Crisis Period (From January, 2008 to December, 2009) 66 6.1.3 Recovering Period (From January, 2010 to December, 2011) 67 6.1.4 Whole Period (From March, 2002 to December, 2011) 68 ll 6.1.1 oi m at nh z z Appendix-2: GARCH Models Analysis 69 vb 6.2 Before Crisis Period (From March, 2002 to December, 2007) 69 6.2.2 Crisis Period (From January, 2008 to December, 2009) 74 6.2.3 Recovering Period (From January, 2010 to December, 2011) 79 6.2.4 Whole Period (From March, 2002 to December, 2011) 84 ht 6.2.1 k jm om l.c gm REFERENCES 89 n a Lu n va y te re K17-EMBA Page 2012 Master of Business Administration Lam Van Bao Dan t to List of Tables ng hi Table No Description Page ep w Price limitations in HOSE over different periods 16 Table Summary for estimation results of before crisis period 60 Summary for estimation results of crisis period 60 Summary for estimation results of recovering period 61 n Table lo ad Table y th Table ju Summary for estimation results of whole period 61 yi Table pl ua al List of Figures n n va Figure No Description Page fu 14 Number of listed company from 2000 to 2011 Figure Market capitalization from 2000 to 2011 Figure Number of securities companies from 2000 to 2011 Figure Number of trading accounts from 2000 to 2011 Figure Performance of VN-Index from 2000 to 2011 Figure Performance of VN-Index in 2007 Figure Performance of VN-Index in 2009 z 15 z vb 17 ht jm 18 40 om l.c a Lu 41 n 42 n va te re 43 y Page gm Histogram of daily return series of VN-Index (01/03/2002 – 28/12/2007) Histogram of daily return series of VN-Index (02/01/2008 – 31/12/2009) Histogram of daily return series of VN-Index (04/01/2010 – 30/12/2011) Histogram of daily return series of VN-Index (01/03/2002 – 30/12/2011) 18 k K17-EMBA 15 at Figure 11 nh Figure 10 14 oi Figure m Figure ll Figure 2012 Master of Business Administration Lam Van Bao Dan t to Acknowledgement ng hi I would like to say special thanks to my supervisor, Dr Vo Xuan Vinh for his helpful ep directions, encouragements and valuable comments in preparing this thesis w I would like to thank all lecturers in EMBA program, especially to Dr Tran Ha Minh n lo Quan for his help ad I would also like to thank all my friends in the program for supporting and encouraging y th me to finish this thesis ju yi Finally, special thanks also go to my wife and my family for their love and staying pl beside me during my study n ua al n va ll fu oi m at nh z z ht vb k jm om l.c gm n a Lu n va y te re K17-EMBA Page 2012 Master of Business Administration t to ng hi I INTRODUCTION 1.1 Background of the Thesis Lam Van Bao Dan ep Investing in emerging stock markets can make a large return but also creates a big loss w for businesses because of high volatility (high risk) Therefore, finding a technique to n lo model volatility is important for businesses and investors investing in stock market ad This thesis will investigate the volatility models which best fits the Vietnam stock y th ju market conditions Modelling volatility will help businesses and investors understand yi and better manage risks involved in their investment pl ua al Volatility is more and more important in financial market There are a huge number of researches and discussions for volatility in the past thirty years and especially in the n n va recent years This is because volatility is a special indicator in financial market It is a ll fu key factor in many securities pricing formula as well as the value-at-risk models oi m Even though volatility is unobservable, it plays an important role in making investment decision On the other hand, it is also the interest of the policy makers in financial nh at markets The policy makers are interested in the impact of volatility on the stability of z the financial market and hence on the economy z ht vb Because of the above implications, volatility is the focus of several studies for jm estimation and forecast The volatility index (VIX) and Nasdaq Volatility Index (VXN) k that defined as a weighted of prices for a range of options on the S&P 500 index and gm the Nasdaq 100 index have started trading from 2006 It is calculated in real time by om l.c Chicago Board Option Exchange (CBOE) These are two of the world’s most popular index of investors concerning to future stock market volatility The goal is to estimate a Lu the implied volatility of the stock market over the next 30 days It is proven that the n There are a lot of models that can be implied for modelling and forecasting volatility y te re including ARCH/GARCH models and non-GARCH models However, ARCH model n va low volatility index, the high trader confidence K17-EMBA Page 2012 Master of Business Administration Lam Van Bao Dan t to proposed by Engle (1982) and generalized by Bollerslev (1986) are said to be most ng hi sufficient for capturing the characteristics of the time varying stock return volatility ep From the introduction of the GARCH model, a huge number of GARCH extensions or GARCH family such as GARCH in mean (GARCH-M) (Bollerslev, 1986), EGARCH w n (Nelson, 1991), Threshold GARCH (TGARCH) (Glosten, Jagannathan and Runkle, lo ad 1993), Asymmetric GARCH model (AGARCH) (Engle, 1990), etc have been studied y th and proven to be sufficient for modelling and forecasting stock return volatility ju yi However, different papers support different models and show the conflicts in pl implication The empirical results argue that different models are suitable for different al ua markets and in different time periods n Therefore, we will employ several widely accepted GARCH models including ARCH, va n GARCH, GARCH-M, TGARCH and EGARCH to investigate the volatility of fu ll Vietnam stock market in this thesis From the results of the study, we will suggest the m oi sufficient GARCH models for capturing the properties of return volatility in Vietnam at nh stock market z There have been numerous researches focusing on modelling stock price volatility z However, most of them have discussed about the developed capital markets The vb ht emerging markets have not received much attention Recently, the emerging markets, jm especially the fast development countries such as China, Brazil, India, Russia, Mexico k gm and the ASEAN countries has increasingly attracted the investors to diversify their l.c portfolios om Vietnam stock market has just been traded more than ten years It has significantly a Lu developed in recent years and has received a great attraction of many investors, both n local and foreign They made considerable amount of profits during the boom time of va 2006-2007 However, the market went down in 2008 and 2009 due to the effect of n y te re world financial crisis that results in a big loss for many businesses and investors K17-EMBA Page 2012 Master of Business Administration Lam Van Bao Dan t to TGARCH (1,1) ng hi ep w n lo Dependent Variable: RETURN Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/01/12 Time: 11:21 Sample (adjusted): 498 Included observations: 497 after adjustments Convergence achieved after 41 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(2) + C(3)*RESID(-1)^2 + C(4)*RESID(-1)^2*(RESID(-1)