(Luận văn) volatility in stock return series of vietnam stock market

t to ng hi ep MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY w n - oOo - lo ad ju y th yi pl ua al n NGUYỄN THỊ KIM NGÂN n va ll fu m oi VOLATILITY IN STOCK RETURN SERIES at nh OF z z VIETNAM STOCK MARKET k jm ht vb om l.c gm MASTER THESIS n a Lu n va y te re th Ho Chi Minh City – 2011 t to ng hi ep MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY w o0o - n lo ad ju y th yi pl ua al n NGUYỄN THỊ KIM NGÂN n va ll fu oi m VOLATILITY IN STOCK RETURN SERIES at nh OF z VIETNAM STOCK MARKET z jm ht vb k MAJOR: BANKING AND FINANCE MASTER THESIS n a Lu INSTRUCTOR: Dr VÕ XUÂN VINH om l.c gm MAJOR CODE: 60.31.12 n va y te re th Ho Chi Minh City – 2011 t to ng ACKNOWLEDGEMENT hi ep At first, I would like to show my sincerest gratitude to my supervisor, Dr Vo Xuan w Vinh, for his valuable time and enthusiasm His whole-hearted guidance, n encouragement and strong support during the time from the initial to the final phase lo ad are the large motivation for me to complete my thesis y th ju I also would like to thank all of my lecturers at Faculty of Banking and Finance, yi University of Economics Hochiminh City for their English program, knowledge and pl ua al teaching during my master course at school n In addition, my thanks also go to my beloved family for creating good and va n convenient conditions for me throughout all my studies at University as well as ll fu helping me overcome all the obstacles to finish this thesis oi m respects during the completion of the study at nh Lastly, I offer my regards and blessings to all of those who supported me in any z z k jm ht vb om l.c gm n a Lu n va y te re th i t to ng ABSTRACT hi ep This thesis studies the features of the stock return volatility and the presence of structural breaks in return variance of VNIndex in the Vietnam stock market by w using the iterated cumulative sums of squares (ICSS) algorithm The relationship n lo between Vietnam stock market’s volatility shifts and impacts of global crisis is also ad detected Using a long-span data, the results show that daily stock returns can be y th ju characterized by GARCH and GARCH in mean (GARCH-M) models while yi threshold GARCH (T-GARCH) is not suitable About structural breaks, when pl applying ICSS to the standardized residuals filtered from GARCH (1, 1) model, the al ua number of sudden jumps significantly decreases in comparison with the raw return n series Events corresponding to those breaks and altering the volatility pattern of va n stock return are found to be country-specific Not any shifts are found during global fu ll crisis period In addition, because the research is not able to point out exactly what m oi events caused sudden changes, the analysis on relationship between these at nh information and shifts is just in relative meaning Further evidence also reveals that z when sudden shifts are taken into account in the GARCH models, reduction in the z volatility persistence is found It suggests that many previous studies may have vb ht overestimated the degree of volatility persistence existing in financial time series k jm The small value of coefficients of the dummies representing breakpoints in much affected by past trend of observed shocks and variance l.c gm modified GARCH model implies that the conditional variance of stock return is om Our results have important implications regarding advising investors on decisions a Lu concerning pricing equity, portfolio investment and management, hedging and forecasting Moreover, it is also helpful for policy-makers in making and n n va promulgating the financial policies y te re th ii t to ng TABLE OF CONTENTS hi ep ACKNOWLEDGEMENT i ABSTRACT ii w TABLE OF CONTENTS iii n LIST OF FIGURES v lo ad LIST OF TABLES vi y th ABBREVIATIONS vii ju 1: INTRODUCTION yi pl 2: LITERATURE REVIEW al 2.1 Common characteristics of return series in the stock market n ua 2.2 Volatility models suitable to the stock return characteristics va 2.3 Identification of breakpoints in volatilities and influence of the regime changes n 2.4 Events related to regime changes fu ll 2.5 Sudden changes in economic recession? 10 m oi 2.6 Overstatement of ICSS algorithm in raw returns series 10 nh 3: HYPOTHESES 12 at 4: RESEARCH METHODS 13 z z 4.1 Stationarity 13 ht vb 4.2 Testing for stationarity 14 jm 4.2.1 Autocorrelation diagram 14 k 4.2.2 Unit root test 15 gm 4.3 GARCH model 16 l.c 4.3.1 ARMA 16 om 4.3.1.1 Moving average processes - MA(q) 17 a Lu 4.3.1.2 Autoregressive processes - AR(p) 17 n 4.3.1.3 ARMA processes 18 4.3.2.1 ARCH Model 20 4.5 GARCH-M model 23 iii th 4.4 TGARCH Model 22 y 4.3.2.2 GARCH Model 21 te re 4.3.2 ARCH & GARCH Model 20 n va 4.3.1.4 Information criteria for ARMA model selection 19 t to ng 4.6 ICSS algorithm 24 hi ep 4.7 Combination of GARCH model and sudden changes 26 5: DATA AND EMPIRICAL RESULTS 27 w 5.1 Data 27 n lo 5.2 Empirical results 29 ad 5.2.1 Suitable models for stock return series of Vietnam 29 y th 5.2.1.1 Choosing suitable ARMA model 29 ju 5.2.1.2 Test for ARCH effect 30 yi 5.2.1.3 GARCH models 31 pl al 5.2.2 Identification of break points and detection of related events 33 n ua 5.2.2.1 Breakpoints in raw returns 33 va 5.2.2.2 Breakpoints in filtered returns 38 n 5.2.2.3 Analysis of each volatility period 44 fu ll 5.2.2.4 General comments on events and volatility corresponding to sudden oi m changes detected by ICSS algorithm 57 nh 5.2.3 Combined model after including dummies 57 at 6: CONCLUSION 60 z Implications of the research 60 z ht vb Limitations of the study 61 jm REFERENCE 62 k APPENDIX 66 gm Table A1 Descriptive statistics of Vietnam stock market’s daily stock return 66 l.c Table A2 Correlogram and Q-statistic of VNIndex daily rate of return 67 om Table A3 Unit Root Test on VNIndex’s daily return 68 a Lu Table A4 Summary for estimation results of all ARMA models 69 Table A5 Statistically significant ARMA models with C constants 70 n Table A8 Estimation results of GARCH-M models 77 Table A11 ICSS code on WINRAT 81 iv th Table A10 Estimation result of GARCH model modified with sudden changes 80 y Table A9 Estimation result of TGARCH model 79 te re Table A7 Estimation results of GARCH models 74 n va Table A6 Statistically significant ARMA models without C constants 72 t to ng hi ep LIST OF FIGURES w Figure 5.1 Daily return series on HOSE 29 n lo Figure 5.2 Structural breakpoints in volatility in raw returns 38 ad Figure 5.3 Structural breakpoints in volatility in filtered returns 39 ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th v t to ng hi ep LIST OF TABLES w Table 5.1 Descriptive statistics of Vietnam stock market’s daily return series 27 n lo Table 5.2 Unit Root Test on VNIndex’s daily return 28 ad Table 5.3 Empirical results of different ARMA models 30 y th Table 5.4 ARCH effect at 7th lag 31 ju Table 5.5 Empirical results of different GARCH-family models 32 yi pl Table 5.6 Breakpoints detected by ICSS algorithm in the raw returns 33 n ua al Table 5.7 Breakpoints detected by ICSS algorithm in the filtered returns 40 n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th vi t to ng ABBREVIATIONS hi ep w CPI Consumer Price Index GARCH Generalized Autoregressive Conditional Heteroscedasticity n GARCH-M lo GARCH in Mean ad GDP Gross Domestic Product y th yi HOSTC Ho Chi Minh City Stock Exchange ju HOSE Ho Chi Minh City Securities Trading Center pl ICSS algorithm Iterated Cumulative Sums of Squares algorithm SSC al TGARCH Threshold GARCH VND Vietnam Dong n ua State Securities Committee of Vietnam n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th vii Volatility in Stock Return Series of Vietnam Stock Market t to ng 1: INTRODUCTION hi ep Volatility is a fundamental concept in the discipline of finance It can be described broadly as anything that is changeable or variable It is associated with w unpredictability, uncertainty or risk Volatility is unobservable in financial market n lo and it is measured by standard deviation or variance of return which can be directly ad considered as a measure of risk of assets Considerable volatilities have been found y th ju in the past few years in mature and emerging financial markets worldwide As a yi proxy of risk, modelling and forecasting stock market volatility has become the pl subject of vast empirical and theoretical investigations over the past decades by al ua academics and practitioners Substantial changes in the volatility of financial market n returns are capable of having significant effects on risk averse investors va n Furthermore, such changes can also impact on consumption patterns, corporate fu ll capital investment decisions, leverage decisions and other business cycle Volatility m oi forecasts of stock price are crucial inputs for pricing derivatives as well as trading at nh and hedging strategies Therefore, it is important to understand the behavior of z return volatility z In addition to return volatility, some relevant problems attracting much interest of vb ht researchers have been whether or not major events may lead to sudden changes in k jm return volatility and how unanticipated shocks will affect volatility over time gm Concerning these factors, persistence term should be considered Persistence in variance of a random variable refers to the property of momentum in conditional l.c om variance or past volatility can explain current volatility in some certain levels The a Lu larger the persistence is, the higher the past volatility can be explained for the current volatility The persistence in volatility is a key ingredient for accurately n th y critically on the permanence of shocks to variance Hence, the degree to which te re return volatility affects stock prices (through a time-varying risk premium) depends n stock prices Poterba and Summers (1986) showed that the extent to which stock- va predicting how events will affect volatility in stock returns and partially determines