MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY --- oOo --- NGUYỄN THỊ KIM NGÂN VOLATILITY IN STOCK RETURN SERIES OF VIETNAM STOCK MARKET MASTER THESIS Ho Chi Minh City – 2011 TIEU LUAN MOI download : skknchat@gmail.com MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY ----------o0o--------- NGUYỄN THỊ KIM NGÂN VOLATILITY IN STOCK RETURN SERIES OF VIETNAM STOCK MARKET MAJOR: BANKING AND FINANCE MAJOR CODE: 60.12 MASTER THESIS INSTRUCTOR: Dr. VÕ XUÂN VINH Ho Chi Minh City – 2011 TIEU LUAN MOI download : skknchat@gmail.com ACKNOWLEDGEMENT At first, I would like to show my sincerest gratitude to my supervisor, Dr. Vo Xuan Vinh, for his valuable time and enthusiasm. His whole-hearted guidance, encouragement and strong support during the time from the initial to the final phase are the large motivation for me to complete my thesis.
I also would like to thank all of my lecturers at Faculty of Banking and Finance, University of Economics Hochiminh City for their English program, knowledge and teaching during my master course at school. In addition, my thanks also go to my beloved family for creating good and convenient conditions for me throughout all my studies at University as well as helping me overcome all the obstacles to finish this thesis. Lastly, I offer my regards and blessings to all of those who supported me in any respects during the completion of the study. i TIEU LUAN MOI download : skknchat@gmail.com ABSTRACT 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 using the iterated cumulative sums of squares (ICSS) algorithm.
The relationship between Vietnam stock market’s volatility shifts and impacts of global crisis is also detected. Using a long-span data, the results show that daily stock returns can be characterized by GARCH and GARCH in mean (GARCH-M) models while threshold GARCH (T-GARCH) is not suitable. About structural breaks, when applying ICSS to the standardized residuals filtered from GARCH (1, 1) model, the number of sudden jumps significantly decreases in comparison with the raw return series. Events corresponding to those breaks and altering the volatility pattern of stock return are found to be country-specific.
Not any shifts are found during global crisis period. In addition, because the research is not able to point out exactly what events caused sudden changes, the analysis on relationship between these information and shifts is just in relative meaning. Further evidence also reveals that when sudden shifts are taken into account in the GARCH models, reduction in the volatility persistence is found. It suggests that many previous studies may have overestimated the degree of volatility persistence existing in financial time series.
The small value of coefficients of the dummies representing breakpoints in modified GARCH model implies that the conditional variance of stock return is much affected by past trend of observed shocks and variance. Our results have important implications regarding advising investors on decisions concerning pricing equity, portfolio investment and management, hedging and forecasting. Moreover, it is also helpful for policy-makers in making and promulgating the financial policies. ii TIEU LUAN MOI download : skknchat@gmail.com TABLE OF CONTENTS ACKNOWLEDGEMENT.
ii TABLE OF CONTENTS. iii LIST OF FIGURES. v LIST OF TABLES. Common characteristics of return series in the stock market.
Volatility models suitable to the stock return characteristics. Identification of breakpoints in volatilities and influence of the regime changes. Events related to regime changes. Sudden changes in economic recession?.
Overstatement of ICSS algorithm in raw returns series. Testing for stationarity. Unit root test. Moving average processes - MA(q).
Autoregressive processes - AR(p). Information criteria for ARMA model selection. ARCH & GARCH Model. 23 iii TIEU LUAN MOI download : skknchat@gmail.
Combination of GARCH model and sudden changes. 26 5: DATA AND EMPIRICAL RESULTS. Suitable models for stock return series of Vietnam. Choosing suitable ARMA model.
Test for ARCH effect. Identification of break points and detection of related events. Breakpoints in raw returns. Breakpoints in filtered returns.
Analysis of each volatility period. General comments on events and volatility corresponding to sudden changes detected by ICSS algorithm. Combined model after including dummies. 60 Implications of the research.
60 Limitations of the study. Descriptive statistics of Vietnam stock market’s daily stock return. Correlogram and Q-statistic of VNIndex daily rate of return. Unit Root Test on VNIndex’s daily return.
Summary for estimation results of all ARMA models. Statistically significant ARMA models with C constants. Statistically significant ARMA models without C constants. Estimation results of GARCH models.
Estimation results of GARCH-M models. Estimation result of TGARCH model. Estimation result of GARCH model modified with sudden changes. ICSS code on WINRAT.
81 iv TIEU LUAN MOI download : skknchat@gmail.com LIST OF FIGURES Figure 5. Daily return series on HOSE. Structural breakpoints in volatility in raw returns. Structural breakpoints in volatility in filtered returns.
39 v TIEU LUAN MOI download : skknchat@gmail.com LIST OF TABLES Table 5. Descriptive statistics of Vietnam stock market’s daily return series. Unit Root Test on VNIndex’s daily return. Empirical results of different ARMA models.
ARCH effect at 7th lag. Empirical results of different GARCH-family models. Breakpoints detected by ICSS algorithm in the raw returns. Breakpoints detected by ICSS algorithm in the filtered returns.
40 vi TIEU LUAN MOI download : skknchat@gmail.com ABBREVIATIONS CPI Consumer Price Index GARCH Generalized Autoregressive Conditional Heteroscedasticity GARCH-M GARCH in Mean GDP Gross Domestic Product HOSE Ho Chi Minh City Stock Exchange HOSTC Ho Chi Minh City Securities Trading Center ICSS algorithm Iterated Cumulative Sums of Squares algorithm SSC State Securities Committee of Vietnam TGARCH Threshold GARCH VND Vietnam Dong vii TIEU LUAN MOI download : skknchat@gmail.com Volatility in Stock Return Series of Vietnam Stock Market 1: INTRODUCTION 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 unpredictability, uncertainty or risk. Volatility is unobservable in financial market and it is measured by standard deviation or variance of return which can be directly considered as a measure of risk of assets.
Considerable volatilities have been found in the past few years in mature and emerging financial markets worldwide. As a proxy of risk, modelling and forecasting stock market volatility has become the subject of vast empirical and theoretical investigations over the past decades by academics and practitioners. Substantial changes in the volatility of financial market returns are capable of having significant effects on risk averse investors. Furthermore, such changes can also impact on consumption patterns, corporate capital investment decisions, leverage decisions and other business cycle.
Volatility forecasts of stock price are crucial inputs for pricing derivatives as well as trading and hedging strategies. Therefore, it is important to understand the behavior of return volatility. In addition to return volatility, some relevant problems attracting much interest of researchers have been whether or not major events may lead to sudden changes in return volatility and how unanticipated shocks will affect volatility over time. Concerning these factors, persistence term should be considered.
Persistence in variance of a random variable refers to the property of momentum in conditional variance or past volatility can explain current volatility in some certain levels. The 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 predicting how events will affect volatility in stock returns and partially determines stock prices. Poterba and Summers (1986) showed that the extent to which stock- return volatility affects stock prices (through a time-varying risk premium) depends critically on the permanence of shocks to variance.
Hence, the degree to which 1 TIEU LUAN MOI download : skknchat@gmail.com Volatility in Stock Return Series of Vietnam Stock Market conditional variance is persistent or permanent in daily stock-return data is an important economic issue. ARCH models proposed by Engle and Bollerslev (1982) and generalized by Bollerslev (1986) and Taylor (1986) have been proved to be sufficient in capturing properties of time-varying stock return volatility as well as volatility persistence. Literature has found many evidences in supporting the capability of GARCH models in volatility estimation (Akgiray (1989) and Pagan, Adrian R. (1989)) rather than other non-GARCH models.
Since the introduction of simple GARCH models, a huge number of extensions and alternative specifications such as GARCH in mean (GARCH-M), Threshold GARCH (Glosten, Jagannathan et al. (1993)), has been proposed in attempt to better capture the characteristics of return series. Meanwhile, a procedure based on an iterated cumulative sums of squares (ICSS) by Inclan and Tiao (1994) is commonly used to detect number of significant/ sudden changes in variance of time series, as well as to estimate the time points and magnitude of each detected sudden change in the variance. While studies on stock markets in mature and emerging markets are widely available, so far not many researches have focused on Vietnam.
Although being set up much later than many countries in the world, since the establishment of the first securities trading center of Vietnam Stock Market in Ho Chi Minh City (HOSTC) on 28 July 2000, Vietnam stock market has been growing rapidly with improved transaction volume and market capitalization. At the opening trading session, only two stocks with a total market capitalization of VND986 billion (about 0.28% of GDP of Vietnam) were traded at the market. Vietnam stock market was then characterized by the illiquidity of stocks, incomplete legal framework and insufficient corporate governance system. However, over time, along with the development and world integration of Vietnam’s economy, it has gradually become a critical channel in terms of mobilizing and distributing capital for short and long- term investments, which contribute to the expansion of business operations as well as development of overall domestic economy.
Over 10 years of operation (until the 2 TIEU LUAN MOI download : skknchat@gmail.com Volatility in Stock Return Series of Vietnam Stock Market end of 2010), along with equitization itinerary, the number of listed companies has increased to 280 firms with a total market capitalization of VND591 trillion. The market capitalization represents about 30% of the country’s GDP in 2010 (equivalent to VND1,980,000 billion by the General Statistic Office), much higher than the amount in 2000. Total stock value bought by foreign investors reached over VND15 trillion. The stocks in HOSTC can be represented by VNIndex which is a market-value-weighted index of all commons stocks on the HOSTC.
The high and rapid growth of Vietnam stock market is, of course, very appealing to domestic and foreign investors. The main objective of this study is to investigate and to model the characteristics of stock return volatility in Vietnam stock market. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH(p, q)) model is used to capture the nature of volatility; GJG model (or TGARCH) and GARCH-in-mean (GARCH-M) are for examining leverage effects and risk – return premium respectively. Meanwhile, a procedure based on iterated cumulative sums of squares (ICSS) is used to detect number of (significant) sudden changes in variance in time series, to estimate the time points and magnitude of each detected sudden changes in the variance.
Major events surrounding the time points of increased volatility are also analyzed. At the same time, the linkage between volatility shifts in Vietnam stock market with impacts from global crisis in US in 2008 is also mentioned. These detected volatility regimes are then included in the standard GARCH model to calculate the "true" estimate of volatility persistence. To solve the problem mentioned above, four research questions needed to be answered are: Question 1: What are characteristics of return volatility in Vietnam’s stock market? Are they similar to the results gained from previous researches? Question 2: Which volatility models are suitable to the stock return characteristics found out? 3 TIEU LUAN MOI download : skknchat@gmail.