A study of chinese stock market empirical and theoretical explorations by bayesian and GARCH models

A STUDY OF CHINESE STOCK MARKET: EMPIRICAL AND THEORETICAL EXPLORATIONS BY BAYESIAN AND GARCH MODELS CHEN HENG A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY OF ECONOMICS DEPARTMENT OF ECONOMICS NATIONAL UNIVERSITY OF SINGAPORE 2007 ACKNOWLEDGEMENTS I am deeply indebted to my supervisor Professor Wong Wing Keung. His precious advices, supervision and generous support are invaluable and indispensable to the completion of this dissertation and my academic research as a whole. I am also very grateful to my co-supervisor Professor Fong Wai Mun and committee member Dr. Lee Jin. Without their inspiring guidance throughout my candidature, my Ph.D. life could have been an even harder process. My sincerest thanks also go to graduate director Professor Tilak, my thesis examiners Professor Kapur and Dr. Gamini, Officers Mrs. Sagi and Ms. Nicky for their kind suggestions and helps during the past four years. Finally, I have to thank my family for their unremitting patience, encouragement and understanding which have been underpinning my morale in pursuing the doctoral study. i Table of Contents Acknowledgements Summary i iv List of Tables vii List of Figures ix Chapter 1: Why China’s IPOs behave extremely? 1.1 Introduction for Chapter 1.2 Literature review and Hypotheses for China’s market 1.3 Briefing on Methodology 26 1.4 Data Features and Empirical Findings 32 1.5 Summary remark for Chapter 46 Chapter 2: Is a Superpower More Important than a Neighbor? --An Application to China’s Stock Market 48 2.1 Introduction for Chapter 49 2.2 Literature review and motivations 52 2.3 Data and Methodology 57 2.4 Empirical Results 67 2.5 Summary remark for Chapter 82 Chapter 3: A remedy to the flaw of GARCH and Applications to China’s and US Markets 84 3.1 Introduction for Chapter 85 3.2 The flaw of GARCH and a remedy model 91 3.3 The algorithm for estimating stochastic volatility models 97 ii 3.4 Empirical evidences 106 3.5 Summary remark for Chapter 118 Chapter 4: Conclusions 120 Bibliography 124 Appendix A 137 Appendix B 139 Appendix C 140 Appendix D 141 Appendix E 142 iii Summary This study on Chinese stock market consists of three essays laid out as three chapters. The first essay tries to decipher the extreme behavior of China’s initial public offerings (IPOs). The short-run underpricing and long-run overpricing of IPOs observed in almost all stock markets are puzzling phenomena in theoretical as well as practical finance. The more challenging situation is that IPOs on developing markets appear to behave more abnormally than those of developed markets. Especially, China’s market presents the most extreme IPO facts ever documented in literature. Based on the close observations of China’s market, we propose that price manipulation, information asymmetry, hot market and hot issue effects and signaling practice could predict much of the underpricing and long run buy-hold returns of IPOs on China’s market. Among these explanatory factors, price manipulation and information asymmetry arising from privatization process are unique to China’s transitional economy. In addition to the traditional analysis, Bayesian approach which is not much pursued for this purpose is also employed to draw inferences from long run return regression. Our empirical results verify our conjecture that large part of IPO initial return and long run buy-hold return is due to institutional defects of China’s market. The second essay investigates the bilateral cointegration relations between China’s and US stock markets, and between China’s and Hong Kong stock markets. Research on cointegrations among stock markets has important practical implications for portfolio management considering the increasing globalization among international financial markets. Due to economic and geographical considerations, US and Hong Kong are closest stock markets to China, thus understanding the integration between China and these two markets could have iv important implications for policy makers as well as investors. Because usual vector error correction model (VECM) may overlook the long memory feature of cointegration residual series which may biases resulting inferences, we employ fractionally integrated VECM (FIVECM) to investigate the cointegration relations binding China’s stock market to the other two stock markets in the long run. Additionally, by augmenting the FIVECM with multivariate GARCH model, the return transmission and volatility spillover between market return series are revealed simultaneously. Our empirical results show that China’s stock market is fractionally cointegrated with the other two markets, and it appears that China’s stock market has stronger ties with its neighboring Hong Kong market than with world’s superpower, US market. The third essay discusses a flaw of GARCH model and provides a remedy to improve the volatility fitting of GARCH-type model. Since its birth about twenty years ago, GARCH model has incurred a vast body of literature and experienced explosive empirical applications, largely due to its structural beauty and computational tractability. More importantly, GARCH model is well acclaimed because of the capability of capturing clustering effect typical of financial time series. However, the most prominent merit of GARCH model turns out to be a demerit as well. This chapter shows that inclusion of lagged squared residuals in GARCH model artificially generates slow decay of volatility following large shocks. As large shocks to financial series are usually followed by much quieter periods, the slow decay and response of volatility are in obvious contrast with market efficiency assumptions. With detailed arguments, we propose that stochastic volatility model could overcome the flaw of GARCH model. The empirical results using return series of both S&P 500 and China’s stock index have confirmed that stochastic volatility model indeed provides a better fitting on conditional volatility process than GARCH type models. An asymmetric stochastic volatility model has also been proposed and fitted to the v data sets. Interestingly, the opposite conclusions about the leverage effect in asymmetric model fitted on two return series indicate different speculative features and trading behaviors on the two major stock markets. Another contribution of this chapter is that we design a simple yet effective Markov Chain Monte Carlo (MCMC) algorithm to estimate the stochastic volatility models with Bayesian approach. Summarizing remark for each essay is given at the end of each chapter, the final conclusions for the whole study are made in chapter 4. vi List of Tables Table 1.1: International evidence on IPO underpricing Table 1.2: Average IPO underpricing on China’s market Table 1.3: IPO underpricing in China by year 34 Table 1.4: Variables used in Chapter and short descriptions 35 Table 1.5: Descriptive statistics for the variables in Chapter 35 Table 1.6: Correlations among variables used Chapter 37 Table 1.7: The estimated results for the underpricing regression model (1.15) 39 Table 1.8: The MCMC results for half year return regression (1.22) 44 Table 1.9: The MCMC results for one year return regression (1.23) 44 Table 2.1: Descriptive statistics of data in Chapter 58 Table 2.2: Unit root tests for index series in Chapter 68 Table2. 3: DOLS model estimates, dependent variable is “ SHH t ” 68 Table 2.4: Stationarity and long memory tests on cointegration residuals 70 Table 2.5: ARFIMA fit results 70 Table 2.6: Estimates for FIVECM-BEKK(1,1) fitted on (ΔSHH t , ΔSPt )′ 71 Table 2.7: Model diagnostic statistics for ΔSHH t and ΔSPt 75 Table 2.8: Estimates for FIVECM-BEKK(1,1) fitted on (ΔSHH t , ΔHSt )′ 76 Table 2.9: Model diagnostic statistics for ΔSHH t and ΔHSt 79 Table 3.1: Fitted variances around first spike on S&P 92 Table 3.2: Fitted variances around second spike on S&P 92 Table 3.3: Parameter Estimates for SV model on S&P 107 Table 3.4: Fitted variances from SV around first spike on S&P 108 vii Table 3.5: Fitted variances from SV around second spike on S&P 108 Table 3.6: Parameter Estimates for Asym. SV on S&P 109 Table 3.7: Fitted variances from Asym. SV around first spike on S&P 110 Table 3.8: Fitted variances from Asym. SV around second spike on S&P 110 Table 3.9: Fitted variances around first spike on SHH 113 Table 3.10: Fitted variances around second spike on SHH 114 Table 3.11: Parameter Estimates for SV model fitted on SHH 114 Table 3.12: Fitted variances of SV around first spike on SHH 115 Table 3.13: Fitted variances of SV around second spike on SHH 115 Table 3.14: Parameter Estimates for Asym. SV fitted on SHH 116 Table 3.15: Fitted variances of Asym. SV around first spike on SHH 117 Table 3.16: Fitted variances of Asym. SV around second spike on SHH 117 viii List of Figures Figure 2.1: Stock indices of China, USA and Hong Kong 58 Figure 2.2: SACF of cointegration residual series between China and US 69 Figure 2.3: SACF of cointegration residual series between China and HK 69 Figure 2.4: Conditional Standard Deviations for ΔSHH t and ΔSPt 73 Figure 2.5: Conditional correlation between series ΔSHH t and ΔSPt 74 Figure 2.6: Conditional Standard Deviations for ΔSHH t and 78 ΔHS t Figure 2.7: Conditional correlation between series ΔSHH t and Figure 2.8: Impulse Response Function of ( ΔSPt , ΔHSt , ΔSHH t )′ ΔHS t 79 80 Figure 3.1: Implied ACF of fitted models on S&P 112 Figure 3.2: Implied ACF of fitted models on SHH 118 ix Dhatt, MS., Y.H. Kim and U. Lim(1993), “The short-run and long-run performance of Korean IPOs: 1980-90”, unpublished working paper (University of Cincinnati and Yonsei University). Dickey, D.A. and W.A. 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Wu, and (2000), “Shareholding structure and Corporate Performance of Partially Privatized Firms: Evidence from Listed Chinese Companies”, Pacific-Basin finance Journal, 587-610, 2000 136 Appendix A: Simulated densities of estimated coefficients Model 1: Half year return regression beta0 chains 1:2 sample: 10000 beta1 chains 1:2 sample: 10000 1.5 15.0 1.0 10.0 0.5 5.0 0.0 0.0 -3.0 -2.0 -1.0 0.0 0.1 beta2 chains 1:2 sample: 10000 30.0 0.2 0.3 0.4 beta3 chains 1:2 sample: 10000 20.0 15.0 10.0 5.0 0.0 20.0 10.0 0.0 -0.05 0.0 0.05 0.1 0.15 -0.35 beta4 chains 1:2 sample: 10000 1.5 10.0 1.0 5.0 0.5 0.0 0.0 0.2 0.3 -1.0 beta6 chains 1:2 sample: 10000 150.0 -0.25 -0.2 -0.15 beta5 chains 1:2 sample: 10000 15.0 0.1 -0.3 0.0 1.0 beta7 chains 1:2 sample: 10000 1.0 0.75 0.5 0.25 0.0 100.0 50.0 0.0 -0.01 0.0 0.01 -3.0 -2.0 -1.0 0.0 1.0 sigma chains 1:2 sample: 10000 8.0 6.0 4.0 2.0 0.0 1.8 2.0 2.2 137 Model 2: One year return regression beta0 chains 1:2 sample: 10000 2.0 1.5 1.0 0.5 0.0 beta1 chains 1:2 sample: 10000 15.0 10.0 5.0 0.0 -2.0 -1.5 -1.0 -0.5 0.0 0.1 beta2 chains 1:2 sample: 10000 30.0 0.2 0.3 beta3 chains 1:2 sample: 10000 20.0 15.0 10.0 5.0 0.0 20.0 10.0 0.0 -0.05 0.0 0.05 0.1 -0.3 beta4 chains 1:2 sample: 10000 20.0 15.0 10.0 5.0 0.0 -0.25 -0.2 -0.15 beta5 chains 1:2 sample: 10000 1.5 1.0 0.5 0.0 0.1 0.15 0.2 0.25 -2.0 beta6 chains 1:2 sample: 10000 1.5 100.0 1.0 50.0 0.5 0.0 0.0 0.0 0.01 0.0 1.0 beta7 chains 1:2 sample: 10000 150.0 -0.01 -1.0 -2.0 -1.0 0.0 1.0 sigma chains 1:2 sample: 10000 8.0 6.0 4.0 2.0 0.0 1.8 2.0 2.2 138 Appendix B: Plots of S & P Monthly Returns and its Squared Series -20 -15 -10 -5 10 15 S&P 500 Monthly Return in Percentage 1965 1970 1975 1980 1985 1990 1995 2000 100 200 300 400 500 600 Squared Series of S&P 500 Monthly Return in Percentage 1965 1970 1975 1980 1985 1990 1995 2000 139 Appendix C: Plots of fitted volatilities from GARCH(1,1) and SVs on S&P 10 20 30 40 50 60 70 GARCH(1,1)conditional variance 1965 1970 1975 1980 1985 1990 1995 2000 1985 1990 1995 2000 1990 1995 2000 10 20 30 40 50 60 70 80 90 Fitted Volatility of SV(1) on S&P 1965 1970 1975 1980 20 40 60 80 100 120 140 160 180 200 Fitted Volatility of Asymm. SV(1) on S&P 1965 1970 1975 1980 1985 140 Appendix D: Plots of SHH Monthly Returns and its Squared Series -40 -20 20 40 60 80 Monthly Return series of Shanghai All Shares Index 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2001 2002 2003 2004 2005 2006 1000 2000 3000 4000 5000 6000 7000 8000 9000 Squared Monthly Return series of Shanghai All Shares Index 1992 1993 1994 1995 1996 1997 1998 1999 2000 141 Appendix E: Plots of fitted volatilities from GARCH(1,1) and SVs on SHH 50 100 150 200 250 300 350 400 450 500 GARCH(1,1) Fitted Volatility on Monthly Return Series of China's Market 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2001 2002 2003 2004 2005 2006 2001 2002 2003 2004 2005 2006 200 400 600 800 1000 1200 1400 Fitted Volatility of SV Model on Return Series of China's Market 1992 1993 1994 1995 1996 1997 1998 1999 2000 1000 2000 3000 4000 5000 Fitted Volatility of Asymm.SV Model on Return Series of China's Market 1992 1993 1994 1995 1996 1997 1998 1999 2000 142 [...]... price and reap profits Price manipulation activities are so widespread and inrooted that securities officials and the management of listed companies are also frequently involved in manipulation scandals Several securities companies and investment banks have collapsed due to manipulating stock prices and other wrongdoings 16 Additionally, the most striking feature of China’s stock market probably is that... which are similar to shares retained by original owners of listed company in western markets, not negotiable and tradable, yet enjoy same rights as tradable shares Whereas, right now, China’s government is converting the non-tradable state shares alike to tradable shares, in order to scale-up privatization and render China’s stock market closer to standard mature markets 13 Some Chinese companies also... literature be applied to China’s market? In order to answer these questions, we have to first understand China’s special market mechanisms Stock market was introduced to China about fifteen years ago to help reforming centralized economy Due to the transitional nature of economy, China’s stock market has some peculiar features which are alien to mature and other emerging markets as well Although stock market. .. close relations between IPO initial and aftermarket returns and abovementioned market imperfects Our analyses verify our hypotheses that large part of the variability of IPO initial and aftermarket returns on China’s market is driven by the deficient market structure In other words, the high IPO underpricing on China’s market is created artificially rather than originates from fundamental reasons like... Because China’s authority is trying to enlarge the privatization scale by gradually converting non-tradable shares (state and legal entity shares) to tradable shares (A- shares), high market liquidity is expected by government Therefore, this chapter tests the winner’s curse hypothesis on China’s market by including the ratio of tradable A- shares to the total shares issued as one of the explanatory variables... inferences about parameters are based on the posterior density Inter alia, Bayesian method addresses (in principle) one logical difficulty associated with classical approach (or frequentist approach) which assumes that repeated sampling is possible However, in real application, usually we have only one realization of a random process, repeated sampling is rarely available if not completely impossible Bayesian. .. As IPOs are usually underpriced, obtaining IPO allocation is the cheapest and easiest way to manipulate stock price and also the huge profits can be reaped from the manipulation, block holders are highly motivated to manipulate the IPO stock s price once the IPO gets listed Maneuvering aftermarket prices is not legally allowed on China’s stock market, but it is immanent in China’s stock market because... combination or in competition with each other Our second concern is that although there are arising attentions on China largely because of its ceaselessly growing economy, academic researches on China’s stock market mount up moderately The possible reasons include the difficulty to acquire data in desirable format as economic and financial data on China’s market remain primitive and fragmented, and also... with small capitalization are easier to control and also incur less risks, manipulators could prefer IPO of small size In the similar spirit, Beatty and Ritter (1986) also show that small sized stocks are more speculative than stocks with large capitalization While other researches examine the discrepancy of risks incurred by small and large IPO and stocks 18 , this chapter test it in the context of price... Typically, when these state-owned enterprises (hereinafter SOEs 11 ) issue IPOs, governments still retain control of the companies by holding a large fraction of shares in the forms of state shares and legal entity shares 12 , only a small proportion of shares are offered to public investors and traded in exchanges, called A- shares IPO in China’s market means issuing A- shares13 On China’s market, tradable . initial and aftermarket returns and abovementioned market imperfects. Our analyses verify our hypotheses that large part of the variability of IPO initial and aftermarket returns on China’s market. between China’s and US stock markets, and between China’s and Hong Kong stock markets. Research on cointegrations among stock markets has important practical implications for portfolio management. A STUDY OF CHINESE STOCK MARKET: EMPIRICAL AND THEORETICAL EXPLORATIONS BY BAYESIAN AND GARCH MODELS CHEN HENG A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY