Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression

We study the nonlinear autoregressive dynamics of stock index returns in seven major advanced economies (G7) and China. The quantile autoregression model (QAR) enables us to investigate the autocorrelation across the whole spectrum of return distribution, which provides more insightful conditional information on multinational stock market dynamics than conventional time series models. The relation between index return and contemporaneous trading volume is also investigated. While prior studies have mixed results on stock market autocorrelations, we find that the dynamics is usually state dependent. The results for G7 stock markets exhibit conspicuous similarities, but they are in manifest contrast to the findings on Chinese stock markets.

[1]  R. Crouch.  The Volume of Transactions And Price Changes on The New York Stock Exchange , 1970 .

[2]  Oliver M. Rui,et al.  Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets , 2000 .

[3]  M. Osborne.  Brownian Motion in the Stock Market , 1959 .

[4]  Investors' reactions to sharp price changes: Evidence from equity markets of the People's Republic of China , 2011 .

[5]  R. Koenker,et al.  Unit Root Quantile Autoregression Inference , 2004 .

[6]  R. Faff,et al.  The Determinants of Conditional Autocorrelation in Stock Returns , 2002 .

[7]  Steven Thorley,et al.  Delayed Reaction to Good News and the Cross‐Autocorrelation of Portfolio Returns , 1996 .

[8]  Chuangxia Huang,et al.  Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model , 2013 .

[9]  Dazhi Zheng,et al.  Effectiveness, cause and impact of price limit—Evidence from China's cross-listed stocks , 2014 .

[10]  Maureen O'Hara,et al.  Market Statistics and Technical Analysis: The Role of Volume , 1994 .

[11]  Huiming Zhu,et al.  Revisiting the asymmetric dynamic dependence of stock returns: Evidence from a quantile autoregression model , 2015 .

[12]  Tau-Hing Lam,et al.  Is the Chinese Stock Market Really Efficient , 2012 .

[13]  Patrik Säfvenblad.  Trading volume and autocorrelation: Empirical evidence from the Stockholm Stock Exchange , 2000 .

[14]  Robert C. Jung,et al.  Stock Return Autocorrelations Revisited: A Quantile Regression Approach , 2011 .

[15]  Kenneth A. Kim,et al.  Price Limit Performance: Evidence from the Tokyo Stock Exchange , 1997 .

[16]  Chung-Ming Kuan,et al.  Causality in quantiles and dynamic stock return–volume relations , 2009 .

[17]  I. Morgan.  Stock Prices and Heteroscedasticity , 1976 .

[18]  M. Wohar,et al.  The determinants of quantile autocorrelations: Evidence from the UK , 2013 .

[19]  Shiu‐Sheng Chen,et al.  Revisiting the empirical linkages between stock returns and trading volume , 2012 .

[20]  R. Westerfield.  The Distribution of Common Stock Price Changes: An Application of Transactions Time and Subordinated Stochastic Models , 1977, Journal of Financial and Quantitative Analysis.

[21]  Jiang Wang,et al.  Trading Volume and Serial Correlation in Stock Returns , 1992 .

[22]  Juan Yao,et al.  Investor herding behaviour of Chinese stock market , 2014 .

[23]  J. Lewellen,et al.  Momentum and Autocorrelation in Stock Returns , 2002 .

[24]  M. Firth,et al.  The Dynamic Relation Between Stock Returns, Trading Volume, and Volatility , 2001 .

[25]  Hsiang-Hsi Liu,et al.  The bivariate GARCH approach to investigating the relation between stock returns, trading volume, and return volatility , 2012 .

[26]  A. Lo,et al.  An Econometric Analysis of Nonsynchronous Trading , 1989 .

[27]  Yan Luo,et al.  Short-Selling, Margin-Trading, and Price Efficiency: Evidence from the Chinese Market , 2013 .

[28]  Lin Tan,et al.  Herding behavior in Chinese stock markets: An examination of A and B shares , 2008 .

[29]  R. Brooks,et al.  Does volume help in predicting stock returns? An analysis of the Australian market , 2010 .

[30]  Jonathan M. Karpoff.  The Relation between Price Changes and Trading Volume: A Survey , 1987, Journal of Financial and Quantitative Analysis.

[31]  Jiang Wang,et al.  Dynamic Volume-Return Relation of Individual Stocks , 2000 .

[32]  M. Wohar,et al.  Causality between trading volume and returns: Evidence from quantile regressions , 2013 .

[33]  R. Koenker,et al.  Regression Quantiles , 2007 .

[34]  Dirk G. Baur,et al.  The Structure and Degree of Dependence - A Quantile Regression Approach , 2011 .

[35]  Kenneth A. Kim,et al.  RECONSIDERING PRICE LIMIT EFFECTIVENESS , 2013 .

[36]  Lin Tan,et al.  Empirical investigation of herding behavior in Chinese stock markets: Evidence from quantile regression analysis , 2010 .