Asymmetric responses of international stock markets to trading volume

The major goal of this paper is to examine the hypothesis that stock returns and return volatility are asymmetric, threshold nonlinear, functions of change in trading volume. A minor goal is to examine whether return spillover effects also display such asymmetry. Employing a double-threshold GARCH model with trading volume as a threshold variable, we find strong evidence supporting this hypothesis in five international market return series. Asymmetric causality tests lend further support to our trading volume threshold model and conclusions. Specifically, an increase in volume is positively associated, while decreasing volume is negatively associated, with the major price index in four of the five markets. The volatility of each series also displays an asymmetric reaction, four of the markets display higher volatility following increases in trading volume. Using posterior odds ratio, the proposed threshold model is strongly favored in three of the five markets, compared to a US news double threshold GARCH model and a symmetric GARCH model. We also find significant nonlinear asymmetric return spillover effects from the US market.

[1]  Bong‐Soo Lee,et al.  The dynamic relationship between stock returns and trading volume: Domestic and cross-country evidence , 2002 .

[2]  Cheol S. Eun,et al.  International Transmission of Stock Market Movements , 1989, Journal of Financial and Quantitative Analysis.

[3]  E. Fama,et al.  The Cross‐Section of Expected Stock Returns , 1992 .

[4]  Cathy W. S. Chen,et al.  On a threshold heteroscedastic model , 2006 .

[5]  T. Andersen Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility , 1996 .

[6]  T. W. Epps,et al.  The Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture-of-Distributions Hypothesis , 1976 .

[7]  Peter E. Rossi,et al.  Stock Prices and Volume , 1992 .

[8]  S. Ross Information and Volatility: The No-Arbitrage Martingale Approach to Timing and Resolution Irrelevancy , 1989 .

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

[10]  P. Clark A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices , 1973 .

[11]  T. Chiang,et al.  Dynamic analysis of stock return volatility in an integrated international capital market , 1996 .

[12]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[13]  S. Chib,et al.  Understanding the Metropolis-Hastings Algorithm , 1995 .

[14]  K. Nam,et al.  Asymmetric reverting behavior of short-horizon stock returns: An evidence of stock market overreaction , 2001 .

[15]  Kent D. Daniel,et al.  Presentation Slides for 'Investor Psychology and Security Market Under and Overreactions' , 1998 .

[16]  Ronald W. Masulis,et al.  Correlations in Price Changes and Volatility Across International Stock Markets , 1990 .

[17]  R. Kohn,et al.  Diagnostics for Time Series Analysis , 1999 .

[18]  Martin T. Bohl,et al.  Trading volume and stock market volatility: The Polish case , 2003 .

[19]  Cathy W. S. Chen,et al.  Asymmetrical reaction to US stock-return news: evidence from major stock markets based on a double-threshold model , 2003 .

[20]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[21]  A Bayesian analysis of generalized threshold autoregressive models , 1998 .

[22]  R. Gerlach,et al.  MCMC methods for comparing stochastic volatility and GARCH models , 2006 .

[23]  Chung-Hua Shen,et al.  Daily serial correlation, trading volume and price limits: Evidence from the Taiwan stock market , 1998 .

[24]  G. Gallo,et al.  The effects of trading activity on market volatility , 2000 .

[25]  E. McKenzie,et al.  Heteroscedasticity in stock returns data revisited: volume versus GARCH effects , 2000 .

[26]  Wai Keung Li,et al.  On a Double-Threshold Autoregressive Heteroscedastic Time Series Model , 1996 .

[27]  M. Firth,et al.  Do bears and bulls swim across oceans? Market information transmission between greater China and the rest of the world , 2004 .

[28]  A. Christie,et al.  The stochastic behavior of common stock variances: value , 1982 .

[29]  Hung Man Tong,et al.  Threshold models in non-linear time series analysis. Lecture notes in statistics, No.21 , 1983 .

[30]  Ser-Huang Poon,et al.  Returns synchronization and daily correlation dynamics between international stock markets , 1999 .

[31]  Panayiotis Theodossiou,et al.  RELATIONSHIP BETWEEN VOLATILITY AND EXPECTED RETURNS ACROSS INTERNATIONAL STOCK MARKETS , 1995 .

[32]  Charles C. Ying STOCK MARKET PRICES AND VOLUMES OF SALES , 1966 .

[33]  R. Westerfield,et al.  The Week-End Effect in Common Stock Returns: The International Evidence , 1985 .

[34]  An empirical investigation of trading volume and return volatility of the Taiwan Stock Market , 2001 .

[35]  Christopher G. Lamoureux,et al.  Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects , 1990 .

[36]  Tarun Chordia,et al.  Trading Volume and Cross‐Autocorrelations in Stock Returns , 2000 .

[37]  Dongcheol Kim,et al.  Alternative Models for the Conditional Heteroscedasticity of Stock Returns , 1994 .

[38]  Testing for Temporal Asymmetry in the Price-Volume Relationship , 2003 .

[39]  N. Shephard,et al.  Stochastic Volatility: Likelihood Inference And Comparison With Arch Models , 1996 .

[40]  Chris Brooks,et al.  A Double-Threshold GARCH Model for the French Franc/Deutschmark Exchange Rate , 2001 .

[41]  P. Green Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[42]  Gregory Koutmos,et al.  Asymmetric Price and Volatility Adjustments in Emerging Asian Stock Markets , 1999 .

[43]  J. Berger Statistical Decision Theory and Bayesian Analysis , 1988 .

[44]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.