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Bayesian Analysis of Dynamic Bivariate Mixture Models: Can They Explain the Behavior of Returns and Trading Volume?

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  • Watanabe, Toshiaki

Abstract

Bivariate mixture models attribute the well-known positive correlation between return volatility and trading volume in financial markets to stochastic changes in a single latent variable representing the number of information arrivals. In this article, dynamic bivariate mixture models that allow for autocorrelation in the latent variable are analyzed by a Bayesian method via Markov-chain Monte Carlo techniques. The results, based on daily data from the Nikkei 225 stock-index futures, reveal that the Tauchen and Pitts model, in which returns and volume follow a bivariate normal distribution conditional on the latent variable, cannot account for the persistence in squared returns. whereas the Andersen model, in which the conditional distribution of volume is Poisson, cannot account for the persistence in volume. It is also found that the Tauchen and Pitts model yields too narrow Bayesian confidence intervals of the out-of-sample squared returns.

Suggested Citation

  • Watanabe, Toshiaki, 2000. "Bayesian Analysis of Dynamic Bivariate Mixture Models: Can They Explain the Behavior of Returns and Trading Volume?," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 199-210, April.
    • Handle: RePEc:bes:jnlbes:v:18:y:2000:i:2:p:199-210
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    Citations

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    Cited by:

    1. Antonio A. F. Santos, 2021. "Bayesian Estimation for High-Frequency Volatility Models in a Time Deformed Framework," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 455-479, February.
    2. Junji Shimada & Yoshihiko Tsukuda, 2004. "Estimation of Stochastic Volatility Models : An Approximation to the Nonlinear State Space," Econometric Society 2004 Far Eastern Meetings 611, Econometric Society.
    3. Ané, Thierry & Ureche-Rangau, Loredana, 2008. "Does trading volume really explain stock returns volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 216-235, July.
    4. Ai-ru (Meg) Cheng & Yin-Wong Cheung, 2008. "Return, Trading Volume, and Market Depth in Currency Futures Markets," Working Papers 202008, Hong Kong Institute for Monetary Research.
    5. Jeff Fleming & Chris Kirby & Barbara Ostdiek, 2006. "Stochastic Volatility, Trading Volume, and the Daily Flow of Information," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1551-1590, May.
    6. Banerjee, A. & Malik, S., 2012. "The changing role of expectations in US monetary policy: A new look using the Livingston Survey," Working papers 376, Banque de France.
    7. Carlos A. Abanto‐Valle & Helio S. Migon & Hedibert F. Lopes, 2010. "Bayesian modeling of financial returns: A relationship between volatility and trading volume," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(2), pages 172-193, March.
    8. Trojan, Sebastian, 2013. "Regime Switching Stochastic Volatility with Skew, Fat Tails and Leverage using Returns and Realized Volatility Contemporaneously," Economics Working Paper Series 1341, University of St. Gallen, School of Economics and Political Science, revised Aug 2014.
    9. Park, Beum-Jo, 2010. "Surprising information, the MDH, and the relationship between volatility and trading volume," Journal of Financial Markets, Elsevier, vol. 13(3), pages 344-366, August.
    10. Beum‐Jo Park, 2007. "Trading Volume, Volatility, And Garch Effects In The South Korean Won/Us Dollar Exchange Market: Evidence From Conditional Quantile Estimation," The Japanese Economic Review, Japanese Economic Association, vol. 58(3), pages 382-399, September.

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