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A Bayesian analysis of stock return volatility and trading volume

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  • Ronald Mahieu
  • Rob Bauer

Abstract

The relationship between stock return volatility and trading volume is analysed by using the modified mixture model (MMM) framework proposed by Andersen (1996). This theory postulates that price changes and volumes are driven by a common latent information process, which is commonly interpreted as the volatility. Using GMM estimation Andersen finds that the persistence in this latent process falls when a bivariate model of returns and volume, i.e. the MMM, is estimated instead of a univariate model for returns. This empirical finding is inconsistent with the MMM. As opposed to Andersen's study we apply recently developed simulation techniques based on Markov Chain Monte Carlo (MCMC). A clear advantage of MCMC methods is that estimates of volatility are readily available for use in, for example, dynamic portfolio allocation and option pricing applications. Using Andersen's data for IBM we find that the persistence of volatility remains high in the bivariate case. This suggests that the choice of the estimation technique could be important in testing the validity of the MMM.

Suggested Citation

  • Ronald Mahieu & Rob Bauer, 1998. "A Bayesian analysis of stock return volatility and trading volume," Applied Financial Economics, Taylor & Francis Journals, vol. 8(6), pages 671-687.
    • Handle: RePEc:taf:apfiec:v:8:y:1998:i:6:p:671-687
    DOI: 10.1080/096031098332718
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    2. Pyun, Chong Soo & Lee, Sa Young & Nam, Kiseok, 2000. "Volatility and information flows in emerging equity market: A case of the Korean Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 9(4), pages 405-420.
    3. Carmen Broto & Esther Ruiz, 2004. "Estimation methods for stochastic volatility models: a survey," Journal of Economic Surveys, Wiley Blackwell, vol. 18(5), pages 613-649, December.
    4. 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.
    5. 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.
    6. Du, Xiaodong & Dong, Fengxia, 2014. "Heterogeneous Responses to Market Information and The Impact on Price Volatility and Trading Volume: The Case of Class III Milk Futures," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169769, Agricultural and Applied Economics Association.
    7. Shimokawa, Tetsuya & Suzuki, Kyoko & Misawa, Tadanobu, 2007. "An agent-based approach to financial stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 207-225.

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