Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility



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      Kellogg Graduate School of Management, Northwestern University. The article develops ideas originally put forth in my Ph.D. dissertation at Yale University. I am grateful to the members of my dissertation committee, Peter Phillips, Steve Ross, and Steve Heston for advice. In addition, conversations with Don Andrews, Tim Bollerslev, Mike Fishman, Bob Hodrick, Neil Shephard, Ken Singleton, Bent Sørensen, George Tauchen, and Stephen Taylor have been helpful. I also received valuable comments from seminar participants at the NBER Summer Institute, 1993, Northwestern University, the University of Illinois at Chicago, the EFA Meetings in Copenhagen, August 1993, University of Wisconsin at Madison, Ohio State University, the AFA Meetings in Boston, January 1994, and the Microstructure Workshop at The Aarhus School of Business, September 1994. Finally, I thank two anonymous referees and the editor, René Stulz, for numerous suggestions that have sharpened the focus of the article. Naturally, all errors remain my own responsibility.


The paper develops an empirical return volatility-trading volume model from a microstructure framework in which informational asymmetries and liquidity needs motivate trade in response to information arrivals. The resulting system modifies the so-called “Mixture of Distribution Hypothesis” (MDH). The dynamic features are governed by the information flow, modeled as a stochastic volatility process, and generalize standard ARCH specifications. Specification tests support the modified MDH representation and show that it vastly outperforms the standard MDH. The findings suggest that the model may be useful for analysis of the economic factors behind the observed volatility clustering in returns.