Market microstructure.

• Position: National Bureau of Economic Research's Project on Market Microstructure

Members of the Market Microstructure Project gathered in Cambridge on May 15 to discuss their recent research. Bruce Lehmann, of the University of California, San Diego, organized the meeting. The following papers were presented:

Joel Hasbrouck, New York University, "Security Bid/Ask Dynamics with Discreteness and Clustering: Simple Strategies for Modeling and Estimation"

Discussant: Michael Brandt, University of Pennsylvania

Aditya Kaul, University of Alberta, "Market Activity Before Volatile Periods: A Reassessment of the Non-Trading Risk Hypothesis" Discussant: Charles Cao, Pennsylvania State University Ananth Madhavan and Venkatesh Panchapagesan, University of Southern California, "Price Discovery in Auction Markets: A Look Inside the Black Box"

Discussant: Frank Hatheway, Pennsylvania State University S. Viswanathan and James wang, Duke University, "Market Architecture: Limit-Order Books versus Dealership Markets"

Discussant: Ananth Madhavan

George Sofianos, New York Stock Exchange, and Ingrid M. Werner, NBER and New York Stock Exchange, "The Trades of NYSE Floor Brokers"

Discussant: Duane Seppi, Carnegie-Mellon University

Hasbrouck shows that the short-term movements of a security price reflect the latent efficient price (or, the conditional expectation of terminal value) and various components arising from the trading mechanism itself. Observed bid and ask quotes are only rough signals of these unobserved quantities. The bid and ask quotes in the dollar/DM market, for example, are discrete, with a tick size that is not trivial relative to the spread. Furthermore, the distribution of these quotes is clustered, with a greater-than-expected incidence of five-tick multiples. He implements a Gibbs sampler approach that proves to be quick and effective; this strategy opens the door for the investigation of a broad class of structural microstructure models.

Kaul examines the extent to which non-trading risk explains high pre-close trading volume and bid-ask spreads. He relies on a model in which distant volatility has a smaller effect on trading activity than does near-term volatility. The model appears to perform well in explaining volume and spreads, with the parameters being of the predicted sign and generally significant. However, the incremental effects of non-trading volatility are not consistently positive or significant for market volume or for spreads and volume at the stock level. A second test finds that pre-close volume and...