Journal Article Introductory Comments: Bloomfield and O'Hara, and Flood, Huisman, Koedijk, and Mahieu Get access Lawrence R. Glosten Lawrence R. Glosten Columbia University Address correspondence to Lawrence R. Glosten, Columbia Business School, Uris Hall, Room 614, Columbia University, 3022 Broadway, New York, NY 10027, or e-mail: [email protected]. Search for other works by this author on: Oxford Academic Google Scholar The Review of Financial Studies, Volume 12, Issue 1, January 1999, Pages 1–3, https://doi.org/10.1093/rfs/12.1.1 Published: 01 June 2015

Under fairly general conditions, this article derives the equilibrium price schedule determined by the bids and offers in an open limit order book. The analysis shows that the order book has a small-trade positive bid-ask spread, and limit orders profit from small trades; the electronic exchange provides as much liquidity as possible in extreme situations; the limit order book does not invite competition from third market dealers, while other trading institutions do; and, if an entering exchange earns nonnegative trading profits, the consolidated price schedule matches the limit order book price schedule.

Under fairly general conditions, the article derives the equilibrium price schedule determined by the bids and offers in an open limit order book. The analysis shows: (1) the order book has a small‐trade positive bid‐ask spread, and limit orders profit from small trades; (2) the electronic exchange provides as much liquidity as possible in extreme situations; (3) the limit order book does not invite competition from third market dealers, while other trading institutions do; (4) If an entering exchange earns nonnegative trading profits, the consolidated price schedule matches the limit order book price schedule.

The bid‐ask spread can be decomposed into two parts: one part due to asymmetric information and the other part due to other factors such as monopoly power. The part due to asymmetric information attenuates statistical biases in mean return, variance, and serial covariance. Thus, using spread data to adjust for biases in return moments requires knowing not only the spread but the composition of the spread. Furthermore, any spread‐estimation procedure using transaction prices must estimate two spread components. On the other hand, the appropriateness of some previously suggested statistical corrections is independent of the spread composition.

This paper develops and implements a technique for estimating a model of the bid/ask spread. The spread is decomposed into two components, one due to asymmetric information and one due to inventory costs, specialist monopoly power, and clearing costs. The model is estimated using NYSE common stock transaction prices in the period 1981–1983. Cross-sectional regression analysis is then used to relate time-series estimated spread components to other stock characteristics. The results cannot reject the hypothesis that significant amounts of NYSE common stock spreads are due to asymmetric information.

Knowledge of the one‐month interest rate is useful in forecasting the sign as well as the variance of the excess return on stocks. The services of a portfolio manager who makes use of the forecasting model to shift funds between bills and stocks would be worth an annual management fee of 2% of the value of the assets managed. During 1954:4 to 1986:12, the variance of monthly returns on the managed portfolio was about 60% of the variance of the returns on the value weighted index, whereas the average return was two basis points higher.

We find support for a negative relation between conditional expected monthly return and conditional variance of monthly return, using a GARCH-M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH-M model, we also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility whereas negative unanticipated returns result in an upward revision of conditional volatility.