PERFORMANCE OF STOLL's SPREAD COMPONENT ESTIMATOR: EVIDENCE FROM SIMULATIONS, TIME‐SERIES, AND CROSS‐SECTIONAL DATA

Abstract

AbstractStoll (1989) introduces an intuitive procedure to estimate the basic components of the bid‐ask spread (order‐processing cost, inventory cost, and adverse‐selection cost). He also provides reasonable estimates of the magnitudes of the order‐processing, inventory, and adverse‐selection costs of making markets for a large cross‐section of NASDAQ/NMS stocks. Empirical applications of Stoll's model produce widely different estimates of the bid‐ask spread components. We derive the sampling properties of Stoll's estimator of the realized bid‐ask spread, i.e., the sum of the order‐processing and inventory components. We test Stoll's model in simulations, using the ideal conditions implied by the model. We conclude that noise in serial covariance estimates causes estimates of the realized spread to be severely biased and highly unreliable in short time‐series and small cross‐sectional samples.