Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model

We propose new methods for estimating the bid-ask spread from observed transaction prices alone. Our methods are based on the empirical characteristic function instead of the sample autocovariance function like the method of Roll (1984). As in Roll (1984), we have a closed form expression for the spread, but this is only based on a limited amount of the model-implied identification restrictions. We also provide methods that take account of more identification information. We compare our methods theoretically and numerically with the Roll method as well as with its best known competitor, the Hasbrouck (2004) method, which uses a Bayesian Gibbs methodology under a Gaussian assumption. Our estimators are competitive with Roll’s and Hasbrouck’s when the latent true fundamental return distribution is Gaussian, and perform much better when this distribution is far from Gaussian. Our methods are applied to the Emini futures contract on the S&P 500 during the Flash Crash of May 6, 2010. Extensions to models allowing for unbalanced order flow or Hidden Markov trade direction indicators or trade direction indicators having general asymmetric support or adverse selection are also presented, without requiring additional data.