Empirical Mechanism Design for Optimizing Clearing Interval in Frequent Call Markets

Abstract

Several recent authors have advocated for financial markets to move from continuous clearing to discrete or batched clearing, as a way to defeat the latency arms race: the never-ending quest for small advantages in time to access markets. How frequently should such a modern batch auction clear? We conduct a systematic simulation-based investigation on the relationship between clearing frequency and metrics of market quality, such as allocative efficiency, comparing the performance of discrete and continuous auction mechanisms under empirical equilibrium behavior of all participating traders. In effect we perform empirical mechanism design on frequent batch auctions. We find that in a wide array of environments, equilibrium efficiency is improved for small positive intervals but falls off dramatically when there are too few opportunities to trade. The result is a large range of batch frequencies that are near optimally efficient; this range is wider in thick markets.