2002/10/22 by Eric Smith, J. Doyne Farmer, László Gillemot +2 · 5 citations
Decision Sciences · Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Auction Theory and Applications #Bid price #Common value auction #Complex Systems and Time Series Analysis #Computer science #Double auction #Econometrics #Economics #Financial Markets and Investment Strategies #Granularity #Limit (mathematics) #Mathematical economics #Mathematics #Microeconomics #Order (exchange) #Order book #Tick size #Volatility (finance) #cond-mat.stat-mech #q-fin.TR
paper · pdf · doi:10.1088/1469-7688/3/6/307
36 pages, 40 figures, RevTex4, submitted to Quantitative Finance
arxiv created 2002/10/22 · openalex publication_date 2003/12/01 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Most modern financial markets use a continuous double auction mechanism to store and match orders and facilitate trading. In this paper we develop a microscopic dynamical statistical model for the continuous double auction under the assumption of IID random order flow, and analyse it using simulation, dimensional analysis, and theoretical tools based on mean field approximations. The model makes testable predictions for basic properties of markets, such as price volatility, the depth of stored supply and demand versus price, the bid–ask spread, the price impact function, and the time and probability of filling orders. These predictions are based on properties of order flow and the limit order book, such as share volume of market and limit orders, cancellations, typical order size, and tick size. Because these quantities can all be measured directly there are no free parameters. We show that the order size, which can be cast as a non-dimensional granularity parameter, is in most cases a more significant determinant of market behaviour than tick size. We also provide an explanation for the observed highly concave nature of the price impact function. On a broader level, this work suggests how stochastic models based on zero intelligence agents may be useful to probe the structure of market institutions. Like the model of perfect rationality, a stochastic zero intelligence model can be used to make strong predictions based on a compact set of assumptions, even if these assumptions are not fully believable.