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A Model of Market Limit Orders By Stochastic PDE's, Parameter Estimation, and Investment Optimization

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  • Zhi Zheng
  • Richard B. Sowers

Abstract

In this paper we introduce a completely continuous and time-variate model of the evolution of market limit orders based on the existence, uniqueness, and regularity of the solutions to a type of stochastic partial differential equations obtained in Zheng and Sowers (2012). In contrary to several models proposed and researched in literature, this model provides complete continuity in both time and price inherited from the stochastic PDE, and thus is particularly suitable for the cases where transactions happen in an extremely fast pace, such as those delivered by high frequency traders (HFT's). We first elaborate the precise definition of the model with its associated parameters, and show its existence and uniqueness from the related mathematical results given a fixed set of parameters. Then we statistically derive parameter estimation schemes of the model using maximum likelihood and least mean-square-errors estimation methods under certain criteria such as AIC to accommodate to variant number of parameters . Finally as a typical economics and finance use case of the model we settle the investment optimization problem in both static and dynamic sense by analysing the stochastic (It\^{o}) evolution of the utility function of an investor or trader who takes the model and its parameters as exogenous. Two theorems are proved which provide criteria for determining the best (limit) price and time point to make the transaction.

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  • Zhi Zheng & Richard B. Sowers, 2012. "A Model of Market Limit Orders By Stochastic PDE's, Parameter Estimation, and Investment Optimization," Papers 1210.7230, arXiv.org.
    • Handle: RePEc:arx:papers:1210.7230
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    References listed on IDEAS

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    1. Ioanid Rosu, 2009. "A Dynamic Model of the Limit Order Book," Post-Print hal-00515873, HAL.
    2. Ioanid Rosu, 2009. "A Dynamic Model of the Limit Order Book," The Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4601-4641, November.
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