Optimal Liquidation under Partial Information with Price Impact

TL;DR

This paper develops a mathematical framework for optimal liquidation in markets with price impact and partial information, using stochastic filtering and control of PDMPs, supported by numerical analysis.

Contribution

It introduces a novel approach combining stochastic filtering with PDMP control for optimal liquidation under partial information and price impact.

Findings

Partial information significantly affects optimal liquidation strategies.

Price impact alters the value function and liquidation rates.

Numerical results demonstrate the model's practical implications.

Abstract

We study the optimal liquidation problem in a market model where the bid price follows a geometric pure jump process whose local characteristics are driven by an unobservable finite-state Markov chain and by the liquidation rate. This model is consistent with stylized facts of high frequency data such as the discrete nature of tick data and the clustering in the order flow. We include both temporary and permanent effects into our analysis. We use stochastic filtering to reduce the optimal liquidation problem to an equivalent optimization problem under complete information. This leads to a stochastic control problem for piecewise deterministic Markov processes (PDMPs). We carry out a detailed mathematical analysis of this problem. In particular, we derive the optimality equation for the value function, we characterize the value function as continuous viscosity solution of the associated dynamic programming equation, and we prove a novel comparison result. The paper concludes with numerical results illustrating the impact of partial information and price impact on the value function and on the optimal liquidation rate.