Numerical methods for an optimal order execution problem

TL;DR

This paper develops a numerical scheme for solving an impulse control problem in optimal portfolio liquidation, incorporating market impact and bid-ask spread, with convergence proofs and practical performance analysis.

Contribution

It introduces an explicit backward numerical scheme for a quasi-variational inequality in optimal liquidation, utilizing lag variables and optimal quantization for expectations.

Findings

Numerical scheme converges via viscosity solutions.

Optimal strategies exhibit interesting patterns on real data.

Sensitivity analysis shows impact of market parameters.

Abstract

This paper deals with numerical solutions to an impulse control problem arising from optimal portfolio liquidation with bid-ask spread and market price impact penalizing speedy execution trades. The corresponding dynamic programming (DP) equation is a quasi-variational inequality (QVI) with solvency constraint satisfied by the value function in the sense of constrained viscosity solutions. By taking advantage of the lag variable tracking the time interval between trades, we can provide an explicit backward numerical scheme for the time discretization of the DPQVI. The convergence of this discrete-time scheme is shown by viscosity solutions arguments. An optimal quantization method is used for computing the (conditional) expectations arising in this scheme. Numerical results are presented by examining the behaviour of optimal liquidation strategies, and comparative performance analysis with respect to some benchmark execution strategies. We also illustrate our optimal liquidation algorithm on real data, and observe various interesting patterns of order execution strategies. Finally, we provide some numerical tests of sensitivity with respect to the bid/ask spread and market impact parameters.