Optimal Trade Execution with Uncertain Volume Target

TL;DR

This paper introduces a model for optimal trade execution that accounts for uncertain volume targets, showing that delaying trades can reduce risk and improve performance compared to traditional fixed-volume strategies.

Contribution

It develops a novel model incorporating volume uncertainty into optimal trading strategies, balancing early and late trades for risk mitigation.

Findings

Risk-averse traders benefit from delaying trades under volume uncertainty.

The proposed model avoids high complexity of dynamic programming solutions.

Strategies outperform static approaches in uncertain volume scenarios.

Abstract

In the seminal paper on optimal execution of portfolio transactions, Almgren and Chriss (2001) define the optimal trading strategy to liquidate a fixed volume of a single security under price uncertainty. Yet there exist situations, such as in the power market, in which the volume to be traded can only be estimated and becomes more accurate when approaching a specified delivery time. During the course of execution, a trader should then constantly adapt their trading strategy to meet their fluctuating volume target. In this paper, we develop a model that accounts for volume uncertainty and we show that a risk-averse trader has benefit in delaying their trades. More precisely, we argue that the optimal strategy is a trade-off between early and late trades in order to balance risk associated with both price and volume. By incorporating a risk term related to the volume to trade, the static optimal strategies suggested by our model avoid the explosion in the algorithmic complexity usually associated with dynamic programming solutions, all the while yielding competitive performance.