Risk-Sensitive Optimal Execution via a Conditional Value-at-Risk Objective

TL;DR

This paper develops a risk-sensitive optimal liquidation strategy using CVaR in a continuous-time setting, deriving closed-form solutions and demonstrating the advantages of adaptive policies over static ones in terms of risk management.

Contribution

It introduces a novel CVaR-based formulation for optimal execution, providing closed-form solutions and insights into the benefits of adaptive trading strategies under risk aversion.

Findings

Adaptive policies outperform static ones by 5-15% for moderate risk aversion.

Dynamic strategies accelerate trading when favorable and slow down when unfavorable.

Closed-form solutions are derived using a PDE approach and dual CVaR representation.

Abstract

We consider a liquidation problem in which a risk-averse trader tries to liquidate a fixed quantity of an asset in the presence of market impact and random price fluctuations. The trader encounters a trade-off between the transaction costs incurred due to market impact and the volatility risk of holding the position. Our formulation begins with a continuous-time and infinite horizon variation of the seminal model of Almgren and Chriss (2000), but we define as the objective the conditional value-at-risk (CVaR) of the implementation shortfall, and allow for dynamic (adaptive) trading strategies. In this setting, we are able to derive closed-form expressions for the optimal liquidation strategy and its value function. Our results yield a number of important practical insights. We are able to quantify the benefit of adaptive policies over optimized static policies. The relevant improvement depends only on the level of risk aversion: for moderate levels of risk aversion, the optimal dynamic policy outperforms the optimal static policy by 5-15%, and outperforms the optimal volume weighted average price (VWAP) policy by 15-25%. This improvement is achieved through dynamic policies that exhibit "aggressiveness-in-the-money": trading is accelerated when price movements are favorable, and is slowed when price movements are unfavorable. From a mathematical perspective, our analysis exploits the dual representation of CVaR to convert the problem to a continuous-time, zero-sum game. We leverage the idea of the state-space augmentation, and obtain a partial differential equation describing the optimal value function, which is separable and a special instance of the Emden-Fowler equation. This leads to a closed-form solution. As our problem is a special case of a linear-quadratic-Gaussian control problem with a CVaR objective, these results may be interesting in broader settings.