Hedging without sweat: a genetic programming approach

TL;DR

This paper introduces a genetic programming approach to derive explicit, near-optimal hedging strategies that are robust across various parameters and do not require prior knowledge of the optimal strategy's structure.

Contribution

It presents a novel application of genetic programming to generate explicit hedging formulas under nonlinear transaction costs, bypassing complex numerical solutions.

Findings

Strategies are valid over a wide range of parameters

No prior knowledge of the optimal strategy structure is needed

Strategies outperform traditional numerical methods in robustness

Abstract

Hedging in the presence of transaction costs leads to complex optimization problems. These problems typically lack closed-form solutions, and their implementation relies on numerical methods that provide hedging strategies for specific parameter values. In this paper we use a genetic programming algorithm to derive explicit formulas for near-optimal hedging strategies under nonlinear transaction costs. The strategies are valid over a large range of parameter values and require no information about the structure of the optimal hedging strategy.