Hedging without sweat: a genetic programming approach
Risk Management
2013-05-30 v1 Pricing of Securities
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.
Keywords
Cite
@article{arxiv.1305.6762,
title = {Hedging without sweat: a genetic programming approach},
author = {Terje Lensberg and Klaus Reiner Schenk-Hoppé},
journal= {arXiv preprint arXiv:1305.6762},
year = {2013}
}