Hedging without sweat: a genetic programming approach

Terje Lensberg and Klaus Reiner Schenk-Hopp\'e

Papers from arXiv.org

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.

Date: 2013-05
New Economics Papers: this item is included in nep-cmp and nep-rmg
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