Hedging without sweat : a genetic programming approach
Working paper

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Date
2013Metadata
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- Working papers (FIN) [10]
Abstract
Hedging in the presence of transaction costs leads to complex op-
timization problems. These problems typically lack closed-form so-
lutions, and their implementation relies on numerical methods that
provide hedging strategies for speci c 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.