Hedging without sweat: a genetic programming approach
AbstractHedging 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.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1305.6762.
Date of creation: May 2013
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-06-04 (All new papers)
- NEP-CMP-2013-06-04 (Computational Economics)
- NEP-RMG-2013-06-04 (Risk Management)
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LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library
2844, London School of Economics and Political Science, LSE Library.
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