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Hedging without sweat: a genetic programming approach

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  • Terje Lensberg
  • Klaus Reiner Schenk-Hopp'e

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.

Suggested Citation

  • Terje Lensberg & Klaus Reiner Schenk-Hopp'e, 2013. "Hedging without sweat: a genetic programming approach," Papers 1305.6762, arXiv.org.
    • Handle: RePEc:arx:papers:1305.6762
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    References listed on IDEAS

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    1. Valeri zakamouline, 2006. "Efficient analytic approximation of the optimal hedging strategy for a European call option with transaction costs," Quantitative Finance, Taylor & Francis Journals, vol. 6(5), pages 435-445.
    2. A. E. Whalley & P. Wilmott, 1997. "An Asymptotic Analysis of an Optimal Hedging Model for Option Pricing with Transaction Costs," Mathematical Finance, Wiley Blackwell, vol. 7(3), pages 307-324, July.
    3. Monoyios, Michael, 2004. "Option pricing with transaction costs using a Markov chain approximation," Journal of Economic Dynamics and Control, Elsevier, vol. 28(5), pages 889-913, February.
    4. Umut Çetin & L. C. G. Rogers, 2007. "Modeling Liquidity Effects In Discrete Time," Mathematical Finance, Wiley Blackwell, vol. 17(1), pages 15-29, January.
    5. Pekka Malo & Teemu Pennanen, 2012. "Reduced form modeling of limit order markets," Quantitative Finance, Taylor & Francis Journals, vol. 12(7), pages 1025-1036, April.
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    Cited by:

    1. Palczewski, Jan & Poulsen, Rolf & Schenk-Hoppé, Klaus Reiner & Wang, Huamao, 2015. "Dynamic portfolio optimization with transaction costs and state-dependent drift," European Journal of Operational Research, Elsevier, vol. 243(3), pages 921-931.

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