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A Neural Network Versus Black-Scholes: A Comparison of Pricing and Hedging Performances

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  • Amilon, Henrik

    ()
    (Department of Economics, Lund University)

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    Abstract

    The Black-Scholes formula is a well-known model for pricing and hedging derivative securities. It relies, however, on several highly questionable assumptions. This paper examines whether a neural network (MLP) can be used to find a call option pricing formula better corresponding to market prices and the properties of the underlying asset than the Black-Scholes formula. The neural network method is applied to the out-of-sample pricing and delta-hedging of daily Swedish stock index call options from 1997-1999. The relevance of a hedge-analysis is stressed further in this paper. As benchmarks, the Black-Scholes model with historical and implicit volatility estimates is used. Comparisons reveal that the neural network models outperform the benchmarks both in pricing and hedging performances. A moving block bootstrap procedure is used to test the statistical significance of the results. Although the neural networks are superiour, the results are sometimes insignificant at the 5% level.

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    Bibliographic Info

    Paper provided by Lund University, Department of Economics in its series Working Papers with number 2001:5.

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    Length: 25 pages
    Date of creation: 30 Mar 2001
    Date of revision: 03 Aug 2001
    Publication status: Published in Journal of Forecasting , 2003, pages 317-335.
    Handle: RePEc:hhs:lunewp:2001_005

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    Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden
    Phone: +46 +46 222 0000
    Fax: +46 +46 2224613
    Web page: http://www.nek.lu.se/en
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    Keywords: option pricing; hedging; bootstrap; statistical inference;

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