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

Henrik Amilon ()
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Henrik Amilon: Department of Economics, Lund University, Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund, Sweden

No 2001:5, Working Papers from Lund University, Department of Economics

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.

Keywords: option pricing; hedging; bootstrap; statistical inference (search for similar items in EconPapers)
JEL-codes: C51 C52 G13 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2001-03-30, Revised 2001-08-03
New Economics Papers: this item is included in nep-evo, nep-fin and nep-fmk
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Citations: View citations in EconPapers (1)

Published in Journal of Forecasting , 2003, pages 317-335.

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Persistent link: https://EconPapers.repec.org/RePEc:hhs:lunewp:2001_005

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