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

Author

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  • Amilon, Henrik

    () (Department of Economics, Lund University)

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.

Suggested Citation

  • Amilon, Henrik, 2001. "A Neural Network Versus Black-Scholes: A Comparison of Pricing and Hedging Performances," Working Papers 2001:5, Lund University, Department of Economics, revised 03 Aug 2001.
  • Handle: RePEc:hhs:lunewp:2001_005
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    More about this item

    Keywords

    option pricing; hedging; bootstrap; statistical inference;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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