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
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.
25 pages, First version: March 30, 2001. Revised: August 3, 2001. Earlier revisions: August 3, 2001.
Questions (including download problems) about the papers in this series should be directed to David Edgerton ()
Report other problems with accessing this service to Sune Karlsson ().
This page generated on 2018-02-06 14:12:25.