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A neural network versus Black-Scholes: A comparison of pricing and hedging performances

Amilon, Henrik LU (2003) In Journal of Forecasting 22(4). p.317-335
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 to 1999. The relevance of a hedge-analysis is stressed further in this paper. As benchmarks, the Black-Scholes model with historical and implied volatility estimates are used. Comparisons reveal that the neural network models outperform the... (More)
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 to 1999. The relevance of a hedge-analysis is stressed further in this paper. As benchmarks, the Black-Scholes model with historical and implied volatility estimates are used. Comparisons reveal that the neural network models outperform the benchmarks both in pricing and hedging performances. A moving block bootstrap is used to test the statistical significance of the results. Although the neural networks are superior, the results are sometimes insignificant at the 5% level. Copyright (C) 2003 John Wiley Sons, Ltd. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
neural networks, option pricing, hedging, bootstrap, inference, statistical
in
Journal of Forecasting
volume
22
issue
4
pages
317 - 335
publisher
Wiley Online Library
external identifiers
  • wos:000184482200003
  • scopus:0042697754
ISSN
1099-131X
DOI
10.1002/for.867
language
English
LU publication?
yes
id
33a6f474-b1b0-426e-8286-9c24b550559d (old id 900075)
date added to LUP
2008-01-10 09:07:52
date last changed
2018-05-29 10:07:43
@article{33a6f474-b1b0-426e-8286-9c24b550559d,
  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 to 1999. The relevance of a hedge-analysis is stressed further in this paper. As benchmarks, the Black-Scholes model with historical and implied volatility estimates are used. Comparisons reveal that the neural network models outperform the benchmarks both in pricing and hedging performances. A moving block bootstrap is used to test the statistical significance of the results. Although the neural networks are superior, the results are sometimes insignificant at the 5% level. Copyright (C) 2003 John Wiley Sons, Ltd.},
  author       = {Amilon, Henrik},
  issn         = {1099-131X},
  keyword      = {neural networks,option pricing,hedging,bootstrap,inference,statistical},
  language     = {eng},
  number       = {4},
  pages        = {317--335},
  publisher    = {Wiley Online Library},
  series       = {Journal of Forecasting},
  title        = {A neural network versus Black-Scholes: A comparison of pricing and hedging performances},
  url          = {http://dx.doi.org/10.1002/for.867},
  volume       = {22},
  year         = {2003},
}