Learning short-option valuation in the presence of rare events

Thumbnail Image

Date

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

We present a neural-network valuation of financial derivatives in the case of fat-tailed underlying asset returns. A two-layer perceptron is trained on simulated prices taking into account the well-known effect of volatility smile. The prices of the underlier are generated using fractional calculus algorithms, and option prices are computed by means of the Bouchaud-Potters formula. This learning scheme is tested on market data; the results show a very good agreement between perceptron option prices and real market ones.
details and related works in http://www.econophysics.org

Keywords

Statistical Mechanics, Disordered Systems and Neural Networks, Pricing of Securities

Citation

URI

http://salesiana.dossiersoluciones.com/handle/123456789/210070

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cond-mat/0001253
 http://arxiv.org/abs/cond-mat/0001253
 International Journal of Theoretical and Applied Finance 3, 563-564 (2000)
 doi:10.1142/S0219024900000590

Collections

arXiv