Learning short-option valuation in the presence of rare events

Marco Raberto, G. Cuniberti, Enrico Scalas, Marco Riani, F. Mainardi and G. Servizi

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Abstract: 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.

Date: 2000-01
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Published in International Journal of Theoretical and Applied Finance 3, 563-564 (2000)

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Related works:
Journal Article: LEARNING SHORT-OPTION VALUATION IN THE PRESENCE OF RARE EVENTS (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:cond-mat/0001253

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