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13. 


Learning short-option valuation in the presence of rare events



M. Raberto, G. Cuniberti, M. Riani, E. Scalas, F. Mainardi, and G. Servizi

International Journal of Theoretical and Applied Finance 3, 563 (2000)

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.



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doi absolute link10.1142/S0219024900000590
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online abstractarXiv preprint: cond-mat/0001253
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mpi-pks/0001002
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