Learning short-option valuation in the presence of rare events

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dc.creatorRaberto, M.
dc.creatorCuniberti, G.
dc.creatorScalas, E.
dc.creatorRiani, M.
dc.creatorMainardi, F.
dc.creatorServizi, G.
dc.date2000-01-18
dc.date.accessioned2026-07-07T12:10:55Z
dc.date.available2026-07-07T12:10:55Z
dc.descriptionWe 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.
dc.descriptiondetails and related works in http://www.econophysics.org
dc.identifierhttps://arxiv.org/abs/cond-mat/0001253
dc.identifierhttp://arxiv.org/abs/cond-mat/0001253
dc.identifierInternational Journal of Theoretical and Applied Finance 3, 563-564 (2000)
dc.identifierdoi:10.1142/S0219024900000590
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/210070
dc.subjectStatistical Mechanics
dc.subjectDisordered Systems and Neural Networks
dc.subjectPricing of Securities
dc.titleLearning short-option valuation in the presence of rare events
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