LEARNING SHORT-OPTION VALUATION IN THE PRESENCE OF RARE EVENTS

Marco Raberto, G. Cuniberti, M. Riani, E. Scales, F. Mainardi and G. Servizi
Additional contact information
G. Cuniberti: Max-Planck-Institut PKS, Nöthnitzer Str. 38, D-01187, Dresden, Germany
M. Riani: INFM and Dipartimento di Fisica, Università di Genova, via Dodecaneso 33, I-16146 Genova, Italy
E. Scales: Lab33 S.r.l., corso Perrone 24, 1-16152 Genova, Italy
F. Mainardi: INFN and Dipartimento di Fisica, Universitàdi Bologna, via Irnerio 46, I-40126 Bologna, Italy
G. Servizi: INFN and Dipartimento di Fisica, Universitàdi Bologna, via Irnerio 46, I-40126 Bologna, Italy

International Journal of Theoretical and Applied Finance (IJTAF), 2000, vol. 03, issue 03, 563-564

Abstract: We extend the neural-network approach for the valution of financial derivatives developed by Hutchinsonet al.[1] to the case of fat-tailed distributions of the underlying asset returns. We use a two-layer perceptron with three inputs, four hidden neurons, and one output. The input parameters of the network are: the simulated price of the underlying assetFdivided by the strike priceE, the time-to-maturityT, and the ratio|F-E|/T. The latter takes into account the volatility smile, whereas the priceFis generated by the method of Gorenfloet al.[2] based on fractional calculus. The output parameter is the call priceCoverE. The learning-set option priceCis computed by means of a formula given by Bouchaud and Potters [3, 4]. Option prices obtained by means of this learning scheme are compared with LIFFE option prices on German treasury bond (BUND) futures.

Keywords: Econophysics; learning; option princing; statistical finance (search for similar items in EconPapers)
Date: 2000
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Working Paper: Learning short-option valuation in the presence of rare events (2000)
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DOI: 10.1142/S0219024900000590

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