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Learning short-option valuation in the presence of rare events

Listed author(s):
  • M. Raberto
  • G. Cuniberti
  • E. Scalas
  • M. Riani
  • F. Mainardi
  • G. Servizi

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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Paper provided by in its series Papers with number cond-mat/0001253.

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Date of creation: Jan 2000
Publication status: Published in International Journal of Theoretical and Applied Finance 3, 563-564 (2000)
Handle: RePEc:arx:papers:cond-mat/0001253
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