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| Author: | Avellaneda, M. Carelli, A. Stella, F. |
| Title: | A Bayesian approach for constructing implied volatility surfaces through neural networks |
| Journal: | Journal of Computational Finance
2001 : FALL, VOL. 4:1, p. 83-107 |
| Index terms: | Neural networks Bayesian statistics Option valuation Option prices |
| Language: | eng |
| Abstract: | In this paper the authors present a new option pricing scheme which deals with a nonconstant volatility for the price of the underlying asset. The main feature of the proposed pricing scheme consists of exploiting recent developments about Bayesian learning within the artificial neural networks framework. The nonparametric model of the implied volatility furface, obtained through an infinite feedforward neural network and by exploiting the Bayesian formulation of the learning problem, is used within the proposed option pricing scheme. This pricing scheme relies upon the Dupire formula, which maps the implied volatility surface to the coresponding local volatility function. |
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