Local volatility function approximation using reconstructed radial basis function networks

Authors:
Bo-Hyun Kim;Daewon Lee;Jaewook Lee
Affiliations:
Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk, Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk, Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk, Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

Modelling volatility smile is very important in financial practice for pricing and hedging derivatives. In this paper, a novel learning method to approximate a local volatility function from a finite market data set is proposed. The proposed method trains a RBF network with fewer volatility data and finds an optimized network through option pricing error minimization. Numerical experiments are conducted on S&P 500 call option market data to illustrate a local volatility surface estimated by the method.