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.
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Kim, BH., Lee, D., Lee, J. (2006). Local Volatility Function Approximation Using Reconstructed Radial Basis Function Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_77
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DOI: https://doi.org/10.1007/11760191_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
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