Local Volatility Function Approximation Using Reconstructed Radial Basis Function Networks

  • Bo-Hyun Kim
  • Daewon Lee
  • Jaewook Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


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.


Radial Basis Function Option Price Implied Volatility Strike Price Local Volatility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bo-Hyun Kim
    • 1
  • Daewon Lee
    • 1
  • Jaewook Lee
    • 1
  1. 1.Department of Industrial and Management EngineeringPohang University of Science and TechnologyPohang, KyungbukKorea

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