Local volatility function approximation using reconstructed radial basis function networks
SCIE
SCOPUS
- Title
- Local volatility function approximation using reconstructed radial basis function networks
- Authors
- Kim, BH; Lee, D; Lee, J
- Date Issued
- 2006-01
- Publisher
- SPRINGER-VERLAG BERLIN
- 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.
- Keywords
- OPTIONS
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/23887
- DOI
- 10.1007/11760191_77
- ISSN
- 0302-9743
- Article Type
- Article
- Citation
- LECTURE NOTES IN COMPUTER SCIENCE, vol. 3973, page. 524 - 530, 2006-01
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