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European Option Pricing by Using the Support Vector Regression Approach

Andreou, P.C.; Charalambous, C.; Martzoukos, S.H.

Authors

C. Charalambous

S.H. Martzoukos



Abstract

We explore the pricing performance of Support Vector Regression for pricing S&P 500 index call options. Support Vector Regression is a novel nonparametric methodology that has been developed in the context of statistical learning theory, and until now it has not been widely used in financial econometric applications. This new method is compared with the Black and Scholes (1973) option pricing model, using standard implied parameters and parameters derived via the Deterministic Volatility Functions approach. The empirical analysis has shown promising results for the Support Vector Regression models.

Citation

Andreou, P., Charalambous, C., & Martzoukos, S. (2009). European Option Pricing by Using the Support Vector Regression Approach. In Artificial neural networks – ICANN 2009 : 19th international conference, Limassol, Cypros, September 14-17, 2009 : proceedings. Part I (874-883). https://doi.org/10.1007/978-3-642-04274-4_90

Conference Name Artificial Neural Networks – ICANN 2009
Conference Location Limassol, Cyprus
Publication Date Jan 1, 2009
Deposit Date Oct 7, 2009
Pages 874-883
Series Title Lecture notes in computer science
Series Number 5768
Series ISSN 0302-9743,1611-3349
Book Title Artificial neural networks – ICANN 2009 : 19th international conference, Limassol, Cypros, September 14-17, 2009 : proceedings. Part I.
DOI https://doi.org/10.1007/978-3-642-04274-4_90
Keywords Option pricing, Implied volatility, Non-parametric methods, Support vector regression.
Public URL https://durham-repository.worktribe.com/output/1160571