European Option Pricing by Using the Support Vector Regression Approach

Authors:
Panayiotis C. Andreou;Chris Charalambous;Spiros H. Martzoukos
Affiliations:
Durham University, UK;University of Cyprus, Cyprus;University of Cyprus, Cyprus
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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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.