Andreou, P. C. and Charalambous, C. and Martzoukos, S. H. (2009) 'European option pricing by using the support vector regression approach.', in Artificial neural networks – ICANN 2009. Berlin ; Heidelberg: Springer Berlin, pp. 874-883. Lecture notes in computer science. (5768).
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.
|Item Type:||Book chapter|
|Additional Information:||Proceedings of the 19th International Conference, Limassol, Cyprus, September 14-17, 2009.|
|Keywords:||Option pricing, Implied volatility, Non-parametric methods, Support vector regression.|
|Full text:||Full text not available from this repository.|
|Publisher Web site:||http://dx.doi.org/10.1007/978-3-642-04274-4_90|
|Record Created:||07 Oct 2009 11:50|
|Last Modified:||03 Nov 2009 12:54|
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