25 Pages Posted: 16 Jun 2008
Date Written: June 2008
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 been practically neglected in financial econometric applications. This new method is compared with Parametric Options Pricing Models using standard implied parameters and parameters derived via Deterministic Volatility Functions. The empirical analysis has shown promising results for the Support Vector Regression approach.
Keywords: Option pricing, implied volatilities, implied parameters, deterministic volatility functions, support vector machines, neural networks
JEL Classification: G13, G14
Suggested Citation: Suggested Citation
Andreou, Panayiotis C. and Charalambous, Christakis and Martzoukos, Spiros, Options Pricing Via Statistical Learning Techniques: The Support Vector Regression Approach (June 2008). Available at SSRN: https://ssrn.com/abstract=1145329 or http://dx.doi.org/10.2139/ssrn.1145329
By David Bates