Options Pricing Via Statistical Learning Techniques: The Support Vector Regression Approach
Panayiotis C. Andreou
Cyprus University of Technology; Durham University - Durham Business School
University of Cyprus - Department of Public and Business Administration
University of Cyprus - Department of Public and Business Administration; George Washington University - School of Business
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.
Number of Pages in PDF File: 25
Keywords: Option pricing, implied volatilities, implied parameters, deterministic volatility functions, support vector machines, neural networks
JEL Classification: G13, G14working papers series
Date posted: June 16, 2008
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