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Options Pricing Via Statistical Learning Techniques: The Support Vector Regression ApproachPanayiotis C. AndreouCyprus University of Technology; Durham University - Durham Business School Christakis CharalambousUniversity of Cyprus - Department of Public and Business Administration Spiros MartzoukosUniversity of Cyprus - Department of Public and Business Administration; George Washington University - School of Business June 2008 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 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, G14 working papers seriesDate posted: June 16, 2008Suggested CitationContact Information
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