European Option Pricing by Using the Support Vector Regression Approach

Andreou, P.C.; Charalambous, C.; Martzoukos, S.H.

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.

Citation

Andreou, P., Charalambous, C., & Martzoukos, S. (2009, December). European Option Pricing by Using the Support Vector Regression Approach. Presented at Artificial Neural Networks – ICANN 2009, Limassol, Cyprus

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Artificial Neural Networks – ICANN 2009
• Publication Date: Jan 1, 2009
• Deposit Date: Oct 7, 2009
• Print ISSN: 0302-9743
• Pages: 874-883
• Series Title: Lecture notes in computer science
• Series Number: 5768
• Series ISSN: 0302-9743,1611-3349
• Book Title: Artificial neural networks – ICANN 2009 : 19th international conference, Limassol, Cypros, September 14-17, 2009 : proceedings. Part I.
• DOI: https://doi.org/10.1007/978-3-642-04274-4_90
• Keywords: Option pricing, Implied volatility, Non-parametric methods, Support vector regression.
• Public URL: https://durham-repository.worktribe.com/output/1160571