Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/6885
Title: European option pricing by using the support vector regression approach
Authors: Andreou, Panayiotis 
Charalambous, Chris
Martzoukos, Spiros H.
Keywords: Econometric models;Artificial neural networks;Vector Research, Inc;Regression analysis
Issue Date: 2009
Publisher: SpringerLink
Source: 19th International Conference on Artificial Neural Networks, 2009, Limassol, Cyprus.
Abstract: We explore the pricing performance of Support Vector Regression for pricing SandP 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.
URI: http://ktisis.cut.ac.cy/handle/10488/6885
http://ktisis.cut.ac.cy/handle/10488/6885
http://hdl.handle.net/10488/6885
DOI: 10.1007/978-3-642-04274-4_90
Rights: © 2009 Springer Berlin Heidelberg
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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