This paper aims to examine how the radial basis function (RBF) technique works in the financial field, to compare the RBF performance with the results obtained with traditional methods (FDM, FEM), to choose the more suitable radial basis function to solve option pricing and to explain how its shape parameters can be set. It is crucial to set properly the shape parameter for the precision of the method and ultimately for the determination of the derivatives fair-value. Applying a maximum likelihood estimation (MLE), the authors propose a financial approach for its evaluation based on market/theoretical prices calibration.

Option pricing via Radial Basis Functions: Performance comparison with traditional numerical integration scheme and parameters choice for a reliable pricing

Pier Giuseppe Giribone;Simone Ligato
2015-01-01

Abstract

This paper aims to examine how the radial basis function (RBF) technique works in the financial field, to compare the RBF performance with the results obtained with traditional methods (FDM, FEM), to choose the more suitable radial basis function to solve option pricing and to explain how its shape parameters can be set. It is crucial to set properly the shape parameter for the precision of the method and ultimately for the determination of the derivatives fair-value. Applying a maximum likelihood estimation (MLE), the authors propose a financial approach for its evaluation based on market/theoretical prices calibration.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1117616
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