Adaptive Radial Basis Function Methods for Pricing Options Under Jump-Diffusion Models

Article


Chan, R. 2016. Adaptive Radial Basis Function Methods for Pricing Options Under Jump-Diffusion Models. Computational Economics. 47 (4), pp. 623-643.
AuthorsChan, R.
Abstract

The aim of this paper is to show that option prices in jump-diffusion models can be computed using meshless methods based on radial basis function (RBF) interpolation instead of traditional mesh-based methods like finite differences or finite elements. The RBF technique is demonstrated by solving the partial integro-differential equation for American and European options on non-dividend-paying stocks in the Merton jump-diffusion model, using the inverse multiquadric radial basis function. The method can in principle be extended to Lévy-models. Moreover, an adaptive method is proposed to tackle the accuracy problem caused by a singularity in the initial condition so that the accuracy in option pricing in particular for small time to maturity can be improved.

JournalComputational Economics
Journal citation47 (4), pp. 623-643
ISSN0927-7099
Year2016
PublisherSpringer Verlag for Society for Computational Economics
Accepted author manuscript
Digital Object Identifier (DOI)doi:10.1007/s10614-016-9563-6
Web address (URL)https://doi.org/10.1007/s10614-016-9563-6
Publication dates
Online14 Mar 2016
Print01 Apr 2016
Publication process dates
Deposited01 Dec 2017
Accepted13 Feb 2016
Accepted13 Feb 2016
Copyright information© 2016 Springer Science+Business Media New York. This is a post-peer-review, pre-copyedit version of an article published in Computational Economics. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10614-016-9563-6.
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https://repository.uel.ac.uk/item/85198

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