Computational Methods for Derivatives with Early Exercise Features

Carl Chiarella; Boda Kang; G.H. Meyer; A. Ziogas

doi:10.1016/B978-0-444-52980-0.00005-0

Computational Methods for Derivatives with Early Exercise Features

Carl Chiarella, Boda Kang, G.H. Meyer, A. Ziogas

Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

In this paper we consider various computational methods for pricing American style derivatives. We do so under both jump diffusion and stochastic volatility processes. We consider integral transform methods, the method of lines, operator-splitting, and the Crank-Nicolson scheme, the latter being used to generate the benchmark solution. Overall, we find that the method of lines approach is quite competitive with other methods for the problems considered in this paper. As one goes to higher dimensions it may be necessary to use methods such as the sparse grid approach.

Original language	English
Title of host publication	Handbook of Computational Economics
Editors	Karl Schmedders, Kenneth L. Judd
Publisher	Elsevier
Pages	225-275
Number of pages	51
Volume	3
ISBN (Print)	978-0-444-52980-0
DOIs	https://doi.org/10.1016/B978-0-444-52980-0.00005-0
Publication status	Published - Jan 2014

Access to Document

10.1016/B978-0-444-52980-0.00005-0

http://www.sciencedirect.com/science/article/pii/B9780444529800000050

Cite this

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title = "Computational Methods for Derivatives with Early Exercise Features",

abstract = "In this paper we consider various computational methods for pricing American style derivatives. We do so under both jump diffusion and stochastic volatility processes. We consider integral transform methods, the method of lines, operator-splitting, and the Crank-Nicolson scheme, the latter being used to generate the benchmark solution. Overall, we find that the method of lines approach is quite competitive with other methods for the problems considered in this paper. As one goes to higher dimensions it may be necessary to use methods such as the sparse grid approach.",

author = "Carl Chiarella and Boda Kang and G.H. Meyer and A. Ziogas",

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