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JournalISSN: 1460-1559

Journal of Computational Finance 

Infopro Digital
About: Journal of Computational Finance is an academic journal published by Infopro Digital. The journal publishes majorly in the area(s): Computational finance & Valuation of options. It has an ISSN identifier of 1460-1559. Over the lifetime, 563 publications have been published receiving 16455 citations.


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Journal ArticleDOI
TL;DR: In this paper, the fast Fourier transform is used to value options when the characteristic function of the return is known analytically, and it is shown how to use it for value selection.
Abstract: This paper shows how the fast Fourier Transform may be used to value options when the characteristic function of the return is known analytically.

2,306 citations

Journal ArticleDOI
Roger Lee1
TL;DR: In this general setting, the numerical pricing error of discretized transform computations, such as DFT/FFT, is bound to enable algorithms to select efficient quadrature parameters and to price with guaranteed numerical accuracy.
Abstract: We extend and unify Fourier-analytic methods for pricing a wide class of options on any underlying state variable whose characteristic function is known. In this general setting, we bound the numerical pricing error of discretized transform computations, such as DFT/FFT. These bounds enable algorithms to select efficient quadrature parameters and to price with guaranteed numerical accuracy.

402 citations

Journal ArticleDOI
TL;DR: A new stochastic mesh method is presented for pricing high-dimensional American options when there is a finite, but possibly large, number of exercise dates and the algorithm provides point estimates and confidence intervals and it converges to the correct values as the computational effort increases.
Abstract: High-dimensional problems frequently arise in the pricing of derivative securities – for example, in pricing options on multiple underlying assets and in pricing term structure derivatives American versions of these options, ie, where the owner has the right to exercise early, are particularly challenging to price We introduce a stochastic mesh method for pricing high-dimensional American options when there is a finite, but possibly large, number of exercise dates The algorithm provides point estimates and confidence intervals; we provide conditions under which these estimates converge to the correct values as the computational effort increases Numerical results illustrate the performance of the method

341 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
20236
202213
20214
202017
201919
201815