Strong predictor-corrector euler methods for stochastic differential equations

Bruti-Liberati, N; Platen, E

Strong predictor-corrector euler methods for stochastic differential equations

Bruti-Liberati, N Platen, E

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Publication Type:: Journal Article
Citation:: Stochastics and Dynamics, 2008, 8 (3), pp. 561 - 581
Issue Date:: 2008-11-27

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Field	Value	Language
dc.contributor.author	Bruti-Liberati, N	en_US
dc.contributor.author	Platen, E https://orcid.org/0000-0003-4595-3123	en_US
dc.date.issued	2008-11-27	en_US
dc.identifier.citation	Stochastics and Dynamics, 2008, 8 (3), pp. 561 - 581	en_US
dc.identifier.issn	0219-4937	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8310
dc.description.abstract	This paper introduces a new class of numerical schemes for the pathwise approximation of solutions of stochastic differential equations (SDEs). The proposed family of strong predictor-corrector Euler methods are designed to handle scenario simulation of solutions of SDEs. It has the potential to overcome some of the numerical instabilities that are often experienced when using the explicit Euler method. This is of importance, for instance, in finance where martingale dynamics arise for solutions of SDEs with multiplicative diffusion coefficients. Numerical experiments demonstrate the improved asymptotic stability properties of the proposed symmetric predictor-corrector Euler methods. © 2008 World Scientific Publishing Company.	en_US
dc.relation.ispartof	Stochastics and Dynamics	en_US
dc.relation.isbasedon	10.1142/S0219493708002457	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Strong predictor-corrector euler methods for stochastic differential equations	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	8	en_US
utslib.for	010205 Financial Mathematics	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	0104 Statistics	en_US
dc.location.activity	Univ Paderborn, Paderborn, GERMANY
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

This paper introduces a new class of numerical schemes for the pathwise approximation of solutions of stochastic differential equations (SDEs). The proposed family of strong predictor-corrector Euler methods are designed to handle scenario simulation of solutions of SDEs. It has the potential to overcome some of the numerical instabilities that are often experienced when using the explicit Euler method. This is of importance, for instance, in finance where martingale dynamics arise for solutions of SDEs with multiplicative diffusion coefficients. Numerical experiments demonstrate the improved asymptotic stability properties of the proposed symmetric predictor-corrector Euler methods. © 2008 World Scientific Publishing Company.

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http://hdl.handle.net/10453/8310