First order strong approximations of jump diffusions

Bruti-Liberati, N; Nikitopoulos-Sklibosios, C; Platen, E

First order strong approximations of jump diffusions

Bruti-Liberati, N Nikitopoulos-Sklibosios, C

Platen, E

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Publication Type:: Journal Article
Citation:: Monte Carlo Methods and Applications, 2006, 12 (3), pp. 191 - 209
Issue Date:: 2006-10-01

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Field	Value	Language
dc.contributor.author	Bruti-Liberati, N	en_US
dc.contributor.author	Nikitopoulos-Sklibosios, C https://orcid.org/0000-0002-5879-3731	en_US
dc.contributor.author	Platen, E https://orcid.org/0000-0003-4595-3123	en_US
dc.date.issued	2006-10-01	en_US
dc.identifier.citation	Monte Carlo Methods and Applications, 2006, 12 (3), pp. 191 - 209	en_US
dc.identifier.issn	0929-9629	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3427
dc.description.abstract	This paper presents new results on strong numerical schemes, which are appropriate for scenario analysis, filtering and hedge simulation, for stochastic differential equations (SDEs) of jump-diffusion type. It provides first order strong approximations for jump-diffusion SDEs driven by Wiener processes and Poisson random measures. The paper covers first order derivative-free, drift-implicit and jump-adapted strong approximations. Moreover, it provides a commutativity condition under which the computational effort of first order strong schemes is independent of the total intensity of the jump measure. Finally, a numerical study on the accuracy of several strong schemes applied to the Merton model is presented. © VSP 2006.	en_US
dc.relation.ispartof	Monte Carlo Methods and Applications	en_US
dc.relation.isbasedon	10.1163/156939606778705191	en_US
dc.title	First order strong approximations of jump diffusions	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	12	en_US
utslib.for	0104 Statistics	en_US
utslib.for	010205 Financial Mathematics	en_US
utslib.for	0102 Applied Mathematics	en_US
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 Business/Finance Discipline
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	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	12	en_US

Abstract:

This paper presents new results on strong numerical schemes, which are appropriate for scenario analysis, filtering and hedge simulation, for stochastic differential equations (SDEs) of jump-diffusion type. It provides first order strong approximations for jump-diffusion SDEs driven by Wiener processes and Poisson random measures. The paper covers first order derivative-free, drift-implicit and jump-adapted strong approximations. Moreover, it provides a commutativity condition under which the computational effort of first order strong schemes is independent of the total intensity of the jump measure. Finally, a numerical study on the accuracy of several strong schemes applied to the Merton model is presented. © VSP 2006.

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http://hdl.handle.net/10453/3427