A new technique for simulating the likelihood of stochastic differential equations
Autor(a) principal: | |
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Data de Publicação: | 2002 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.5/27596 |
Resumo: | This article presents a new simulation-based technique for estimating the likelihood of stochastic differential equations. This technique is based on a result of Dacunha-Castelle and Florens-Zmirou. These authors proved that the transition densities of a nonlinear diffusion process with a constant diffusion coefficient can be written in a closed form involving a stochastic integral. We show that this stochastic integral can be easily estimated through simulations and we prove a convergence result. This simulator for the transition density is used to obtain the simulated maximum likelihood (SML) estimator. We show through some Monte Carlo experiments that our technique is highly computationally efficient and the SML estimator converges rapidly to the maximum likelihood estimator |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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A new technique for simulating the likelihood of stochastic differential equationsSimulated Maximum Likelihood EstimatorSimulation-based MethodEstimationStochastic Differential EquationsTransition Density EstimationDiffusion ProcessesThis article presents a new simulation-based technique for estimating the likelihood of stochastic differential equations. This technique is based on a result of Dacunha-Castelle and Florens-Zmirou. These authors proved that the transition densities of a nonlinear diffusion process with a constant diffusion coefficient can be written in a closed form involving a stochastic integral. We show that this stochastic integral can be easily estimated through simulations and we prove a convergence result. This simulator for the transition density is used to obtain the simulated maximum likelihood (SML) estimator. We show through some Monte Carlo experiments that our technique is highly computationally efficient and the SML estimator converges rapidly to the maximum likelihood estimatorRoyal Economic Society | Blackwell Publishers Ltd.Repositório da Universidade de LisboaNicolau, João2023-04-06T13:09:37Z20022002-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/27596engNicolau, João .(2002). “A new technique for simulating the likelihood of stochastic differential equations”. Econometrics Journal, Volume 5: pp. 91–103. (Search PDF in 2023)info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-04-09T01:32:20Zoai:www.repository.utl.pt:10400.5/27596Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:49:03.591344Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A new technique for simulating the likelihood of stochastic differential equations |
title |
A new technique for simulating the likelihood of stochastic differential equations |
spellingShingle |
A new technique for simulating the likelihood of stochastic differential equations Nicolau, João Simulated Maximum Likelihood Estimator Simulation-based Method Estimation Stochastic Differential Equations Transition Density Estimation Diffusion Processes |
title_short |
A new technique for simulating the likelihood of stochastic differential equations |
title_full |
A new technique for simulating the likelihood of stochastic differential equations |
title_fullStr |
A new technique for simulating the likelihood of stochastic differential equations |
title_full_unstemmed |
A new technique for simulating the likelihood of stochastic differential equations |
title_sort |
A new technique for simulating the likelihood of stochastic differential equations |
author |
Nicolau, João |
author_facet |
Nicolau, João |
author_role |
author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Nicolau, João |
dc.subject.por.fl_str_mv |
Simulated Maximum Likelihood Estimator Simulation-based Method Estimation Stochastic Differential Equations Transition Density Estimation Diffusion Processes |
topic |
Simulated Maximum Likelihood Estimator Simulation-based Method Estimation Stochastic Differential Equations Transition Density Estimation Diffusion Processes |
description |
This article presents a new simulation-based technique for estimating the likelihood of stochastic differential equations. This technique is based on a result of Dacunha-Castelle and Florens-Zmirou. These authors proved that the transition densities of a nonlinear diffusion process with a constant diffusion coefficient can be written in a closed form involving a stochastic integral. We show that this stochastic integral can be easily estimated through simulations and we prove a convergence result. This simulator for the transition density is used to obtain the simulated maximum likelihood (SML) estimator. We show through some Monte Carlo experiments that our technique is highly computationally efficient and the SML estimator converges rapidly to the maximum likelihood estimator |
publishDate |
2002 |
dc.date.none.fl_str_mv |
2002 2002-01-01T00:00:00Z 2023-04-06T13:09:37Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.5/27596 |
url |
http://hdl.handle.net/10400.5/27596 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Nicolau, João .(2002). “A new technique for simulating the likelihood of stochastic differential equations”. Econometrics Journal, Volume 5: pp. 91–103. (Search PDF in 2023) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Royal Economic Society | Blackwell Publishers Ltd. |
publisher.none.fl_str_mv |
Royal Economic Society | Blackwell Publishers Ltd. |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799131572095418368 |