Particle Filters and Bayesian Inference in Financial Econometrics

In this paper we review sequential Monte Carlo (SMC) methods, or particle fi lters (PF), with special emphasis on its potential applications in fi nancial time series analysis and econometrics. We start with the well-known normal dynamic linear model, also known as the normal linear state space mode...

Descripción completa

Detalles Bibliográficos
Autores: Tsay, Ruey S., HEDIBERT FREITAS LOPES
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2011
País:Brasil
Institución:Instituição de Ensino Superior e de Pesquisa (INSPER)
Repositorio:Repositório Institucional da INSPER
Idioma:inglés
OAI Identifier:oai:repositorio.insper.edu.br:11224/4129
Acceso en línea:https://repositorio.insper.edu.br/handle/11224/4129
Access Level:acceso abierto
Palabra clave:particle learning
sequential Monte Carlo
Markov chain Monte Carlo
stochastic volatility
realized volatility
Nelson-Siegel model
id BR_7ca7b6f6dae3dd13fffdcc0fef9907c8
oai_identifier_str oai:repositorio.insper.edu.br:11224/4129
network_acronym_str BR
network_name_str Brasil
repository_id_str
spelling Particle Filters and Bayesian Inference in Financial Econometricsparticle learningsequential Monte CarloMarkov chain Monte Carlostochastic volatilityrealized volatilityNelson-Siegel modelIn this paper we review sequential Monte Carlo (SMC) methods, or particle fi lters (PF), with special emphasis on its potential applications in fi nancial time series analysis and econometrics. We start with the well-known normal dynamic linear model, also known as the normal linear state space model, for which sequential state learning is available in closed form via standard Kalman fi lter and Kalman smoother recursions. Particle fi lters are then introduced as a set of Monte Carlo schemes that enable Kalman-type recursions when normality or linearity or both are abandoned. The seminal bootstrap fi lter (BF) of Gordon, Salmond and Smith (1993) is used to introduce the SMC jargon, potentials and limitations. We also review the literature on parameter learning, an area that started to attract much attention from the particle fi lter community in recent years. We give particular attention to the Liu–West fi lter (2001), Storvik fi lter (2002) and particle learning (PL) of Carvalho, Johannes, Lopes and Polson (2010). We argue that the BF and the auxiliary particle fi lter (APF) of Pitt and Shephard (1999) defi ne two fundamentally distinct directions within the particle fi lter lit erature. We also show that the distinction is more pronounced with parameter learning and argue that PL, which follows the APF direction, is an attractive extension. One of our contributions is to sort out the research from BF to APF (during the 1990s), from APF to now (the 2000s) and from Liu–West fi lter to Storvik fi lter to PL. To this end, we provide code in R for all the examples of the paper. Readers are invited to fi nd their own way into this dynamic and active research arena.John Wiley & Sons, Ltd.2022-10-04T20:13:38Z2022-10-04T20:13:38Z2011info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlep. 168-209Digitalapplication/pdfapplication/pdfhttps://repositorio.insper.edu.br/handle/11224/412910.1002/for.119530Journal of ForecastingNão InformadoNão informadoO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITORinfo:eu-repo/semantics/openAccessengreponame:Repositório Institucional da INSPERinstname:Instituição de Ensino Superior e de Pesquisa (INSPER)instacron:INSPERTsay, Ruey S.HEDIBERT FREITAS LOPESTsay, Ruey S.2025-08-26T15:16:10Zoai:repositorio.insper.edu.br:11224/4129Biblioteca Digital de Teses e Dissertaçõeshttps://www.insper.edu.br/biblioteca-telles/PRIhttps://repositorio.insper.edu.br/oai/requestbiblioteca@insper.edu.br || conteudobiblioteca@insper.edu.bropendoar:2025-08-26T15:16:10Repositório Institucional da INSPER - Instituição de Ensino Superior e de Pesquisa (INSPER)false
dc.title.none.fl_str_mv Particle Filters and Bayesian Inference in Financial Econometrics
title Particle Filters and Bayesian Inference in Financial Econometrics
spellingShingle Particle Filters and Bayesian Inference in Financial Econometrics
Tsay, Ruey S.
particle learning
sequential Monte Carlo
Markov chain Monte Carlo
stochastic volatility
realized volatility
Nelson-Siegel model
title_short Particle Filters and Bayesian Inference in Financial Econometrics
title_full Particle Filters and Bayesian Inference in Financial Econometrics
title_fullStr Particle Filters and Bayesian Inference in Financial Econometrics
title_full_unstemmed Particle Filters and Bayesian Inference in Financial Econometrics
title_sort Particle Filters and Bayesian Inference in Financial Econometrics
dc.creator.none.fl_str_mv Tsay, Ruey S.
HEDIBERT FREITAS LOPES
Tsay, Ruey S.
author Tsay, Ruey S.
author_facet Tsay, Ruey S.
HEDIBERT FREITAS LOPES
author_role author
author2 HEDIBERT FREITAS LOPES
author2_role author
dc.subject.por.fl_str_mv particle learning
sequential Monte Carlo
Markov chain Monte Carlo
stochastic volatility
realized volatility
Nelson-Siegel model
topic particle learning
sequential Monte Carlo
Markov chain Monte Carlo
stochastic volatility
realized volatility
Nelson-Siegel model
description In this paper we review sequential Monte Carlo (SMC) methods, or particle fi lters (PF), with special emphasis on its potential applications in fi nancial time series analysis and econometrics. We start with the well-known normal dynamic linear model, also known as the normal linear state space model, for which sequential state learning is available in closed form via standard Kalman fi lter and Kalman smoother recursions. Particle fi lters are then introduced as a set of Monte Carlo schemes that enable Kalman-type recursions when normality or linearity or both are abandoned. The seminal bootstrap fi lter (BF) of Gordon, Salmond and Smith (1993) is used to introduce the SMC jargon, potentials and limitations. We also review the literature on parameter learning, an area that started to attract much attention from the particle fi lter community in recent years. We give particular attention to the Liu–West fi lter (2001), Storvik fi lter (2002) and particle learning (PL) of Carvalho, Johannes, Lopes and Polson (2010). We argue that the BF and the auxiliary particle fi lter (APF) of Pitt and Shephard (1999) defi ne two fundamentally distinct directions within the particle fi lter lit erature. We also show that the distinction is more pronounced with parameter learning and argue that PL, which follows the APF direction, is an attractive extension. One of our contributions is to sort out the research from BF to APF (during the 1990s), from APF to now (the 2000s) and from Liu–West fi lter to Storvik fi lter to PL. To this end, we provide code in R for all the examples of the paper. Readers are invited to fi nd their own way into this dynamic and active research arena.
publishDate 2011
dc.date.none.fl_str_mv 2011
2022-10-04T20:13:38Z
2022-10-04T20:13:38Z
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 https://repositorio.insper.edu.br/handle/11224/4129
10.1002/for.1195
30
url https://repositorio.insper.edu.br/handle/11224/4129
identifier_str_mv 10.1002/for.1195
30
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Forecasting
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv p. 168-209
Digital
application/pdf
application/pdf
dc.coverage.none.fl_str_mv Não Informado
Não informado
dc.publisher.none.fl_str_mv John Wiley & Sons, Ltd.
publisher.none.fl_str_mv John Wiley & Sons, Ltd.
dc.source.none.fl_str_mv reponame:Repositório Institucional da INSPER
instname:Instituição de Ensino Superior e de Pesquisa (INSPER)
instacron:INSPER
instname_str Instituição de Ensino Superior e de Pesquisa (INSPER)
instacron_str INSPER
institution INSPER
reponame_str Repositório Institucional da INSPER
collection Repositório Institucional da INSPER
repository.name.fl_str_mv Repositório Institucional da INSPER - Instituição de Ensino Superior e de Pesquisa (INSPER)
repository.mail.fl_str_mv biblioteca@insper.edu.br || conteudobiblioteca@insper.edu.br
_version_ 1853673681082908672
score 15,812429

Similares en LA Referencia