Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimation
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/20383 |
Resumo: | The statistical properties of a general family of asymmetric stochastic volatility (A-SV) models which capture the leverage effect in financial returns are derived providing analytical expressions of moments and autocorrelations of power-transformed absolute returns. The parameters of the A-SV model are estimated by a particle filter-based simulated maximum likelihood estimator and Monte Carlo simulations are carried out to validate it. It is shown empirically that standard SV models may significantly underestimate the value-at-risk of weekly S&P 500 returns at dates following negative returns and overestimate it after positive returns. By contrast, the general specification proposed provide reliable forecasts at all dates. Furthermore, based on daily S&P 500 returns, it is shown that the most adequate specification of the asymmetry can change over time. |
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Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimationParticle filteringLeverage effectSV modelsValue-at-riskThe statistical properties of a general family of asymmetric stochastic volatility (A-SV) models which capture the leverage effect in financial returns are derived providing analytical expressions of moments and autocorrelations of power-transformed absolute returns. The parameters of the A-SV model are estimated by a particle filter-based simulated maximum likelihood estimator and Monte Carlo simulations are carried out to validate it. It is shown empirically that standard SV models may significantly underestimate the value-at-risk of weekly S&P 500 returns at dates following negative returns and overestimate it after positive returns. By contrast, the general specification proposed provide reliable forecasts at all dates. Furthermore, based on daily S&P 500 returns, it is shown that the most adequate specification of the asymmetry can change over time.Elsevier2020-04-20T11:16:26Z2020-01-01T00:00:00Z20202020-04-20T12:15:37Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/20383eng2452-306210.1016/j.ecosta.2019.08.002Mao, X.Czellar, V.Ruiz, E.Veiga, H.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:RCAAP2023-11-09T17:39:07Zoai:repositorio.iscte-iul.pt:10071/20383Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:17:58.194141Repositório 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 |
Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimation |
title |
Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimation |
spellingShingle |
Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimation Mao, X. Particle filtering Leverage effect SV models Value-at-risk |
title_short |
Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimation |
title_full |
Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimation |
title_fullStr |
Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimation |
title_full_unstemmed |
Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimation |
title_sort |
Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimation |
author |
Mao, X. |
author_facet |
Mao, X. Czellar, V. Ruiz, E. Veiga, H. |
author_role |
author |
author2 |
Czellar, V. Ruiz, E. Veiga, H. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Mao, X. Czellar, V. Ruiz, E. Veiga, H. |
dc.subject.por.fl_str_mv |
Particle filtering Leverage effect SV models Value-at-risk |
topic |
Particle filtering Leverage effect SV models Value-at-risk |
description |
The statistical properties of a general family of asymmetric stochastic volatility (A-SV) models which capture the leverage effect in financial returns are derived providing analytical expressions of moments and autocorrelations of power-transformed absolute returns. The parameters of the A-SV model are estimated by a particle filter-based simulated maximum likelihood estimator and Monte Carlo simulations are carried out to validate it. It is shown empirically that standard SV models may significantly underestimate the value-at-risk of weekly S&P 500 returns at dates following negative returns and overestimate it after positive returns. By contrast, the general specification proposed provide reliable forecasts at all dates. Furthermore, based on daily S&P 500 returns, it is shown that the most adequate specification of the asymmetry can change over time. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-04-20T11:16:26Z 2020-01-01T00:00:00Z 2020 2020-04-20T12:15:37Z |
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/10071/20383 |
url |
http://hdl.handle.net/10071/20383 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2452-3062 10.1016/j.ecosta.2019.08.002 |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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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1799134739035062272 |