Bayesian semiparametric Markov switching stochastic volatility model
| dc.contributor.author | Virbickaité, Audrone | |
| dc.contributor.author | HEDIBERT FREITAS LOPES | |
| dc.coverage.cidade | Não informado | pt_BR |
| dc.coverage.pais | Não Informado | pt_BR |
| dc.creator | Virbickaité, Audrone | |
| dc.date.accessioned | 2022-08-23T19:48:48Z | |
| dc.date.available | 2022-08-23T19:48:48Z | |
| dc.date.issued | 2019 | |
| dc.description.other | This paper proposes a novel Bayesian semiparametric stochastic volatility model with Markov switching regimes for modeling the dynamics of the financial returns. The distribution of the error term of the returns is modeled as an infinite mixture of Normals; meanwhile, the intercept of the volatility equation is allowed to switch between two regimes. The proposed model is estimated using a novel sequential Monte Carlo method called particle learning that is especially well suited for state-space models. The model is tested on simulated data and, using real financial times series, compared to a model without the Markov switching regimes. The results show that including a Markov switching specification provides higher predictive power for the entire distribution, as well as in the tails of the distribution. Finally, the estimate of the persistence parameter decreases significantly, a finding consistent with previous empirical studies. | pt_BR |
| dc.format.extent | p. 978-997 | pt_BR |
| dc.format.medium | Digital | pt_BR |
| dc.identifier.doi | doi.org/10.1002/asmb.2434 | pt_BR |
| dc.identifier.issue | 4 | pt_BR |
| dc.identifier.uri | https://repositorio.insper.edu.br/handle/11224/4098 | |
| dc.identifier.volume | 35 | pt_BR |
| dc.language.iso | Inglês | pt_BR |
| dc.publisher | Não informado | pt_BR |
| dc.relation.isbound | Produção vinculada ao Núcleo de Ciências de Dados e Decisão | |
| dc.relation.ispartof | Applied Stochastic Models in Business and Industry | pt_BR |
| dc.rights.license | O 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 EDITOR. | pt_BR |
| dc.subject.keywords | Bayes factor | pt_BR |
| dc.subject.keywords | Dirichlet process mixture | pt_BR |
| dc.subject.keywords | Particle learning | pt_BR |
| dc.subject.keywords | Sequential Monte Carlo | pt_BR |
| dc.title | Bayesian semiparametric Markov switching stochastic volatility model | pt_BR |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| local.identifier.sourceUri | https://onlinelibrary.wiley.com/doi/10.1002/asmb.2434 | |
| local.subject.cnpq | Ciências Sociais Aplicadas | pt_BR |
| local.type | Artigo Científico | pt_BR |
| relation.isAuthorOfPublication | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca | |
| relation.isAuthorOfPublication.latestForDiscovery | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca |
Arquivos
Pacote original
1 - 2 de 2
N/D
- Nome:
- R_Artigo_2019_Bayesian semiparametric Markov_TC.pdf
- Tamanho:
- 1.45 MB
- Formato:
- Adobe Portable Document Format
- Descrição:
- R_Artigo_2019_Bayesian semiparametric Markov_TC
- Nome:
- Acesso_Primeira Pagina_Bayesian semiparametric Markov switching stochastic volatility model.pdf
- Tamanho:
- 47.97 KB
- Formato:
- Adobe Portable Document Format
Licença do pacote
1 - 1 de 1
N/D
- Nome:
- license.txt
- Tamanho:
- 282 B
- Formato:
- Item-specific license agreed upon to submission
- Descrição: