The extended Liu and West filter: Parameter learning in Markov switching stochastic volatility models
| dc.contributor.author | Rios, Maria Paula | |
| dc.contributor.author | HEDIBERT FREITAS LOPES | |
| dc.coverage.cidade | Nova York | pt_BR |
| dc.coverage.pais | Estados Unidos | pt_BR |
| dc.creator | Rios, Maria Paula | |
| dc.date.accessioned | 2022-12-15T20:06:58Z | |
| dc.date.available | 2022-12-15T20:06:58Z | |
| dc.date.issued | 2013 | |
| dc.description.other | Book cover State-Space Models pp 23–61Cite as The Extended Liu and West Filter: Parameter Learning in Markov Switching Stochastic Volatility Models Maria Paula Rios & Hedibert Freitas Lopes Chapter First Online: 01 January 2013 3249 Accesses 8 Citations Part of the Statistics and Econometrics for Finance book series (SEFF,volume 1) Abstract We explore kernel smoothing and conditional sufficient statistics extensions of the Pitt and Shephard (1999) auxiliary particle and Gordon, Salmond and Smith (1993) bootstrap filters. Using simulated data following Markov Switching Stochastic Volatility models, we show that the LW particle filter degenerates and has the largest Monte Carlo error, while the auxiliary particle filter (APF) + sufficient statistics (SS) outperforms. Our APF + SS filter takes advantage of recursive sufficient statistics that are sequentially tracked and whose behavior resembles that of a latent state with conditionally deterministic updates. The performance of the APF + SS filter is also assessed when exploring its sequential estimation of real data examples. | pt_BR |
| dc.format.extent | p. 23-61 | pt_BR |
| dc.format.medium | Físico | pt_BR |
| dc.identifier.uri | https://repositorio.insper.edu.br/handle/11224/4978 | |
| dc.language.iso | Inglês | pt_BR |
| dc.publisher | Springer | pt_BR |
| dc.relation.ispartofseries | Statistics and Econometrics for Finance | pt_BR |
| dc.relation.isreferencedby | State-Space Models | 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 | Mean Square Error | pt_BR |
| dc.subject.keywords | Confidence Band | pt_BR |
| dc.subject.keywords | Stochastic Volatility Model | pt_BR |
| dc.subject.keywords | Sequential Monte Carlo | pt_BR |
| dc.subject.keywords | Kernel Smoothing | pt_BR |
| dc.title | The extended Liu and West filter: Parameter learning in Markov switching stochastic volatility models | pt_BR |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| local.identifier.sourceUri | https://link.springer.com/chapter/10.1007/978-1-4614-7789-1_2 | |
| local.subject.cnpq | Ciências Sociais Aplicadas | pt_BR |
| local.type | Capítulo de Livro | pt_BR |
| relation.isAuthorOfPublication | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca | |
| relation.isAuthorOfPublication.latestForDiscovery | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca |
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