An efficient sequential learning algorithm in regime-switching environments
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jaeho | - |
dc.contributor.author | Lee, Sunhyung | - |
dc.date.accessioned | 2023-08-16T07:45:54Z | - |
dc.date.available | 2023-08-16T07:45:54Z | - |
dc.date.issued | 2018-11 | - |
dc.identifier.issn | 1081-1826 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114235 | - |
dc.description.abstract | We provide a novel approach of estimating a regime-switching nonlinear and non-Gaussian state-space model based on a particle learning scheme. In particular, we extend the particle learning method in Liu, J., and M. West. 2001. "Combined Parameter and State Estimation in Simulation-Based Filtering." In Sequential Monte Carlo Methods in Practice, 197-223. Springer. by constructing a new proposal distribution for the latent regime index variable that incorporates all available information contained in the current and past observations. The Monte Carlo simulation result implies that our approach categorically outperforms a popular existing algorithm. For empirical illustration, the proposed algorithm is used to analyze the underlying dynamics of US excess stock return. © 2019 Walter de Gruyter GmbH, Berlin/Boston 2019. | - |
dc.format.extent | 14 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MIT Press | - |
dc.title | An efficient sequential learning algorithm in regime-switching environments | - |
dc.type | Article | - |
dc.publisher.location | 독일 | - |
dc.identifier.doi | 10.1515/snde-2018-0016 | - |
dc.identifier.scopusid | 2-s2.0-85056894971 | - |
dc.identifier.wosid | 000472475200001 | - |
dc.identifier.bibliographicCitation | Studies in Nonlinear Dynamics and Econometrics, v.22, no.3, pp 1 - 14 | - |
dc.citation.title | Studies in Nonlinear Dynamics and Econometrics | - |
dc.citation.volume | 22 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 14 | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Business & Economics | - |
dc.relation.journalResearchArea | Mathematical Methods In Social Sciences | - |
dc.relation.journalWebOfScienceCategory | Economics | - |
dc.relation.journalWebOfScienceCategory | Social Sciences, Mathematical Methods | - |
dc.subject.keywordPlus | VOLATILITY | - |
dc.subject.keywordPlus | RETURN | - |
dc.subject.keywordPlus | SIMULATION | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | parameter learning | - |
dc.subject.keywordAuthor | particle filters | - |
dc.subject.keywordAuthor | regime switching models | - |
dc.subject.keywordAuthor | sequential Monte Carlo estimation | - |
dc.subject.keywordAuthor | volatility models | - |
dc.identifier.url | https://www.degruyter.com/document/doi/10.1515/snde-2018-0016/html | - |
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