Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student’s-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over ti...

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Detalles Bibliográficos
Autores principales: Sampid, Marius Galabe, Hasim, Haslifah M., Dai, Hongsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6014648/
https://www.ncbi.nlm.nih.gov/pubmed/29933383
http://dx.doi.org/10.1371/journal.pone.0198753

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6014648/
https://www.ncbi.nlm.nih.gov/pubmed/29933383
http://dx.doi.org/10.1371/journal.pone.0198753

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