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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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| Publié dans: | PLoS One |
|---|---|
| Auteurs principaux: | , , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
Public Library of Science
2018
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6014648/ https://ncbi.nlm.nih.gov/pubmed/29933383 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0198753 |
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