Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87166
Authors: 
Gatarek, Lukasz
Hoogerheide, Lennart
Hooning, Koen
van Dijk, Herman K.
Year of Publication: 
2013
Series/Report no.: 
Tinbergen Institute Discussion Paper 13-060/III
Abstract: 
Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a posterior in which the likelihood is replaced by the censored likelihood; and the censored predictive likelihood, which is used for Bayesian Model Averaging. We perform extensive experiments involving simulated and empirical data. Our results show the ability of these new approaches to outperform the standard posterior and traditional Bayesian Model Averaging techniques in applications of Value-at-Risk prediction in GARCH models.
Subjects: 
censored likelihood
censored posterior
censored predictive likelihood
Bayesian Model Averaging
Value at Risk
Metropolis-Hastings algorithm.
JEL: 
C11
C15
C22
C51
C53
C58
G17
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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