Censored Posterior and Predictive Likelihood in Left-Tail Prediction for Accurate Value at Risk Estimation

Lukasz Gatarek, Lennart Hoogerheide, Koen Hooning and Herman van Dijk
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Lukasz Gatarek: Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam
Lennart Hoogerheide: VU University Amsterdam
Koen Hooning: Delft University of Technology

No 13-060/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: censored likelihood; censored posterior; censored predictive likelihood; Bayesian Model Averaging; Value at Risk; Metropolis-Hastings algorithm. (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 C51 C53 C58 G17 (search for similar items in EconPapers)
Date: 2013-04-15, Revised 2014-03-06
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20130060

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