Arbeitspapier
Bayesian Forecasting of Value at Risk and Expected Shortfall using Adaptive Importance Sampling
An efficient and accurate approach is proposed for forecasting Value at Risk [VaR] and Expected Shortfall [ES] measures in a Bayesian framework. This consists of a new adaptive importance sampling method for Quantile Estimation via Rapid Mixture of t approximations [QERMit]. As a first step the optimal importance density is approximated, after which multi-step `high loss' scenarios are efficiently generated. Numerical standard errors are compared in simple illustrations and in an empirical GARCH model with Student-t errors for daily S&P 500 returns. The results indicate that the proposed QERMit approach outperforms several alternative approaches in the sense of more accurate VaR and ES estimates given the same amount of computing time, or equivalently requiring less computing time for the same numerical accuracy.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 08-092/4
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Statistical Simulation Methods: General
Forecasting Models; Simulation Methods
Criteria for Decision-Making under Risk and Uncertainty
- Thema
-
Value at Risk
Expected Shortfall
numerical accuracy
numerical standard error
importance sampling
mixture of Student-t distributions
variance reduction technique
Risikomaß
Prognoseverfahren
Bayes-Statistik
Maßzahl
Statistische Verteilung
Varianzanalyse
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Hoogerheide, Lennart
van Dijk, Herman K.
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2008
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Hoogerheide, Lennart
- van Dijk, Herman K.
- Tinbergen Institute
Entstanden
- 2008
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