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Bayesian forecasting of value at risk and expected shortfall using adaptive importance sampling

Hoogerheide, Lennart; Dijk, Herman K. van - 2008

in a Bayesian framework. This consists of a new adaptive importance sampling method for Quantile Estimation via Rapid …

Persistent link: https://www.econbiz.de/10011377096

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

Gatarek, Lukasz T. - 2014

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...

Persistent link: https://www.econbiz.de/10013064150

Censored posterior and predictive likelihood in Bayesian left-tail prediction for accurate value at risk estimation

Gatarek, Lukasz; Hoogerheide, Lennart; Hooning, Koen; … - 2013

Persistent link: https://www.econbiz.de/10009756308

A class of adaptive importance sampling weighted EM algorithms for efficient and robust posterior and predictive simulation

Hoogerheide, Lennart; Opschoor, Anne; Dijk, Herman K. van - 2012

Persistent link: https://www.econbiz.de/10009722688

Partially censored posterior for robust and efficient risk evaluation

Borowska, Agnieszka; Hoogerheide, Lennart; Koopman, Siem Jan - 2019

simulate model parameters from the Partially Censored Posterior, and PCP-QERMit, an Importance Sampling method that is …

Persistent link: https://www.econbiz.de/10012057160

A Class of Adaptive Importance Sampling Weighted EM Algorithms for Efficient and Robust Posterior and Predictive Simulation

Hoogerheide, Lennart F. - 2012

A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed … between target and mixture is minimized. We label this approach <I>Mixture of t by Importance Sampling and Expectation … Importance Sampling (IS) or the Metropolis-Hastings (MH) method. We also introduce three extensions of the basic MitISEM approach …

Persistent link: https://www.econbiz.de/10013108670

Prediction of financial downside-risk with heavy-tailed conditional distributions

Mittnik, Stefan; Paolella, Marc S. - 2003

The use of GARCH models with stable Paretian innovations in financial modeling has been recently suggested in the literature. This class of processes is attractive because it allows for conditional skewness and leptokurtosis of financial returns without ruling out normality. This contribution...

Persistent link: https://www.econbiz.de/10009765347

Bayesian predictive distributions of oil returns using mixed data sampling volatility models

Virbickaite, Audrone; Nguyen, Hoang; Minh-Ngoc Tran - 2023

Stochastic Volatility (SV), along with Mixed Data Sampling (MIDAS) regressions, which enable us to incorporate the impacts of …

Persistent link: https://www.econbiz.de/10014252427

A class of adaptive importance sampling weighted EM algorithms for efficient and robust posterior and predictive simulation

Hoogerheide, Lennart; Opschoor, Anne; Dijk, Herman K. van - In: Journal of econometrics 171 (2012) 2, pp. 101-120

Persistent link: https://www.econbiz.de/10009691174

Conditional density forecasting : a tempered importance sampling approach

Montes-Galdón, Carlos; Paredes, Joan; Wolf, Elias - 2022

proposed algorithm, which is based on tempered importance sampling, adapts the model-based density forecasts to target … distributions the researcher has access to. As an example, this paper shows how to implement the algorithm by conditioning the …

Persistent link: https://www.econbiz.de/10013463266