Template-Type: ReDIF-Paper 1.0
Author-Name: Bauwens, L.
Author-Name-Last: Bauwens
Author-Name-First: Luc
Author-Person: pba4
Author-Name: Bos, C.S.
Author-Name-Last: Bos
Author-Name-First: Charles
Author-Person: pbo94
Author-Name: van Dijk, H.K.
Author-Name-Last: van Dijk
Author-Name-First: Herman
Author-Person: pva325
Title: Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk
Abstract: Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlo method for Bayesian analysis of models with ill-behaved posterior distributions. In order to sample efficiently from such a distribution, a location-scale transformation and a transformation to polar coordinates are used. After the transformation to polar coordinates, a Metropolis-Hastings algorithm is applied to sample directions and, conditionally on these, distances are generated by inverting the CDF. A sequential procedure is applied to update the location and scale. Tested on a set of canonical models that feature near non-identifiability, strong correlation, and bimodality, APS compares favourably with the standard Metropolis-Hastings sampler in terms of parsimony and robustness. APS is applied within a Bayesian analysis of a GARCH-mixture model which is used for the evaluation of the Value-at-Risk of the return of the Dow Jones stock index.
Creation-Date: 1999-10-21
File-URL: https://repub.eur.nl/pub/7712/1999-0824.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: TI 99-082/4
Number: TI 99-082/4
Classification-JEL: C11, C15, C63
Keywords: GARCH, Markov Chain Monte Carlo, ill-behaved posterior, polar coordinates, simulation, value-at-risk
Handle: RePEc:ems:eureir:7712