Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/85730
Authors: 
Bauwens, Luc
Bos, Charles S.
van Dijk, Herman K.
Year of Publication: 
1999
Series/Report no.: 
Tinbergen Institute Discussion Paper 99-082/4
Abstract: 
Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlomethod for Bayesian analysis of models with ill-behaved posteriordistributions. In order to sample efficiently from such a distribution,a location-scale transformation and a transformation to polarcoordinates are used. After the transformation to polar coordinates, aMetropolis-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 nearnon-identifiability, strong correlation, and bimodality, APS comparesfavourably with the standard Metropolis-Hastings sampler in terms ofparsimony and robustness. APS is applied within a Bayesian analysisof a GARCH-mixture model which is used for the evaluation of theValue-at-Risk of the return of the Dow Jones stock index.
Subjects: 
Markov chain Monte Carlo
simulation
polar coordinates
GARCH
ill-behaved posterior
Value-at-Risk
JEL: 
C11
C15
C63
Document Type: 
Working Paper

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