Adaptive polar sampling with an application to a bayes measure of value-at-risk
(1999)
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- Authors
Bauwens, Luc 
- 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, location-scale transformation and a transformation to polar coordinates are used. After the transformation to polar coordinates, a MetropolisHastings 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.
- Affiliations
Citations
Bauwens, L., Bos, C. S., & Van Dijk, H. (1999). Adaptive polar sampling with an application to a bayes measure of value-at-risk (CORE Discussion Papers 1999/57). https://hdl.handle.net/2078.5/128157