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Adaptive Polar Sampling With An Application To A Bayes Measure Of Value-At-Risk

  • K. Van Dijk

    (Erasmus University of Rotterdam)

  • Luc Bauwens

    (CORE, Belgium)

  • Charles Bos

    (Erasmus University Rotterdam)

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 aGARCH-mixture model which is used for the evaluation of the Value-at-Risk of the return of the Dow Jones stock index.

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 145.

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Date of creation: 05 Jul 2000
Date of revision:
Handle: RePEc:sce:scecf0:145
Contact details of provider: Postal: CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain
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