Zero variance in Markov chain Monte Carlo with an application to credit risk estimation

Tenconi Paolo
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Tenconi Paolo: Department of Economics, University of Insubria, Italy

Economics and Quantitative Methods from Department of Economics, University of Insubria

Abstract: We propose a general purpose variance reduction technique for Markov Chain Monte Carlo estimators based on the Zero-Variance principle introduced in the physics literature by Assaraf and Ca arel ( 1999). The potential of the new idea is illustrated with some toy examples and a real application to Bayesian inference for credit risk estimation.

Keywords: Markov chain Monte Carlo; Metropolis-Hastings algorithm; Variance reduction; Zero-Variance principle. (search for similar items in EconPapers)
Pages: 23 pages
Date: 2008-04
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Persistent link: https://EconPapers.repec.org/RePEc:ins:quaeco:qf0804

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