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

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 Caarel ( 1999). The potential of the new idea is illustrated with some toy examples and a real application to Bayesian inference for credit risk estimation.

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Year of publication:	2008-04
Authors:	Paolo, Tenconi
Institutions:	Facoltà di Economia, Università degli Studi dell'Insubria
Subject:	Markov chain Monte Carlo \| Metropolis-Hastings algorithm \| Variance reduction \| Zero-Variance principle

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Extent:	application/pdf
Series:	Economics and Quantitative Methods.
Type of publication:	Book / Working Paper
Language:	English
Notes:	23 pages
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005771909