Bayesian estimate of credit risk via MCMC with delayed rejection

Mira Antonietta () and Tenconi Paolo ()
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Mira Antonietta: Department of Economics, University of Insubria, Italy
Tenconi Paolo: University of Switzerland

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

Abstract: We develop a Bayesian hierarchical logistic regression model to predict the credit risk of companiers classified in different sectors. Explanatory variables derived by experts from balance-sheets are included. Markov chain Monte Carlo (MCMC) methods are used to estimate the proposed model. In particular we show how the delaying rejection strategy outperforms the standart Metrtopolis-Hastings algorithm in terms of asymptotic efficiency of the resulting estimates. The advantages of our over others proposed in the literature are discussed and tested via cross-validation procedures.

Keywords: Asymptotic efficiency of MCMC estimates; Creadit risk; Default risk; Delayng rejection; Hierarchical logistic regression; Metropolis-Hastings algorithm (search for similar items in EconPapers)
Pages: 17 pages
Date: 2003-10
New Economics Papers: this item is included in nep-ecm, nep-fin and nep-rmg
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ins:quaeco:qf0315

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