Abstract
We develop a Bayesian hierarchical logistic regression model to predict the credit risk of companies 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 standard Metropolis-Hastings algorithm in terms of asymptotic efficiency of the resulting estimates. The advantages of our model over others proposed in the literature are discussed and tested via cross-validation procedures.
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© 2004 Springer Basel AG
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Mira, A., Tenconi, P. (2004). Bayesian Estimate of Default Probabilities via MCMC with Delayed Rejection. In: Dalang, R.C., Dozzi, M., Russo, F. (eds) Seminar on Stochastic Analysis, Random Fields and Applications IV. Progress in Probability, vol 58. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-7943-9_17
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DOI: https://doi.org/10.1007/978-3-0348-7943-9_17
Publisher Name: Birkhäuser, Basel
Print ISBN: 978-3-0348-9630-6
Online ISBN: 978-3-0348-7943-9
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