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Predicting Credit Default Probabilities Using Bayesian Statistics and Monte Carlo Simulations

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  • Dominic Joseph

Abstract

Banks and financial institutions all over the world manage portfolios containing tens of thousands of customers. Not all customers are high credit-worthy, and many possess varying degrees of risk to the Bank or financial institutions that lend money to these customers. Hence assessment of credit risk is paramount in the field of credit risk management. This paper discusses the use of Bayesian principles and simulation-techniques to estimate and calibrate the default probability of credit ratings. The methodology is a two-phase approach where, in the first phase, a posterior density of default rate parameter is estimated based the default history data. In the second phase of the approach, an estimate of true default rate parameter is obtained through simulations

Suggested Citation

  • Dominic Joseph, 2021. "Predicting Credit Default Probabilities Using Bayesian Statistics and Monte Carlo Simulations," Papers 2108.03389, arXiv.org, revised Sep 2021.
    • Handle: RePEc:arx:papers:2108.03389
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    References listed on IDEAS

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    1. Dirk Tasche, 2012. "The art of probability-of-default curve calibration," Papers 1212.3716, arXiv.org, revised Nov 2013.
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