Modelling Portfolio Defaults Using Hidden Markov Models with Covariates

Abstract

We extend the Hidden Markov Model for defaults of Crowder, Davis, and Giampieri (2005) to include covariates. The covariates enhance the prediction of transition probabilities from high to low default regimes. To estimate the model, we extend the EM estimating equations to account for the time varying nature of the conditional likelihoods due to sample attrition and extension. Using empirical U.S. default data, we find that GDP growth, the term struc-ture of interest rates and stock market returns impact the state transition probabilities. The impact, however, is not uniform across industries. We only find a weak correspondence between industry credit cycle dynamics and gen-eral business cycles. Key words: defaults; Markov switching; default regimes. ∗ We thank Standard & Poor’s for making the CreditPro 7 database available for this research.

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