Modelling Portfolio Defaults Using Hidden Markov Models with Covariates
AbstractWe extend the hidden Markov Model for defaults of Crowder et al. (2005, Quantitative Finance 5, 27--34) 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 structure 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 general business cycles. Copyright Royal Economic Society 2008
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Bibliographic InfoArticle provided by Royal Economic Society in its journal Econometrics Journal.
Volume (Year): 11 (2008)
Issue (Month): 1 (03)
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Other versions of this item:
- Konrad Banachewicz & Aad van der Vaart & Andr� Lucas, 2006. "Modeling Portfolio Defaults using Hidden Markov Models with Covariates," Tinbergen Institute Discussion Papers 06-094/2, Tinbergen Institute.
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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