Predicting the Credit Cycle with an Autoregressive Model
by Steffi Höse of Technische Universität Dresden, and
August 2, 2005
Abstract: Credit default events show cross sectional as well as serial correlation. While the latter is often neglected by current credit risk models, this work incorporates both types of dependence. A Bernoulli mixture model is considered, where in each rating grade the probit of the stochastic Bernoulli parameter follows an autoregressive stationary process with exogenous variables. The model parameters are estimated for a large retail portfolio. Exemplarily, prediction intervals of the default probabilities of the best and worst nondefault rating grade are given and predicted credit portfolio loss distributions are plotted in contrast to the unconditional loss distributions.
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