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Credit Rating Dynamics in the Presence of Unknown Structural Breaks

by Haipeng Xing of the State University of New York at Stony Brook,
Ning Sun of the State University of New York at Stony Brook, and
Ying Chen of the MEAG New York Corp.

May 5, 2011

Abstract: In many credit risk and pricing applications, credit transition matrix is modeled by a constant transition probability or generator matrix for Markov processes. Based on empirical evidence, we model rating transition processes as piecewise homogeneous Markov chains with unobserved structural breaks. The proposed model provides explicit formulas for the posterior distribution of the time-varying rating transition generator matrices, the probability of structural break at each period and prediction of transition matrices in the presence of possible structural breaks. Estimating the model by credit rating history, we show that the structural break in rating transitions can be captured by the proposed model. We also show that structural breaks in rating dynamics are different for different industries. We then compare the prediction performance of the proposed and time-homogeneous Markov chain models.

JEL Classification: C13, C41, G12, G20.

Keywords: Credit risk, Hidden Markov model, Structural break

Published in: Journal of Banking & Finance, Vol. 36, No. 1, (January 2012), pp. 78-89..

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