Type of Document	Dissertation
Author	Li, Zhi
URN	etd-11082010-002718
Title	Multistate Intensity Model With AR-GARCH Random Effect For Corporate Credit Rating Transition Analysis
Degree	Doctor of Philosophy
Department	Statistics, Department of
Advisory Committee	Advisor Name Title Xufeng Niu Committee Chair Fred Huffer Committee Co-Chair Wei Wu Committee Member Alec Kercheval University Representative
Keywords	Rating Transition Analysis
Date of Defense	2010-10-19
Availability	unrestricted
Abstract This thesis presents a stochastic process and time series study on corporate credit rating and market implied rating transitions. By extending an existing model, this paper incorporates the generalized autoregressive conditional heteroscedastic (GARCH) random effects to capture volatility changes in the instantaneous transition rates. The GARCH model is a crucial part in financial research since its ability to model volatility changes gives the market practitioners flexibility to build more accurate models on high frequency financial data. The corporate rating transition modeling was historically dealing with low frequency data which did not have the need to specify the volatility. However, the newly published Moody’s market implied ratings are exhibiting much higher transition frequencies. Therefore, we feel that it is necessary to capture the volatility component and make extensions to existing models to reflect this fact. The theoretical model specification and estimation details are discussed thoroughly in this dissertation. The performance of our models is studied on several simulated data sets and compared to the original model. Finally, the models are applied to both Moody’s issuer rating and market implied rating transition data as an application.
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