Multistate Intensity Model with AR-GARCH Random Effect for Corporate Credit Rating Transition Analysis

• Other Authors: Li, Zhi (authoraut)
• Format: Others
• Language: English; English
• Published: Florida State University
• Subjects: Statistics; Probabilities
• Online Access: http://purl.flvc.org/fsu/fd/FSU_migr_etd-1426

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. === A Dissertation Submitted to the Department of Statistics in Partial FulfiLlment of the Requirements for the Degree of Doctor of Philosophy. === Fall Semester, 2010. === October 19, 2010. === Rating Transition Analysis === Includes bibliographical references. === Xufeng Niu, Professor Co-Directing Dissertation; Fred Huffer, Professor Co-Directing Dissertation; Alec Kercheval, Outside Committee Member; Wei Wu, Committee Member.