We conduct a thorough analysis on the role played by the unobservable systematic risk factor in default prediction. We find that this factor outweighs the observable systematic risk factors and can substantially improve the in-sample predictive accuracy at the firm, rating group, and aggregate levels. Thus it might be helpful to include the unobservable systematic risk factor when simulating portfolio credit losses. However, we also find that this factor only marginally improves out-of-sample model performance. Therefore, although the models we investigated all show reasonably good ability to rank order firms by default risk, accurate prediction of default rate remains challenging even when the unobservable systematic risk factor is considered.
Min Qi is the Deputy Director of the Credit Risk Analysis Division of the Office of the Comptroller of the Currency (OCC). She serves as a credit-risk modeling expert participating in examinations of national banks, reviewing models used in credit risk management and in Basel II risk-based capital for wholesale and retail credit exposures. She has provided quantitative support for international and domestic policy development, and was involved in the work of the Research Task Force of the Basel Committee to summarize vendor credit risk models. Her research and publications cover a wide range of topics on quantitative modeling and analysis in economics and finance. Her recent research investigates mortgage default and loss given default, corporate default and loss given default, exposure at default of unsecured credit cards, and indicators of systemic risk. Prior to joining the OCC, she was associate professor of economics at Kent State University. She graduated from Tsinghua University, and received her Ph.D. in economics from the Ohio State University.