Modeling credit cycle index for loan loss forecasting under macro-economic scenarios

The estimation of future loan losses is not only important for the financial institutions to effectively control the credit risk of commercial loan portfolio, but also an essential component in the capital plan submitted for regulatory approval in the annual stress testing. 1 This paper describes a methodology of modeling the credit cycle index and estimating the point-in-time (PIT) default and rating migration probabilities based on credit cycle index under macro-economic scenarios. The modeling approach is based on Credit Metrics (1996) and designed to capture the credit risk concentration at the region and industry sector levels for effective stress testing and risk management of credit portfolios. The credit cycle index 2 in the model represents the “hidden” risk factor underlying the default and credit migration with the asset correlation parameter attributed to the default clustering, while the maximum likelihood estimation (MLE) of credit cycle index and asset correlation provides a compelling explanation for the original design of risk-weight function used in the calculation of credit risk capital charge under Basel advanced capital model. The credit cycle index generated from the model captures the peak and trough of speculative grade default rates over the long-run historical period, which is consistent with the empirical evidence of credit cycle exhibited for each historical downturn during last 30 years. Furthermore, we apply least-square regression techniques to show the credit cycle index is statistically linked to the macro-economic variables, such as GDP, CPI and Unemployment Rate (UR) which are key economic indicators used by economists to analyze and explain the business cycle. The model establishes a powerful connection between credit cycle and economic cycle which enables the risk manager at large financial institutions to perform stress testing and loss forecasting to effectively control the potential credit loss of loan portfolio under various macro-economic scenarios.