PDF Ebook Default correlation: An empirical investigation of a subprime lender
Subprime lenders include both large financial institutions that offer subprime loans as a subset of their portfolios as well as institutions that restrict their lending activities to such loans. Subprime loan portfolios generally have greater risks of default with greater credit spreads to compensate lenders for the higher risk. The subprime lending market has grown substantially in recent years. As noted by Scheessele (2002), total subprime lending grew from $90 billion in 1996 to $173 billion in 2001. In 2001, subprime loans represented a considerable 8.3% of the overall mortgage market. Given the growth of this market sector combined with the higher risks relative to other mortgage portfolios, understanding the credit risk of these portfolios is of critical importance both to the lenders themselves and to the regulators of these lenders. This paper focuses on one aspect of credit risk analysis; i.e., the importance of default correlation in measuring credit risk in subprime portfolios.
Important advances are being made in the measurement and modeling of credit risk in lending portfolios. Given inherent difficulties in necessary data acquisition, fewer advances have been made with respect to retail credit portfolios in general. This paper represents the first empirical investigation of credit risk within a subprime loan portfolio, and we limit our focus to only one aspect of credit risk. Using a proprietary dataset of subprime loans as a single case study, we are able to provide additional insight into the importance of default correlation in subprime loan portfolios. Subprime lenders have historically used specified limits to manage credit risk exposure; such as, a dollar limit established by borrower or a dollar limit established by geographic region. In fact, Carey (2000) observes that the monitoring of established credit limits has long been a part of examinations in the United States. Our results suggest that subprime lenders would be well served to develop more sophisticated credit measurement techniques. Despite the smaller exposures from subprime loans as compared with commercial portfolios, we find substantially larger default correlations than reported for commercial bonds and loans. Gordy (2000) finds that capital requirements based on industry credit risk models vary considerably based on average default correlations in the portfolio. Thus, our findings are relevant to both regulators who evaluate internal risk models and financial institutions developing models to manage risks. Despite the fact that we analyze the portfolio of a single lender, it strongly supports the investment in further understanding default correlation in subprime portfolios.
Default correlation is a measure of the dependence among risks. Along with default rates and recovery rates, it is a necessary input in the estimation of the value of the portfolio at risk due to credit. In general, the concept of default correlation incorporates the fact that systemic events cause the default event to cluster. Coincident movements in default among borrowers may be triggered by common, underlying factors. Within the context of retail portfolios, systemic events might include macroeconomic events such as changes in the rate of unemployment or geographically specific events. Default correlation is defined by Nagpal and Bahar (2001) as the relationship between default probabilities and joint default probabilities. They note that historical rates of default support the idea that credit events are correlated. This correlation is a critical factor in the estimation of the tails of the overall credit loss distributions. Thus, failure to recognize the impact of shocks to the portfolio through default correlation will ultimately underestimate the measures of risk and economic capital required to manage that risk.
In contrast with other residential loan portfolios in which one would anticipate that default correlation can be very low, understanding default correlation is critical in the lower credit quality portfolios of subprime lenders. Several authors have documented the relationship between the initial credit quality of the portfolio and default correlation in commercial portfolios. Generally, as credit quality declines, the importance of default correlation increases. For example, Zhou (1997) shows implied default correlations based on Z-values that are almost zero for highly rated firms but substantial for lowly rated firms even over short time horizons. Using corporate bond and loan portfolios, Lucas et al. (2001) provides numerical results showing that for a given correlation, a higher portfolio quality lowers extreme credit loss quantiles. Similarly, Loffler (2003) finds that correlation uncertainty is a more significant factor for B-rated portfolios as compared with BBB-rated portfolios for uncertainty in the 1% VaR. Although these studies deal with commercial loan and bond portfolios, the management of a subprime loan portfolio is analogous to the management of a non-investment grade bond portfolio. Our results are consistent with these previous studies. The significance of default correlation increases as the internal ratings of the lender decline. Thus, it is likely that ignoring default correlation in the development of credit risk models for subprime portfolios would lead to considerable model risk.
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PDF Ebook Default correlation: An empirical investigation of a subprime lender
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