In this article, we execute the three Fed scenarios on a sample of non-agency RMBS to demonstrate how to apply one approach to both the securitized tranches and the underlying residential mortgage portfolios collateralizing the securitizations.
Calculating stressed losses on structured finance portfolios to comply with DFAST or CCAR can be challenging for many financial institutions. Consistency between the underlying asset and whole loan portfolio analyses is critical, and yet few stress testing solutions in the marketplace offer a singular solution for all asset classes.
When reviewing potential losses under the Federal Reserve (Fed) macroeconomic scenarios, it helps to have a sense of what to expect. Table 1 demonstrates the average discounted tranche loss across the three Fed scenarios through the life of the transaction. Table 2 shows the average projected loss on the underlying mortgages from the Fed’s CCAR report, as well as the results from Moody’s Analytics.
The numbers used in this exercise are based on a sample portfolio of 583 US non-agency residential mortgage backed securities (RMBS) tranches. In order to run the three Fed scenarios, this methodology leverages a top-down modeling framework, which enables macroeconomic assumptions from Moody’s Analytics economists to automatically filter down into loan-level credit model projections. These loan-level cash flows for the RMBS transaction are then allocated to the tranches based on the legal structure (the waterfall).
Based on these figures, the findings indicate that for the Fed scenarios, the senior RMBS notes would lose, on average, about 35%, while the mezzanine notes would lose around 70%. The incremental loss between the Baseline and Severe scenarios is roughly 6% to 8% on average for each of the Senior and Mezzanine tranches, respectively. We found that the underlying mortgage pools would suffer around 7% loss on currently performing mortgages. Given that RMBS pools tend to hold primarily first-liens, this estimate is not much different than the Fed’s projections. As we would expect, a 7% loss on the underlying collateral translates to a lower loss for the senior notes relative to the mezzanine notes.
While averages can be useful for thinking about a portfolio, individual tranche results may diverge significantly. Table 3 breaks down the projected tranche losses into quartiles.
The variance in tranche loss is non-trivial. While the average senior note loss is about 35%, around a quarter of the senior notes in the sample lose more than 50% and another quarter lose less than around 15%. As expected, the mezzanine notes are even more volatile. Even under the Baseline Fed scenario, a quarter of the mezzanines lose almost 100%, while another quarter lose less than 35% – a smaller loss than the overall average for the senior notes. Additionally, for all three scenarios and for all tranche categories, the maximum loss is 100% and the minimum is 0%. Anything can happen given the specific performance of each deal.
Figure 1 reinforces the high variance by showing the distribution of projected tranche losses by seniority. Both senior and mezzanine notes have bar-belled distributions but the senior notes have a higher concentration in the lower losses.
Table 4 breaks down the sample portfolio results by asset class, which highlights the variance in the individual tranche results. The overall average loss for senior notes is around 35% but there’s a large dispersion based on asset class – under the Baseline Fed scenario, the average Prime tranche loss is 28% while the average Option ARM loss is 70%. It is interesting to note, too, that Subprime losses in the sample are actually among the lowest for the senior tranches. Despite having lower-quality underlying collateral, in these cases the senior notes tend to have more effective subordination due to safer deal structure to offset the riskier assets and higher current credit support.
Leading economist; recognized authority and commentator on personal finance and credit, U.S. housing, economic trends and policy implications; innovator in econometric and credit modeling techniques.
Douglas W. Dwyer leads Corporate Credit Research in Predictive Analytics. This group produces credit risk metrics of small businesses, medium sized enterprises, large corporations, financial institutions, and sovereigns worldwide. The group’s models are used by banks, asset managers, insurance companies, accounting firms and corporations to measure name specific credit risk for a wide variety of purposes. We measure credit risk using information drawn from financial statements, regulatory filings, security prices, derivative contracts, behavioral and payment information. For each asset class, the methodology is developed based on the available information for each obligor. <br><br> Current projects include developing a climate adjusted probability of default and incorporating ESG factors into credit analytics. We also are developing an approach to produces comparable PDs across asset classes that opportunistically uses whatever information is available. <br><br> Prior to working at Moody’s Analytics, Dr. Dwyer was a Principal at William M. Mercer, Inc., in their Human Capital Strategy practice. Dr. Dwyer earned a Ph.D. in Economics at Columbia University and a B.A. in Economics from Oberlin College.
Dr. Juan M. Licari is a managing director at Moody's Analytics. Juan and team-members are responsible for the research and analytics that enable our quantitative solutions. The team helps our customers solve complex business problems; adding value through data and analytics.
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