Understanding the Impact of Federal Reserve Emergency Rate Cuts
Disruptions to the U.S. and global economy brought about by COVID-19 have prompted the Federal Reserve to take a number of emergency measures. These include twice cutting the federal funds rate (to near zero), resuming its purchase of securities, and temporarily relaxing regulatory capital and liquidity requirements (among several other things).
Although the Fed’s actions take many forms, few things capture investors’ attention in the way emergency rate cuts do. Predicting how financial markets will respond to these cuts is a complicated undertaking. To help investors analyze how these events have affected markets historically, RiskSpan has developed a tool to help investors visualize how various market indices, commodities, currencies and bond yields have reacted to emergency Fed rate cuts in the wake of various market shocks.
Analyzing events in this way enables investors to more effectively manage their portfolio risk by monitoring market–moving events and identifying response patterns. We analyze a range of past market events to formulate scenarios for RiskSpan’s RiskDynamics market risk service.
Every crisis is unique, of course. But the Fed’s interest rate cuts this month are specifically reminiscent of seven actions it has taken in response to past economic threats, including the Russian Ruble crisis (2014), the bursting of the dot-com bubble (2000), the September 11th attacks (2001), and the subprime mortgage/Lehman Brothers collapse (2008).
The chart below compares the response of the S&P 500 to the Lehman collapse and COVID-19 and how long it takes the ensuing Fed rate cut to affect the market. The similarity in the shape of these two curves is quite striking. It also reflects the time required for Congress to pass stimulus following Fed action.
The tool displays the performance of several markets across three asset classes in response to each of the seven Fed cuts. In this version we have included stocks, rates and commodities. The two interactive charts specifically help to visualize the following:
- Performance of asset classes from 20 days before through 60 days following each rate cut.
- Performance indexed to the event date—helping to illustrate market conditions leading up to the rate cut and its subsequent impact.
- Daily returns enabling a cross-sector, cross-market comparisons to each rate cut.
Additional patterns also emerge when looking at how markets have responded to these seven prior cuts:
- Equity market collapses tend to stall, but the recovery (if any) is slow.
- The volatility index stabilizes, but it takes time to mean revert.
- Treasury bonds generally perform better than other asset classes. Long–dated bonds don’t perform as well.
- Crude oil continues to sell off in most cases.
We are continually expanding the list of asset classes and events covered by the tool. Our data science team is also working some interesting analytics for publication.
We welcome your feedback and requests for additional analysis. Please contact us to discuss further.
¹ In 2011-12, the market saw significant differences in buyout behavior, for example Bank of America was slow to buy out delinquent loans.
² On Bloomberg, the delinquency states 90 days onward are compressed into a single 90+ state.
Visualizing a CMBS Portfolio’s Exposure to COVID-19
he economic impact of the Coronavirus outbreak is all but certain to be felt by CMBS investors. The only real uncertainty surrounds when missed rent payments will begin, what industries are likely to feel them most acutely, and—more to the point—how your portfolio aligns with these eventualities.
The dashboard below—created using RS Edge and Tableau—displays a stylized example compiling small random excerpts from several CMBS portfolios. While business disruptions have not (yet) lasted long enough to be reflected in CMBS default rates, visualizing portfolios in this way provides a powerful tool for zeroing in on where problems are most likely to emerge.
The maps at the top of the dashboard juxtapose the portfolio’s geographic concentration with states where COVID-19 prevalence is highest. Investors are able to drill down not only into individual states but into individual NAICS-defined industries that the loans in their deals cover.
At each level of analysis (overall, by state, or by industry) the dashboard not only reports total exposure in UPB but also important risk metrics around the portfolio’s DSCR and LTV, thus enabling investors to quickly visualize how much cushion the underlying loans have to absorb missed rent payments before the deals begin to experience losses.
The real value of visualizations like these, of course, is the limitlessness of their flexibility and their applicability to any market sector.
We sincerely desire to be helpful during these unprecedented market conditions. Our teams are actively helping clients to manage through them. Whether you are looking for historical context, market analysis or just a conversation with folks who have been through several market cycles, we are here to provide support. Please contact us to talk about what we can do for you.
Managing Coronavirus-Related Risks in Aircraft Lease ABS
Last week, UK airline Flybe grounded its planes, stranded passengers and filed for bankruptcy protection, as the struggling carrier was buffeted by a Coronavirus-related slowdown in demand. Flybe’s demise is a trenchant example of the implications of Coronavirus for investors in the aircraft sector, including aircraft lease ABS investors whose cash flow depends on continued lease payments from various global carriers. Of course, the impacts of Coronavirus will vary, with some countries, servicers, credit-rating sectors and deal structures worse off than others. Using RS Edge, aircraft lease ABS investors can drill down into collateral to see country, carrier and aircraft exposures, stress test deals and learn potential fault lines for deals as Coronavirus uncertainty looms.
The snapshot below illustrates how clients can benefit from RiskSpan and Intex data and analysis in this sector. Using its embedded Tableau functionality, RS Edge can quickly show investors the top country, carrier and aircraft exposures for each deal. Investors concerned about carriers that might be increasingly vulnerable to Coronavirus disruption (such as Italian or Asian airlines today and likely others in coming weeks) can determine the exposure to these countries using the platform. In addition, when news is announced that impacts the credit quality of carriers, investors can view exposure to these carriers and, with additional analysis, calculate the potential residual value of individual aircraft if the carrier goes bankrupt or the lease terminates. RiskSpan can also provide data on exposure to aircraft manufacturers and provide valuation of bonds backed by aircraft leases.
Contact us to learn more about how RiskSpan helps clients manage their airline (and other risk) exposure and how we can assist with customized requests to perform further analysis requiring add-on data or calculations.
RiskSpan VQI: Current Underwriting Standards – February 2020
The RiskSpan Vintage Quality Index (“VQI”) edged higher for mortgages originated during February despite remaining low (90.41) by historical, pre-crisis standards. Low-FICO and high-LTV loans continued to trend downward, while high-DTI loans, investment properties, and cash-out refinances continued to rebound after declining through much of 2019.
As the historical trend of risk layering (see below) shows, mortgages with one borrower—now accounting for more than 50 percent of originations—remain a consistent and important driver of the index. High-DTI loans today drive the index more than they did during the years immediately after the 2008 crisis but not nearly so much as they did during the years leading up to it. High-LTV loans continue to be originated in abundance, while adjustable-rate mortgages and loans with subordinate financing, in contrast, have practically vanished.
RiskSpan introduced the VQI in 2015 as a way of quantifying the underwriting environment of a particular vintage of mortgage originations. The idea is to provide credit modelers a way of controlling for a particular vintage’s underwriting standards, which tend to shift over time. The VQI is a function of the average number of risk layers associated with a loan originated during a given month. It is computed using:
- The loan-level historical data released by the GSEs in support of Credit Risk Transfer initiatives (CRT data) for months prior to December 2005, and
- Loan-level disclosure data supporting MBS issuances through today.
The value is then normalized to assign January 1, 2003 an index value of 100. The peak of the index, a value of 139 in December 2007, indicates that loans issued in that month had an average risk layer factor 39% greater (i.e., loans issued that month were 39% riskier) than loans originated during 2003. In other words, lower VQI values indicate tighter underwriting standards (and vice-versa).
Build-Up of VQI
The following chart illustrates how each of the following risk layers contributes to the overall VQI:
- Loans with low credit scores (FICO scores below 660)
- Loans with high loan-to-value ratios (over 80 percent)
- Loans with subordinate liens
- Loans with only one borrower
- Cash-out refinance loans
- Loans secured by multi-unit properties
- Loans secured by investment properties
- Loans with high debt-to-income ratios (over 45%)
- Loans underwritten based on reduced documentation
- Adjustable rate loans