Within weeks of the March 11th declaration of COVID-19 as a global pandemic by the World Health Organization, rating agencies were downgrading businesses across virtually every sector of the economy. Not surprisingly, these downgrades were felt most acutely by businesses that one would reasonably expect to be directly harmed by the ensuing shutdowns, including travel and hospitality firms and retail stores. But the downgrades also hit food companies and other areas of the economy that tend to be more recession resistant.
An accompanying spike in credit spreads was even quicker to materialize. Royal Caribbean’s and Marriott’s credit spreads tripled essentially overnight, while those of other large companies increased by twofold or more.
But then something interesting happened. Almost as quickly as they had risen, most of these spreads began retreating to more normal levels. By mid-June, most spreads were at or lower than where they were prior to the pandemic declaration.
What business reason could plausibly explain this? The pandemic is ongoing and aggregate demand for these companies’ products does not appear to have rebounded in any material way. People are not suddenly flocking back to Marriott’s hotels or Wynn’s resorts.
The story is indeed one of increased demand. But rather than demand for the companies’ products, we’re seeing an upswing in demand for these companies’ debt. What could be driving this demand?
Enter the Federal Reserve. On March 23rd, The Fed announced that its Secondary Market Corporate Credit Facility (SMCCF) would begin purchasing investment-grade corporate bonds in the secondary market, first through ETFs and directly in a later phase.
And poof! Instant demand. And instant price stabilization. All the Fed had to do was announce that it would begin buying bonds (it hasn’t actually started buying yet) for demand to rush back in, push prices up and drive credit spreads down.
To illustrate how quickly spreads reacted to the Fed’s announcement, we tracked seven of the top 20 companies listed by S&P across different industries from early March through mid-June. The chart below plots swap spreads for a single bond (with approximately five years to maturity) from each of the following companies:
- Royal Caribbean Cruises (RCL)
- The TJX Companies (which includes discount retailers TJ Maxx, Marshalls, and HomeGoods, among others)
- Wynn Resorts
- Kraft Foods
- Ford Motor Company
We sourced the underlying data for these charts from two RiskSpan partners: S&P, which provided the timing of the downgrades, and Refinitiv, which provided time-series spread data.
The companies we selected don’t cover every industry, of course, but they cover a decent breadth. Incredibly, with the lone exception of Royal Caribbean, swap spreads for every one of these companies are either better than or at the same level as where they were pre-pandemic.
As alluded to above, this recovery cannot be attributed to some miraculous improvement in the underlying economic environment. Literally the only thing that changed was the Fed’s announcement that it would start buying bonds. The fact that Royal Caribbean’s spreads have not fully recovered seems to suggest that the perceived weakness in demand for cruises in the foreseeable future remains strong enough to overwhelm any buoying effect of the impending SMCCF investment. For all the remaining companies, the Fed’s announcement appears to be doing the trick.
We view this as clear and compelling evidence that the Federal Reserve in achieving its intended result of stabilizing asset prices, which in turn should help ease corporate credit.
Our chart of the month presents data illustrating what has already been acutely felt by mezzanine and other subordinate bond investors – a sharp rise in spreads across all sectors coinciding with the imposition of pandemic-related lockdowns in the United States and around the world.
Spreads on aircraft leases had already begun widening by the start of March as travel was slowing dramatically well before widespread government-imposed shutdowns began hitting other parts of the economy. Spreads on aircraft bonds soared to 1,800 basis points at the end of March and 2,400 basis points on April 25th.
Aircraft differed from most other sectors in its spreads continued to widen throughout April. Spreads in most other sectors began reverting closer to normal in April after experiencing the March market shock. Another notable exception to this pattern were timeshare spreads, which also continued widening during April, reaching a level on April 25th four times where they were on March 2nd.
It is not surprising to see bonds associated with the travel sector of the economy react in this way. Other sectors that did not rebound during April included student, equipment, floor plan and commercial loans.
Widening spreads naturally correspond with price declines over the same period.
The spreads in this chart were computed using TRACE data enhanced by RiskSpan’s Market Color application.
Market dislocations like these are compelling an increasing number of portfolio managers to begin marking their portfolios to model rather than to market. Join us on Thursday at 1:00 p.m. for the webinar, “Valuing Hard-to-Value Bonds” for a lively discussion on some of the ramifications of this change.
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.
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.
The non-agency residential-mortgage-backed-securities (RMBS) market has high expectations for increased volume in 2020. Driven largely by expected changes to the qualified mortgage (QM) patch, private-label securities (PLS) issuers and investors are preparing for a 2020 surge. The tight underwriting standards of the post-crisis era are loosening and will continue to loosen if debt-to-income restrictions are lifted with changes to the QM patch.
PLS programs can differ greatly. It’s increasingly important to understand the risks inherent in each underlying pool. At the same time, investment opportunities with substantial yield are becoming harder to find without developing a deep understanding of the riskier components of the capital structure. A structured approach to pre-trade and portfolio analytics can help mitigate some of these challenges. Using a data-driven approach, portfolio managers can gain confidence in the positions they take and make data influenced pricing decisions.
Industry best practice for pre-trade analysis is to employ a holistic approach to RMBS. To do this, portfolio managers must combine analysis of loan collateral, historical data for similar cohorts of loans (within previous deals), and scenarios for projected performance. The foundation of this approach is:
- Historical data can ground assumptions about projected performance
- A consistent approach from deal to deal will illuminate shifting risks from shifting collateral
- Scenario analysis will inform risk assessment and investment decision
RiskSpan’s modeling and analytics expert, Janet Jozwik, suggests a framework for analyzing a new RMBS deal with analysis of 3 main components: deal collateral, historical performance, and scenario forecasting. Combined, these three components give portfolio managers a present, past, and future view into the deal.
Present: Deal Collateral Analysis
Deal collateral analysis consists of: 1) a deep dive into the characteristics of the collateral underlying the deal itself, and 2) a comparison of the collateral characteristics of the deal being analyzed to similar deals. A comparison to recently issued deals can highlight shifts in underlying collateral risk within a particular shelf or across issuers.
Below, RiskSpan’s RS Edge provides the portfolio manager with a dashboard highlighting key collateral characteristics that may influence deal performance.
Example 1: Deal Profile Stratification
Example 2: Deal Comparative Analysis
Past: Historical Performance Analysis
Historical analysis informs users of a deal’s potential performance under different scenarios by looking at how similar loan cohorts from prior deals have performed. Jozwik recommends analyzing historical trends both from the recent past and from historical stress vintages to give a sense for what the expected performance of the deal will be, and what the worst-case performance would be under stress scenarios.
Recent Trend Analysis: Portfolio managers can understand expected performance by looking at how similar deals have been performing over the prior 2 to 3 years. There are a significant number of recently issued PLS that can be tracked to understand recent prepayment and default trends in the market. While the performance of these recent deals doesn’t definitively determine expectations for a new deal (as things can change, such as rate environment), it provides one data point to help ground data-driven analyses. This approach allows users to capitalize on the knowledge gained from prior market trends.
Historical Vintage Proxy Analysis: Portfolio managers can understand stressed performance of the deal by looking at performance of similar loans from vintages that experienced the stress environment of the housing crisis. Though potentially cumbersome to execute, this approach leverages the rich set of historical performance data available in the mortgage space.
For a new RMBS Deal, portfolio managers can review the distribution of key features, such as FICO, LTV, and documentation type. They can calculate performance metrics, such as cumulative loss and default rates, from a wide set of historical performance data on RMBS, cut by vintage. When pulling these historical numbers, portfolio managers can adjust the population of loans to better align with the distribution of key loan features in the deal they are analyzing. So, they can get a view into how a similar loans pool originated in historical vintages, like 2007, performed. There are certainly underwriting changes that have occurred in the post-crisis era that would likely make this analysis ultra–conservative. These ‘proxy cohorts’ from historical vintages can provide an alternative insight into what could happen in a worst-case scenario.
Future: Forecasting Scenario Analysis
Forecasting analysis should come in two flavors. First, very straightforward scenarios that are explicitly transparent about assumptions for CPR, CDR, and severity. These assumptions-based scenarios can be informed with outputs from the Historical Performance Analysis above.
Second, forecasting analysis can leverage statistical models that consider both loan features and macroeconomic inputs. Scenarios can be built around macroeconomic inputs to the model to better understand how collateral and bond performance will change with changing economic conditions. Macroeconomic inputs, such as mortgage rates and home prices, can be specified to create particular scenario runs.
How RiskSpan Can Help
Pulling the required data and models together is typically a burden. RiskSpan’s RS Edge has solved these issues and now offers one integrated solution for:
- Historical Data: Loan-level performance and collateral data on historical and pre-issue RMBS deals
- Predictive Models: Credit and Prepayment models for non-agency collateral types
- Deal Cashflow Engine: Intex is the leading source for an RMBS deal cashflow library
There is a rich source of data, models, and analytics that can support decision making in the RMBS market. The challenge for a portfolio manager is piecing these often-disparate pieces of information together to a cohesive analysis that can provide a consistent view from deal to deal. Further, there is a massive amount of historical data in the mortgage space, containing a vast wealth of insight to help inform investment decisions. However, these datasets are notoriously unwieldy. Users of RS Edge cut through the complications of large, disparate datasets for clear, informative analysis, without the need for custom-built technology or analysts with advanced coding skills.
RiskSpan Introduces RS Edge for Loans and Structured Products
RiskSpan, the leading mortgage data and analytics provider, is excited to announce the release of RS Edge for Loans and Structured Products.
RS Edge is the next generation of RiskSpan’s data, modeling, and analytics platform that manages portfolio risk and delivers powerful analysis for loans and structured products. Users can derive insights from historical trends and powerful predictive forecasts under a range of economic scenarios on our cloud-native solution. RS Edge streamlines analysis by bringing together key industry data and integrations with leading 3rd party vendors.
An on-demand team of data scientists, quants, and technologists with fixed-income portfolio expertise support the integration, calibration, and operation across all RS Edge modules.
RMBS Analytics in Action
RiskSpan has developed a holistic approach to RMBS analysis that combines loan collateral, historical, and scenario analysis with deal comparison tools to more accurately predict future performance. Asset managers can define an acceptable level of risk and ground pricing decisions with data-driven analysis. This approach illuminates risk from shifting collateral and provides investors with confidence in their positions.
Loan Analytics in Action
Whole loan asset managers and investors use RiskSpan’s Loan Analytics to enhance and automate partnerships with Non-Qualified Mortgage originators and servicers. The product enhances the on-boarding, pricing analytics, forecasting, and storage of loan data for historical trend analytics. RS Edge forecasting analytics support rate–sheet validation and loan pricing.
RiskSpan provides innovative technology and services to the financial services industry. Our mission is to eliminate inefficiencies in loans and structured finance markets to improve investors’ bottom line through incremental cost savings, improved return on investment, and mitigated risk.
RiskSpan is holding a webinar on November 6 to show how RS Edge pulls together past, present, and future for insights into new RMBS deals. Click below to register.
RiskSpan Adds Whole Loan Analytics to Edge Platform
ARLINGTON, VA, May 20, 2019 – Leading mortgage data and analytics provider RiskSpan announced the release of its Whole Loan Analytics Module on the RiskSpan Edge Platform. The module enables whole loan investors, portfolio managers, and risk managers to manage loan-level data flows and predictive models that forecast loan performance under a range of scenarios.
The off-the-shelf SaaS version supports whole loan pricing and surveillance. It enables complex forecasting analytics including geographically granular House Price scenarios and historically significant economic event scenarios. Other features and custom configurations are also available for advanced risk management use cases.
RiskSpan’s Whole Loan Analytics Module is supported by a team of data scientists, quants, and technologists who maintain the company’s proprietary prepayment and credit models. The SaaS delivery model includes continuous feature updates.
Machine Learning for Better Whole Loan Data Management
The Edge Platform uses machine learning to normalize and standardize data from disparate data file input formats. With this technology, users may easily benchmark portfolio performance against a combination of datasets. Better data inputs also dramatically improve the accuracy of downstream analytics.
Whole Loan Analytics in Production
Recently, a large asset manager sought to enter the whole loan market by partnering with Non-Qualified Mortgage originators and servicers. This asset manager subscribed to RiskSpan’s Edge Platform and used the Whole Loan Analytics Module to perform end-to-end tracking, analysis, forecasting, and storage of all loan data. RS Edge forecasting analytics support rate sheet validation, loan pricing and pipeline analysis. The client uses the platform to automatically load and validate new data.
About RiskSpan’s Edge Platform
The Edge Platform is a cloud-native, data, modeling, and analytics platform for loans, securities, and structured products. RiskSpan’s commercially available SaaS platform allows clients to integrate their data with leading third-party data providers. The Edge Platform solves the hardest data management and analytical problem – affordable, off-the-shelf integration of clean data and reliable predictive models.
RiskSpan is a leading provider of technology solutions and services to the residential mortgage, capital markets, banking, and insurance industries. RiskSpan’s mission is to innovate. We help clients deploy new technologies to eliminate the inefficiencies in the loan and structured finance markets and leverage the value of advanced analytics.