In the years of calm economic expansion before CECL adoption, institutions carefully tuned the macroeconomic forecasting approaches and macro-conditioned credit models they must defend under the new standard. Now, seemingly an hour before public entities are to record (and support) their first macro-conditioned credit losses, a disease with no cure and no vaccine sweeps the globe and darkens whole sectors of the economy. Truth is stranger than fiction. 

Institutions Need New Scenarios and Model Adjustments, and Fast 

Institutions must overhaul their projection capabilities to withstand audit scrutiny and with only nominal relief in CECL deadlines. 

Faced with this unprecedented crisis, many institutions will need to find new sources of macroeconomic scenarios. Business-as-usual scenarios that seemed sensible a few months ago now appear wildly optimistic. 

Credit and prepay models built on data prior to February 2020 – the models that institutions have spent so much time and effort validating – must now be rethought entirely. 

Institutions may not have a great deal of time to make the necessary adjustments. While the Coronavirus Aid, Relief, and Economic Security (CARES) Act (in Section 4014, Optional Temporary Relief from Current Expected Credit Losses) allows banks and credit unions a brief delay in CECL adoption, RiskSpan’s public bank clients are adopting as scheduled. One reason is the short and uncertain length of the delay, which expires either on 12/31/2020 or when the national coronavirus emergency is declared over, whichever comes first. The national emergency could be declared over at any time, and indeed we hope the national emergency does end soon. Another reason is that, as Grant Thornton has noted, eligible entities that defer adoption will need to retrospectively restate their year-to-date results when they adopt ASU 2016-13. Ultimately, the “relief” is anti-relief. 

The revised CECL approaches that institutions race into production will need to withstand the inspection not only of the normal sets of eyes, but many other senior stakeholders. Scrutiny on credit accounting will be more intense than ever in light of COVID-19. 

Finally, to converge on a new macroeconomic scenario and model adjustments, institutions will be prompted by auditors and senior management to run their portfolio many times under many different combinations of approaches. As you can imagine, the volume of runs hitting RiskSpan’s Allowance Suite has spiked this month, with institutions running many different scenarios, and institutions with available-for-sale bond portfolios sending more impaired bonds than anticipated. The physics of pulling off so many runs in such a short time are impossible for teams and systems not set up for that scale. 

How RiskSpan is Helping Institutions Overcome These Challenges 

RiskSpan helps clients solve the new credit accounting rules for loans, held-to-maturity debt securities, and available-for-sale debt securities. As we all navigate these unique and evolving times, let us share how we incorporate the impact of COVID-19 into the allowances we generate. The toolbox includes new macroeconomic scenarios that reflect COVID-19, adjustments to credit and prepay models, an ability to selectively bypass models with user-defined scenarios, and even – sparingly – support for the dreaded “Q-factor” or management qualitative adjustment. 

COVID-19-Driven Macroeconomic scenarios 

RiskSpan partners with S&P Global Market Intelligence (“Market Intelligence”), employing their CECL model within our Allowance Suite. Each quarter, we apply a new macroeconomic forecast from the S&P Global Ratings team of economists (“S&P Global Economics”). We feed this forecast to all credit models in our platform to influence projections of default and severity and ultimately allowance. S&P Global Economics recent research has focused significantly on coronavirus, including the global and US economic impact of containment and mitigation measures and the recovery timeline. When the credit models take in this bearish outlook for the 3/31/2020 runs, they will return higher defaults and severities compared to prior quarters when the macroeconomic forecast was benign, which in turn will drive higher allowances. Auditors, examiners, and investors will rightly expect to see this.  

MODEL ENHANCEMENTS AND TUNINGS 

RiskSpan advises clients to apply model enhancements or adjustments as follows:

C&I loans, Corporate Bonds, Bank Loans, CLOs 

Corporate & Industrial (C&I) loans often carry internal risk ratings that are ‘through-the-cycle’ evaluations of the default risk and highly independent of cyclical changes in creditworthiness. Corporate bonds carry public credit ratings that are designed to represent forward-looking opinions about the creditworthiness of issuers and obligations, known to be relatively stable throughout a cycle. (Note: higher ratings have been consistently more stable than lower ratings).  

During upswings (downturns), an obligor’s point-in-time or short-term default rates will fall (rise) as the economic environment changes, and credit expectations may be better (worse) than implied by stable credit indicators and associated long-term default frequencies.  

To appropriately reflect the impact of COVID-19 on allowances, most of our clients are now applying industry-specific Point-in-Time (PIT) adjustments, based on Market Intelligence’s Probability of Default Model Market Signals (PDMS). These PIT signals, which use recent, industry-specific trading activity, are used as a guide to form limited adjustments to stable (or in some cases lagging) internal risk ratings of commercial loans and the current credit ratings of corporate bonds. (Adjustments are for the purpose of the CECL model only.)  

Because these adjusted risk ratings are key inputs to Market Intelligence’s CECL Model that we apply to these asset classes, Market Intelligence’s PDMS can influence allowances. Since economic conditions impact certain industry sectors (e.g., airlines, oil and gas, retail) in different ways, the industry-specific notches tend to vary by industry – some positive, some neutral, some negative. Consequently, in a diversified portfolio, we would not ordinarily expect a directional bias to the overall allowance, even though the allowance by industry will be refined. But this assumes a normal economic climate. During a major market downturn like we experienced in the runup to March 31, 2020, notches were negative across almost all industries, and we saw higher allowances as a result. Given the environment, this result is to be expected.  

Resi Loans and RMBS 

Even if we forecast the macroeconomy exactly right, the models of how borrowers perform given different macroeconomic patterns were built on prior decades of experience. Some of the macroeconomic twists and turns that this crisis will unleash will take different shapes than the last crisis.  

For example, a model built on the past two decades of data can only extrapolate what borrowers will do if unemployment goes to 20%; the historical data doesn’t contain such a stress. Even if the macroeconomic patterns do resemble prior crises, policy response will be different, and so will borrower behavior. And then after some recovery period, we expect borrower behavior to fall back into its classic grooves. For these reasons, we recommend model tunings that, all else equal, boost or dampen delinquencies, defaults and recoveries during a time-limited recovery window to account for the near-term impacts of COVID-19. We help clients quantify these model tunings by back-testing model projections against experience from recent weeks. 

In the past month, we have observed slower prepays from housing turnover because social distancing has blocked on-site walkthroughs and therefore home sales. Refinance applications, however, continue to roll in (as expected in this low rate environment), and the refi market is adopting property inspection waivers and remote notarization to work through the demand. As noted under Credit Model Tuning above, we help clients quantify and apply prepay model tunings that act in the short-term and can phase out across the long-term forecast horizon. 

ABS 

Conventionally, ABS research departments form expected-case projections for underlying collateral by averaging the historical default, severity, and prepay behavior of the issuer. Because CECL calls for expected-case projections, RiskSpan’s bond research team has applied the same approach to generate ABS collateral projections for clients. 

ABS researchers identify stress scenarios by applying multiples or standard deviations to the historical averages. In the current climate, the expected case is a stress case. Therefore, RiskSpan has refined its methodology to apply our stress scenarios as our expected scenarios in times – like now – when the S&P Global Economics baseline macroeconomic forecasts show stress. 

MODEL OVERRIDES/USER-DEFINED SCENARIOS 

Where clients have their own views of how loans or bonds will perform, we have always empowered them to bypass models and define their own projections of default, severity, and prepayment. 

Resi Loans and RMBS 

For resi loans and RMBS collateral, we have rolled out new “build-a-curve” functionality that allows clients to use our platform to create their own default and severity paths by stipulating drivers such as: 

  • Peak unemployment rate,  
  • How long the peak will last and where unemployment rate will settle, 
  • Share of those unemployed who will roll delinquent, 
  • Length of external forbearance timelines, and 
  • Share of loans that roll delinquent that will eventually default. 

“Q-Factors” 

At many institutions, we have seen “Q-factors” (“qualitative” management adjustments on top of modeled allowance results) go from all but forbidden before this crisis to all but required during it. This is because the macroeconomic scenarios now being fed into credit models is beyond the data upon which any vendor or proprietary models were built.  

For example, unemployment rate gradually rose to 10% during the great recession. Many scenarios institutions are now considering call for unemployment rate to spike suddenly to 20% or more. Models can only extrapolate (beyond their known sample) to project obligor performance under these scenarios—there is nothing else they can do. But we know that such extrapolations are unlikely to be exactly right. This creates a strong argument to allow, or even encourage, management adjustments to model results. We are advising many clients to do just that, drawing on available data from natural disasters. 

Throughput 

As important as these quantitative refinements are, performing multiple runs to better understand the range of possible allowance results is equally important to meeting auditor expectations. Whereas before some institutions would use month-end allowances from a month before quarter-end because of tight reporting deadlines, now such institutions are running again at quarter-end, under a very tight timeline, to meet auditor demands for up-to-the-minute analysis. Whereas previously many institutions would run one macroeconomic scenario, now – at the prompting of auditors or their own management – they are running multiple. Institutions that previously did not apply Market Intelligence’s PDMS to their commercial loans and corporate bonds are now running with and without it to evaluate the difference. The dimensionality quickly explodes from one run per quarter to two, ten, or twenty. RiskSpan is happy to offer its platform to clients to support such throughput. 

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We will be exploring these topics in greater detail in a webinar on May 28th, 2020 at 1:00 p.m. EDT. You can join us by registering here. I can also be reached directly at dandrukonis@riskspan.com.