Collateralized Loan Obligations
Authors from the North American Client Team:
Warren A Sherman firstname.lastname@example.org
Mark Slattery email@example.com
Eric Penanhoat firstname.lastname@example.org
NEW YORK, September 14, 2020: “Leveraged finance default rates increased during 2020 as COVID-19 lockdowns impacted corporate earnings and the capacity to service debt. US institutional loan defaults climbed to 3.9% on a trailing 12-month basis in June, according to Debtwire Par. This is the highest level since the 2008 financial crisis and puts the year-to-date default figure at US$44 billion.”
While CLOs are getting plenty of attention in industry publications, insurance companies and pension funds, which hold the bulk of the mezzanine tranches, are displaying a surprising nonchalance. Undoubtedly many corporate debt obligors are counting on continued support from the Administration, the Federal Reserve, and Congress until revenues and cash flows required to service their debt returns to pre-COVID levels, or they will be able to find financing to rollover the debt.
In support, look at the Fed’s corporate bond portfolio. Note the portion that is speculative grade, i.e., with a below triple BBB rating. That wasn’t as planned. When the Secondary Market Corporate Credit Facility was established in March of this year, the policy statement given at the time was to support market liquidity by purchasing “corporate bonds issued by investment grade companies or certain U.S. companies that were investment grade as of March 22.” Unfortunately, their published portfolio doesn’t include bond or issuer ratings, so it’s tough to determine which bonds and which issuers hit the slippery slope since the program’s launch. Note as well that over half of the portfolio is only a notch above speculative grade.
According to the Wall Street Journal, US companies raised $900 billion through bond sales between April 1 and August 31, more than twice the volume of a year ago. Much of this was used to pay back revolving credit facilities.
A look at an index of Corporate debt for the past twenty-two years (i.e., quarterly data indexed to 2001) confirms the marked increase in debt liabilities. The only difference is that today commercial banks are less inclined to keep these loans on their balance sheets and many have partnered with investment banks to securitize and sell them.
So, which bonds are being purchased in which sectors? Presumably this is an indication of the Fed’s belief pertaining to the sectors most at risk. From the same Fed report, consumer sectors, which include restaurants, hospitality, media, apparel, retail, and autos and auto parts, are clearly more heavily weighted in the Fed’s bond purchasing decisions.
THE FED’S BOND PORTFOLIO
It’s not hard to see why. Consider Kamakura’s Troubled Company Index for the U.S. Consumer Discretionary sector and one-year default probabilities (Figure 1). The highest risk gradient (i.e., colored red) has reached the level attained during the past credit crisis, when that sector was hit very hard.
Figure 1: Kamakura Troubled Company Index for U.S. Consumer Discretionary Sector
Given the uncertainty of future of the economic environment and market conditions, how can we accurately evaluate the potential for loses for these securitized investments? How do we link relevant variables to loan performance among the CLO collateral and proceed to simulate credit losses?
For exposition we start by constructing a CLO portfolio. The tranches in our portfolio include the names below which fall in the Consumer sector and happen to be rated (Figure 2). We then estimate default probability functions for each loan obligor using the online data store maintained by Kamakura Risk Information Services and the KRIS online regression libraries. Default probabilities for the loan obligors provide the dependent variables. In our example the regressions were based on twenty years of history, enough to capture at least one significant downturn other than the recent one. For the Value at Risk analysis, the number of factors was reduced to twelve for brevity, though there is no fixed limit. KRIS is designed to allow users to freely incorporate their insights into default modeling and its application.
Figure 2: CLO Portfolio Consumer Sector Obligors
For each loan obligor, the KRIS default probability function estimator lists the top explanatory variables based on statistical significance (Figure 3). Of course, the list and variable coefficients are different for each loan obligor. Standard outputs are displayed such as the variable name, coefficient value, standard error, t-statistic, and confidence boundaries, as well as a chart which overlays actual and predicted data values.
Figure 3: PD Function Regression Performance – STAPLES INC.
By simulating loan defaults for each collateral item for a thousand or more trials, KRIS is able to pass simulated defaults up to CDO Net (or other third-party waterfall providers) and receive back default-adjusted cash flows. By summarizing these, it is possible to evaluate each tranche and compute its losses. KRIS will report loss at both the loan and tranche levels. There are two versions of its TAIL RISK output tab below (Figures 4&5). One is ranked by size of the loss and one by percent change from the nominal valuation. The outputs also display the values of the simulated risk factor variables which drive the defaults, so it’s possible to understand and reconcile the outcomes with the scenarios as generated.
Figure 4: Tail Risk by Scenario and Percent Factor Change
Figure 5: Tail Risk by Scenario and Risk Factor Value
The overall portfolio loss distribution for all simulated paths is available as well as the loss for each loan pledged as collateral. Columns itemize the initial default probabilities, minimum and maximum values, and the percentage of simulated defaults per the total number of simulated paths (Figure 6).
Figure 6: Default Probabilities Summary for VaR Monte Carlo Simulation
In 2007 and 2008, as we were recently reminded in Atlantic magazine , over thirteen thousand triple-A rated CDOs defaulted. Ratings alone are a poor substitute for default probability models and factor-driven loss simulation. Given the long intervals between ratings updates, their utility is primarily conferred on primary issuance. Further, some loans are not rated at all, so as a risk practitioner, how do you evaluate the risk of loss on unrated collateral irrespective of the rating of the CLO tranche?
KRIS and its Credit Portfolio Manager suite is the industry’s most accurate, flexible, and transparent answer to CLO, Combo Note and portfolio modeling. Our next installment will look specifically at Combo Notes.
About Kamakura Corporation
Founded in 1990, Honolulu-based Kamakura Corporation is a leading provider of risk management information, processing, and software. Kamakura was recognized as a category leader in the Chartis Report, Technology Solutions for Credit Risk 2.0 2018. Kamakura was named to the World Finance 100 by the editor and readers of World Finance magazine in 2017, 2016 and 2012. In 2010, Kamakura was the only vendor to win two Credit Magazine innovation awards. Kamakura Risk Manager, first sold commercially in 1993 and now in version 10.1, is the first enterprise risk management system for users focused on credit risk, asset and liability management, market risk, stress testing, liquidity risk, counterparty credit risk, and capital allocation from a single software solution. The KRIS public firm default service was launched in 2002. The KRIS sovereign default service, the world’s first, was launched in 2008, and the KRIS nonpublic firm default service was offered beginning in 2011. Kamakura added its U.S. Bank default probability service in 2014.
Kamakura has served more than 330 clients with assets ranging in size from $1.5 billion to $3.0 trillion. Current clients have a combined “total assets” or “assets under management” in excess of $26 trillion. Its risk management products are currently used in 47 countries, including the United States, Canada, Germany, the Netherlands, France, Austria, Switzerland, the United Kingdom, Russia, Ukraine, South Africa, Australia, China, Hong Kong, India, Indonesia, Japan, Korea, Malaysia, Singapore, Sri Lanka, Taiwan, Thailand, Vietnam, and many other countries in Asia, Europe and the Middle East.
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