Tag Archives: CDOs

Correlation contagion

I fear that the daily announcements on bankruptcies, specifically in the retail sector, is just the beginning of the journey into our new reality. Despite relatively positive noises from US banks about short term loan provisions and rebounding consumer spending, the real level of defaults, particularly in the SME sector, will not become clear until the sugar high of direct government stimulus is withdrawn. In the UK, for example, the furlough scheme is paying 80% of the wages of approximately 9 million workers and is currently costing the same in government spending monthly as the NHS. This UK subsidy is due to be withdrawn in October. In the US, the $600 weekly boost to unemployment payments is due to expire at the end of July.

The S&P forecasts for the default rate on US junk debt, as below, illustrates a current projection. There are many uncertainties on the course of the pandemic and the economic impacts over the coming months and quarters that will dictate which scenario is in our future.

It is therefore not surprising that the oft highlighted concerns about the leveraged loan market have been getting a lot of recent attention, as the following articles in the New Yorker and the Atlantic dramatically attest to – here and here. I would recommend both articles to all readers.

I must admit to initially feeling that the dangers have been exaggerated in these articles in the name of journalist license. After all, the risks associated with the leveraged loan market have been known for some time, as this post from last year illustrates, and therefore we should be assured that regulators and market participants are on top of the situation from a risk management perspective. Right? I thought I would dig a little further into the wonderful world of collateralized loan obligations, commonly referred to as CLOs, to find out.

First up is a report from the Bank of International Settlements (BIS) in September on the differences between collateralised debt obligations (CDOs) and CLOs. I was heartened to learn that “there are significant differences between the CLO market today and the CDO market prior to the great financial crisis”. The report highlighted the areas of difference as well as the areas of similarity as follows:

CLOs are less complex, avoiding the use of credit default swaps (CDS) and resecuritisations; they are little used as collateral in repo transactions; and they are less commonly funded by short-term borrowing than was the case for CDOs. In addition, there is better information about the direct exposures of banks. That said, there are also similarities between the CLO market today and the CDO market then, including some that could give rise to financial distress. These include the deteriorating credit quality of CLOs’ underlying assets; the opacity of indirect exposures; the high concentration of banks’ direct holdings; and the uncertain resilience of senior tranches, which depend crucially on the correlation of losses among underlying loans.

The phase “uncertain resilience of senior tranches” and the reference to correlation sent a cold shiver down my spine. According to BIS, the senior AAA tranches are higher up the structure (e.g. 65% versus 75%-80% in the bad old days), as this primer from Guggenheim illustrates:

As in the good old CDO days, the role of the rating agencies is critical to the CLO ecosystem. This May report from the European Securities and Markets Authority (ESMA) shows that EU regulators are focused on the practices the rating agencies are following in relation to their CLO ratings. I was struck by the paragraph below in the executive summary (not exactly the reassurance I was hoping for).

The future developments regarding the Covid 19 outbreak will be an important test for CLO methodologies, notably by testing: i) the approaches and the assumptions for the modelling of default correlation among the pool of underlying loans; and ii) the sensitivity of CLO credit ratings to how default and recovery rates are calibrated. Moreover, the surge of covenant-lite loans prevents lenders and investors from early warning indicators on the deterioration of the creditworthiness of the leveraged loans.

As regular readers will know, correlations used extensively in financial modelling is a source of much blog angst on my part (examples of previous posts include here, here and here). As I may have previously explained, I worked for over a decade in a quant driven firm back in the 1990’s that totally underestimated correlations in a tail event on assumed diverse risk portfolios. The firm I worked for did not survive long after the events of 9/11 and the increased correlation across risk classes that resulted. It was therefore with much bewilderment that I watched the blow-up in complex financial structures because of the financial crisis and the gross misunderstanding of tail correlations that were absent from historical data sets used to calibrate quant models. It is with some trepidation therefore when I see default correlation been discussed yet again in relation to the current COVID19 recession. To paraphrase Buffett, bad loans do not become better by simply repackaging them. Ditto for highly leveraged loans with the volume turned up to 11. As many commentators have highlighted in recent years and the Fed noted recently (see this post), the leverage in terms of debt to EBITDA ratios in leveraged loans has crept up to pre-financial crisis levels before the COVID19 global outbreak.

Next up, I found this blog from MSCI in early April insightful. By applying market implied default rates and volatilities from late March to MSCI’s CLO model of a sample 2019 CLO deal with 300 loans diversified across 10 industry sectors, they arrived at some disturbing results. Using 1-year default rates for individual risks of approximately 20% to 25% across most sectors (which does not seem outrageous to me when talking about leverage loans, they are after all highly leveraged!!), they estimated the probability of joint defaults using sample 2019 CLO deal at 1 and 3-year horizons as below.

The MSCI analysis also showed the implied cross-sector default-rate correlations and a comparison with the correlations seen in the financial crisis, as below.

Even to me, some of these correlations (particularly those marked in red) look too elevated and the initial market reaction to COVID19 of shoot first and ask questions later may explain why. The MSCI article concludes with an emphasis on default correlation as below.

During periods of low default correlation, even with relatively high loan default rates, the tail probability of large total default is typically slim. If the current historically high default-rate correlations persist — combined with high loan default rates and default-rate volatility — our model indicates that a large portion of the examined pool may default and thereby threaten higher-credit tranches considered safe before the crisis

I decided to end my CLO journey by looking at what one rating agency was saying. S&P states that the factors which determine their CLO ratings are the weighted average rating of a portfolio, the diversity of the portfolio (in terms of obligors, industries, and countries), and the weighted average life of the portfolio. Well, we know we are dealing with highly leveraged loans, the junkiest if you like, with an average pre-COVID rating of B (although it is likely lower in today’s post-COVID environment) so I have focused on the portfolio diversity factor as the most important risk mitigant. Typically, CLOs have 100 to 300 loans which should give a degree of comfort although in a global recession, the number of loans matters less given the common risky credit profile of each. In my view, the more important differentiator in this recession is its character in terms of the split between sector winners and losers, as the extraordinary rally in the equity market of the technology giants dramatically illustrates.

S&P estimated that as at year end 2019, the average CLO contained approximately 200 loans and had an average industry diversity metric of 25. Its important to stress the word “average” as it can hid all sorts of misdemeanors. Focusing on the latter metric, I investigated further the industry sector classifications used by S&P. These classifications are different and more specific from the usual broad industry sectorial classifications used in equity markets given the nature of the leveraged loan market. There are 66 industry sectors in all used by S&P although 25 of the sectors make up 80% of the loans by size. Too many spurious variables are the myth that often lies at the quant portfolio diversification altar. To reflect the character of this recession, I judgmentally grouped the industry sectors into three exposure buckets – high, medium and low. By sector number the split was roughly equal to a third for each bucket. However, by loan amount the split was 36%, 46% and 18% for the high, medium and low sectors respectively. Over 80% in the high and medium buckets! That simplistic view of the exposure would make me very dubious about the real amount of diversification in these portfolios given the character of this recession. As a result, I would question the potential risk to the higher credit quality tranches of CLOs if their sole defense is diversification.

Maybe the New Yorker and Atlantic articles are not so sensationalist after all.

Confounding correlation

Nassim Nicholas Taleb, the dark knight or rather the black swan himself, said that “anything that relies on correlation is charlatanism”.  I am currently reading the excellent “The signal and the noise” by Nate Silver. In Chapter 1 of the book he has a nice piece on CDOs as an example of a “catastrophic failure of prediction” where he points to certain CDO AAA tranches which were rated on an assumption of a 0.12% default rate and which eventually resulted in an actual rate of 28%, an error factor of over 200 times!.

Silver cites a simplified CDO example of 5 risks used by his friend Anil Kashyap in the University of Chicago to demonstrate the difference in default rate if the 5 risks are assumed to be totally independent and dependent.  It got me thinking as to how such a simplified example could illustrate the impact of applied correlation assumptions. Correlation between core variables are critical to many financial models and are commonly used in most credit models and will be a core feature in insurance internal models (which under Solvency II will be used to calculate a firms own regulatory solvency requirements).

So I set up a simple model (all of my models are generally so) of 5 risks and looked at the impact of varying correlation from 100% to 0% (i.e. totally dependent to independent) between each risk. The model assumes a 20% probability of default for each risk and the results, based upon 250,000 simulations, are presented in the graph below. What it does show is that even at a high level of correlation (e.g. 90%) the impact is considerable.

click to enlarge5 risk pool with correlations from 100% to 0%

The graph below shows the default probabilities as a percentage of the totally dependent levels (i.e 20% for each of the 5 risks). In effect it shows the level of diversification that will result from varying correlation from 0% to 100%. It underlines how misestimating correlation can confound model results.

click to enlargeDefault probabilities & correlations

Global Macro-Risks from IOSCO Report

The International Organization of Securities Commissions (IOSCO) released an interesting report last week, their first in an annual series, entitled “Securities Markets Risk Outlook for 2013-2014” highlighting trends, vulnerabilities and systemic risks. The four risks that the report highlighted are:

1) Low interest rates and the resulting search for yield is reawakening demand for leveraged products such as CDO´s and leveraged real estate investment funds.

2) Increased demand for high quality collateral due to higher regulatory margin requirements and central bank liquidity facilities is limiting availability of high-quality collateral and altering the balance in the system.

3) The move of OTC derivatives markets to mandatory clearing through central counterparties (CCPs) creates a challenging balancing act with a potential for systemic CCP counterparty risk.

4) Global imbalances of significant capital inflows into emerging markets after the financial crisis have been sharply reversed in recent months with the expectation that the tapering of the expansionary monetary policies in the US will begin shortly.

These are all interesting points, a number of which cover issues referred to in previous posts on this blog. As is likely obvious to regular readers, I am a sucker for graphs, and a number of the graphs that caught my attention from the IOSCO report are reproduced below.

click to enlargeCorporate Debt Issuance

click to enlargeHigh Yield Issuance

click to enlargeCDO Issuance

click to enlargeCredit Bank Debt Government Debt to GDP

click to enlargeRisk Premia

click to enlargeEquity Market Valuations

Insurance & capital market convergence hype is getting boring

As the horde of middle aged (still mainly male) executives pack up their chinos and casual shirts, the overriding theme coming from this year’s Monte Carlo Renez-Vous seems to be impact of the new ILS capacity or “convergence capital” on the reinsurance and specialty insurance sector. The event, described in a Financial Times article as “the kind of public display of wealth most bankers try to eschew”, is where executives start the January 1 renewal discussions with clients in quick meetings crammed together in the luxury location.

The relentless chatter about the new capital will likely leave many bored senseless of the subject. Many may now hope that, just like previous hot discussion topics that were worn out (Solvency II anybody?), the topic fades into the background as the reality of the office huts them next week.

The more traditional industry hands warned of the perils of the new capacity on underwriting discipline. John Nelson of Lloyds highlighted that “some of the structures being used could undermine some of the qualities of the insurance model”. Tad Montross of GenRe cautioned that “bankers looking to replace lost fee income” are pushing ILS as the latest asset class but that the hype will die down when “the inability to model extreme weather events accurately is better understood”. Amer Ahmed of Allianz Re predicted the influx “bears the danger that certain risks get covered at inadequate rates”. Torsten Jeworrek of Munich Re said that “our research shows that ILS use the cheapest model in the market” (assumingly in a side swipe at AIR).

Other traditional reinsurers with an existing foothold in the ILS camp were more circumspect. Michel Lies of Swiss Re commented that “we take the inflow of alternative capital seriously but we are not alarmed by it”.

Brokers and other interested service providers were the loudest cheerleaders. Increasing the size of the pie for everybody, igniting coverage innovative in the traditional sector, and cheap retrocession capacity were some of the advantages cited. My favourite piece of new risk management speak came from Aon Benfield’s Bryon Ehrhart in the statement “reinsurers will innovate their capital structures to turn headwinds from alternative capital sources into tailwinds”. In other words, as Tokio Millennium Re’s CEO Tatsuhiko Hoshina said, the new capital offers an opportunity to leverage increasingly diverse sources of retrocessional capacity. An arbitrage market (as a previous post concluded)?

All of this talk reminds me of the last time that “convergence” was a buzz word in the sector in the 1990s. For my sins, I was an active participant in the market then. Would the paragraph below from an article on insurance and capital market convergence by Graciela Chichilnisky of Columbia University in June 1996 sound out of place today?

“The future of the industry lies with those firms which implement such innovation. The companies that adapt successfully will be the ones that survive. In 10 years, these organizations will draw the map of a completely restructured reinsurance industry”

The current market dynamics are driven by low risk premia in capital markets bringing investors into competition with the insurance sector through ILS and collaterised structures. In the 1990s, capital inflows after Hurricane Andrew into reinsurers, such as the “class of 1992”, led to overcapacity in the market which resulted in a brutal and undisciplined soft market in the late 1990s.

Some (re)insurers sought to diversify their business base by embracing innovation in transaction structures and/or by looking at expanding the risks they covered beyond traditional P&C exposures. Some entered head first into “finite” type multi-line multi-year programmes that assumed structuring could protect against poor underwriting. An over-reliance on the developing insurance models used to price such transactions, particularly in relation to assumed correlations between exposures, left some blind to basic underwriting disciplines (Sound familiar, CDOs?). Others tested (unsuccessfully) the limits of risk transfer and legality by providing limited or no risk coverage to distressed insurers (e.g. FAI & HIH in Australia) or by providing reserve protection that distorted regulatory requirements (e.g. AIG & Cologne Re) by way of back to back contracts and murky disclosures.

Others, such as the company I worked for, looked to cover financial risks on the basis that mixing insurance and financial risks would allow regulatory capital arbitrage benefits through increased diversification (and may even offer an inflation & asset price hedge). Some well known examples* of the financial risks assumed by different (re)insurers at that time include the Hollywood Funding pool guarantee, the BAe aircraft leasing income coverage, Rolls Royce residual asset guarantees, dual trigger contingent equity puts, Toyota motor residual value protection, and mezzanine corporate debt credit enhancement  coverage.

Many of these “innovations” ended badly for the industry. Innovation in itself should never be dismissed as it is a feature of the world we live in. In this sector however, innovation at the expense of good underwriting is a nasty combination that the experience in the 1990s must surely teach us.

Bringing this back to today, I recently discussed the ILS market with a well informed and active market participant. He confirmed that some of the ILS funds have experienced reinsurance professionals with the skills to question the information in the broker pack and who do their own modelling and underwriting of the underlying risks. He also confirmed however that there is many funds (some with well known sponsors and hungry mandates) that, in the words of Kevin O’Donnell of RenRe, rely “on a single point” from a single model provided by to them by an “expert” 3rd party.

This conversation got me to thinking again about the comment from Edward Noonan of Validus that “the ILS guys aren’t undisciplined; it’s just that they’ve got a lower cost of capital.” Why should an ILS fund have a lower cost of capital to a pure property catastrophe reinsurer? There is the operational risk of a reinsurer to consider. However there is also operational risk involved with an ILS fund given items such as multiple collateral arrangements and other contracted 3rd party service provided functions to consider. Expenses shouldn’t be a major differing factor between the two models. The only item that may justify a difference is liquidity, particularly as capital market investors are so focussed on a fast exit. However, should this be material given the exit option of simply selling the equity in many of the quoted property catastrophe reinsurers?

I am not convinced that the ILS funds should have a material cost of capital advantage. Maybe the quoted reinsurers should simply revise their shareholder return strategies to be more competitive with the yields offered by the ILS funds. Indeed, traditional reinsurers in this space may argue that they are able to offer more attractive yields to a fully collaterised provider, all other things being equal, given their more leveraged business model.

*As a complete aside, an article this week in the Financial Times on the anniversary of the Lehman Brothers collapse and the financial crisis highlighted the role of poor lending practices as a primary cause of significant number of the bank failures. This article reminded me of a “convergence” product I helped design back in the late 1990s. Following changes in accounting rules, many banks were not allowed to continue to hold general loan loss provisions against their portfolio. These provisions (akin to an IBNR type bulk reserve) had been held in addition to specific loan provision (akin to case reserves). I designed an insurance structure for banks to pay premiums previously set aside as general provisions for coverage on massive deterioration in their loan provisions. After an initial risk period in which the insurer could lose money (which was required to demonstrate an effective risk transfer), the policy would act as a fully funded coverage similar to a collaterised reinsurance. In effect the banks could pay some of the profits in good years (assuming the initial risk period was set over the good years!) for protection in the bad years. The attachment of the coverage was designed in a way similar to the old continuous ratcheting retention reinsurance aggregate coverage popular at the time amongst some German reinsurers. After numerous discussions, no banks were interested in a cover that offered them an opportunity to use profits in the good times to buy protection for a rainy day. They didn’t think they needed it. Funny that.