LME Perspectives: Pricing the Omni-Blocker: A New Paradigm for Pricing Non-Pro-Rata LME Protection

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In a market driven by supply and demand, everything has a price. The recent rise of the omni-blocker has cheered creditors concerned about losing value to other creditors in a non-pro-rata liability management exercise. However, this protection remains rare in the primary market, as it surfaces mostly in post-LME or post-bankruptcy paper. This is partly because demand for new loans seems to be outstripping supply, but another reason may be an inability of market participants to price this protection.

Some may ask why they should pay a 60-basis-point premium for an omni-blocker as opposed to potentially paying less for a tighter version of typical pro rata sharing protection. In our view, typical pro rata sharing protections are forever at risk of being exploited by clever lawyers. Investors should look no further than the series of events following the Fifth Circuit’s December 2024 decision in Serta. By closing off the “open market purchase” path to a non-pro-rata deal, the Fifth Circuit decision ostensibly effected a marketwide tightening of pro rata sharing protections in broadly syndicated loan documents. Yet a workaround – extend and exchange – was achieved only weeks later by Better Health and replicated shortly thereafter by Oregon Tool. With that in mind, we believe investors should focus on a protection that is more likely to hold up: an omni-blocker that expressly protects lenders’ pro rata sharing rights in the context of a liability management exercise.

With the following exercise, we aim to bridge the gap, providing investors with a rubric to use in valuing the risks associated with non-pro-rata LMEs. To be clear, we are explicitly not looking to price the risk of a default that leads to a bankruptcy or a pro rata LME, as we believe those should be compensated for by the interest rate spread on the associated debt. Instead, we are looking to price the risk associated with a creditor being treated differently from like-situated creditors – the non-pro-rata LME.
 

To begin to rectify this situation, we have leveraged our liability management data set in Credit Cloud to derive the portfolio-level risk of a non-pro-rata LME, incorporating both the risk of a transaction and estimating the amount of value lost. Credit Cloud, launched in 2022, enables complex research, screening and analysis across multiple leverage finance and restructuring data sets.

Below are the assumptions we used in our modeling.
 

The above calculations provide a first-order estimate for how much in yield investors should be willing to give up to get omni-blocker protection. Similarly, they are a simplification of how the insurance market might begin to price directors and officers liability protection for LME transactions in light of the Fifth Circuit’s excision of indemnification in its recent Serta decision, although importantly, the above estimate does not include any margin for the insurance company.

It is also possible that investors may choose to price the protection as an OID as opposed to paying for it on the run. We estimate that a 230 bps OID would compensate lenders at a similar level as paying annually for omni-blocker protection on a five-year loan at SOFR+300.

Our omni-blocker insurance model is based on a number of assumptions, several of which are driven by our LME data set now available on Credit Cloud HERE.

First, we base our calculation on a speculative grade default rate in line with the long-term average of 4.1%. We further assume that this captures all defaults including distressed exchanges, which are expected to make up 70% of the defaults, based on our expectation that this will continue to be the preferred method used by sponsors and companies.

In our LME database of transactions going back more than 10 years, uptiers have occurred about three times as often as drop-downs, and we assume this relationship will hold. We further assume that 62% of the uptiers will be non-pro-rata, which has been the average in our database since 2022. As mentioned above, we are most interested in pricing protection of non-pro-rata transactions, as we believe that investors are compensated for the risk of a pro-rata uptier through the interest rate paid on the loan.

We estimate that the average non-pro-rata uptier has moved 42% of value away from the non-ad-hoc creditors. Note that our estimate of value moved away is based on the value of the company at the time of the transaction, whereas the value loss being used here is based on a percentage of par. As such, it may be overstating the loss exposure of the lenders looking for omni-blocker protection.

For drop-down transactions, we estimate that the average transaction has moved 46% of value away from the afflicted lenders. However, we believe that unlike uptier transactions, in order for a non-ad-hoc creditor in a drop-down transaction to lose value, the company has to file for bankruptcy, as an uptier may include a coercive exchange where non-ad-hoc holders take a discount on their paper which is not a feature of a drop-down transaction. We further estimate that about 60% of companies that engage in a drop-down transaction will eventually file for bankruptcy, on the basis of our drop-down dataset, where nine of 18 companies that dropped assets down away from creditors filed for bankruptcy. We believe that 50% level is below the actual likely outcome, as several companies in our data set have only recently done their drop-downs, while the bankruptcies occurred an average of 21 months post bankruptcy.

We Are Not Calculating a Loss Triangle

We would anticipate that insurance companies looking to price protection based on these types of transactions would likely rely on loss triangles built using representative portfolios which would incorporate the timing of the non-pro-rata liability management transactions as a function of the portfolio age, which is not accounted for in our above exercise. For example, we would expect that LMEs are more likely to occur several years after the issuance of the affected debt, which could serve to reduce the cost of the insurance, as the insurer is able to invest the float for a longer period of time. Regardless, we believe the above calculations and their associated assumptions provide the market with a decent proxy for the risks that portfolios may be exposed to from liability management exercises.

Further, we would note that interested parties can look to leverage our LME database to adjust the above assumptions to calculate their expectations for the risk within their portfolios, as certain sponsors may be more apt to undertake aggressive liability management transactions while other loans in their portfolios may have stricter protections.