Alter Domus is proudly sponsoring LSTA’s Annual Conference in New York on October 12. Connect with our team at the conference to learn how evolving risk assessments and new technologies are reshaping loan markets. Our attendees at the event include:

• Greg Myers
• Juliana Deblois
• Lora Peloquin
• Pete Himes
• Tim Houghton

Interested in discovering how our expertise and systems can bring clarity to your loan portfolio? Come and meet us at out booth!

What are some of the key considerations when identifying the right service operating model for your company?

Your operating model and managed services provider need to be able to accommodate your future growth plans. If you are considering moving into new jurisdictions, asset classes or strategies, they need to be able to flex accordingly to support that next step for your company. Crucially in today’s data-driven environment, you also want to think about your data and technology needs. Investors are demanding real-time access to information and transparency. Do you want to take on the cost and responsibility of building and maintaining the capability to provide that in-house? Many asset managers are engaged in M&A activity, which is a logical moment for a fundamental rethink of your operating model.

How is traditional outsourcing changing?

The need to access data is driving change – for the better in our view. We’re moving away from a commoditized and transactional type of model towards operationally integrated partnerships, where there’s transparency and access to data in real-time. We’re also seeing some consolidation and rationalization of partnerships. Where perhaps a manager might have had multiple fund administrator partnerships in the past, now they might have one or two deeply embedded partnerships that can cover all the jurisdictional and sector specialisms they need globally.

Co-sourcing is a relatively new concept. What is it and why might firms consider it?

Essentially, co-sourcing is an operating model where the manager maintains an in-house data and technology stack that their administrator has access to and can create and modify primary data elements. It’s a hybrid model between fully outsourced and fully insourced. The benefit it offers managers is that it allows total control and ownership of their data and real-time access to it, while tapping into the asset class and systems specialists, and talent acquisition capabilities of a fund administrator, all while reducing manager level overheads.

Beyond co-sourcing, in what circumstances might a full lift-out be the right solution for a company?

That partly depends on whether, as a manger, you have the scale and appetite to reinvest in your own technology and in-house operations or not. There are considerable advantages to partnering with a provider who constantly upgrades their technology platforms and can provide a long-term career path to valuable internal resources. There are also the economies of scale and best practices that a global administrator can offer, without being distracted by the challenges of maintaining a back office. We’ve seen great success for both clients and personnel as we’ve created a playbook to successfully assist with these types of full lift-out transitions.

With this evolution in mind, what should a company be looking for when choosing a service provider?

Ultimately a good administrator is focused on white-glove levels of service and forming a deep partnership with their clients, which will include customizable solutions and specific asset-class expertise that meets specific needs. An administrator should be viewed as a critical member of the team, who when leveraged correctly delivers significant value-add to portfolio, risk management, and investor teams. Critically, you need to have confidence that they are technologically innovative, as well as culturally a good fit for your organization.

This article was originally published in Preqin's Service Provider Report.

What do you think are the factors driving the incredible growth in distressed debt and special opportunity funds?

First, there’s the legacy effects of a long-term zero interest rate environment, and the proliferation of dividend distributions from a lot of LBOs, especially from the sponsor finance community, or private credit funds. They were done when rates were low – one floor or two for reference rates – and now it’s ticking up to the five range.

And with these legacy spreads and the current reference rates, some of these companies can’t afford that debt service as part of their operating model. That’s starting to trigger a lot of the EBITDA covenants within their underlying credit and lending agreements.

So we’ve seen a lot of our traditional private credit lenders and opportunistic managers launching special situations and credit opportunity funds, where they can step in, restructure the debt, and maybe put it on non-accrual or non-cash pay for a period of time to work these deals out. There was a bump in these funds being formed at the beginning of covid, with the assumption the pandemic would create a boom in distressed situations for the then pending economic distress.

However, due to all the government stimulus, that boom was delayed. But with the prolonged increase in rates, even with the continued economic performance, a lot of these managers are expecting that boom to commence. There are also situations like the collapse of Silicon Valley Bank that suggests there will be interesting portfolios coming to market, priced to be offloaded quickly and able to be worked out at significant returns to investors.

Do you think that same environment is fueling a rise in asset-based lending funds?

Traditional asset-based lending is typically lending where there’s a lag time between when corporate borrowers need to finance their commercial operations and bridge the period of time that their customers are paying them for the product that’s been delivered.

Up until recently, that’s been the world of a money center bank, or a super-regional money center bank that have these facilities where they will make those loans, monitor those loans and pledged collateral, and keep that relationship with a borrower. But given the ultra-sensitivity of those super-regional bank market events, those are really good loans to shed because they have high market value, without the bank to reserve against them.

So we’ve seen a number of those portfolios come to market where it’s private capital that will take on those ABL facilities on behalf of the borrowers at a pretty good rate from the original bank lender.

And then there’s the role of the traditional investment bank on providing portfolio leverage, which we now see large insurers and actual funds coming in to replace them, despite all the compliance issues and strict rules around what’s applicable, what’s admissible, and substitution rights if a particular asset goes wrong. This is now becoming the realm of large insurers, since they have a more permanent capital base, one that isn’t based on deposits.

We’ve had a few clients entering into lending or refinancing arrangements, and they really liked the term loan and the borrower. The borrower then brings up the fact that they also have this ABL and would like to have the same provider for both.

So the manager decided to meet that market need, and as a result, we ended up exploring what we could do to service them, and licensed a product dedicated to the ABL space that provides transparency to the lender, the borrower and us though the operating infrastructure.

For managers looking to launch their first credit fund to take advantage of this environment, how should they think about the operational infrastructure to administer it?

When I speak with PE managers that are used to underwriting and investing in a portfolio company and valuing their portfolio once every quarter, they’re in for a very different level of activity in the credit space. The same underwriting process and the ongoing valuations occur, but additionally the bank debt pays at a minimum quarterly, and the rate resets typically quarterly. There are amortisation payments. Loans are typically originating below par. So they’ve got non-cash income that they need to recognise.

These deals get amended constantly, so there could be different compliance rules under the credit agreements. Furthermore, the maturities get extended, the size of the deal could move up and down, and all this requires a great deal of monitoring of the underlying borrower. And they need a system that will address and support all those things.

They have to decide who will be the administrative agent on the credit, whether it’s done internally, or outsourced completely.

Then there’s SEC oversight around the custody of investor assets. How are they going to build an infrastructure where they’re not co-mingling investor monies across multiple funds or different borrowers and everything else required to withstand the scrutiny of the SEC? And that’s just on the legal and operational side of things.

As a result, our clients invest a lot of resources on attorneys, compliance experts and our services because we have the appropriate systems for the agent components, the loan administration, which is tracking and ticking and tying all the cashflows, positions, rate resets, amortisation schedules, and then ultimately the fund accounting and investor reporting. Because a direct result of this growth in private credit is there is a dearth of people that know how to do credit accounting because it is very different than PE, or fund-of-funds accounting.

This ends up producing a massive amount of data to monitor and manage. The front office wants credit monitoring. The middle office needs to monitor the compliance with the credit agreements. And then the back office needs the data to produce the reports and everything else. There are big ticket systems available that cost millions to implement or off-the-shelf systems that support various functions for credit managers.

There are much lower cost solutions for data warehouses where they can build report writing software on top of the warehouse – these become a kind of integral hub for the spokes that go out to address reporting requirements. And then there are other inexpensive add-ons that can offer portfolio view technology as well.

Most clients want that data in-house, but it’s a daunting task to build internally. This is why we’re confident that outsourcing will continue to offer a compelling value proposition for the GPs looking to make the most of this particular moment in the credit markets.

This article was originally published in PDI's US Report.

We’re delighted to sponsor Debtwire’s Asia-Pacific Forum in Hong Kong on 10 October. Scott Reid, Albert Sugianto, and Jamie Loke will be attending the forum in person to examine the latest trends and developments shaping credit and distressed debt opportunities in Asia Pacific. The forum will spotlight Scott Reid who contribute to the “Asian Credit: The State of Play” panel at 9:35HKT.

Let’s talk about your credit ambitions in Asia Pacific! Schedule a meeting with a member of our team to learn how Alter Domus is helping managers seize credit opportunities in the region.

Angela Summonte is attending Pension & Investment’s World Pension Summit in The Hague from 10-12 October. She will join a range of pension fund executives and other industry experts to discuss the best practices and key strategies driving growth for pension funds around the world. Meet her at the conference to learn more about how changes across technology, financial markets, the environment, and society are transforming opportunities, and how Alter Domus is supporting the evolution of the sector.

Get in touch with Angela ahead of the summit to find out how Alter Domus’ Asset Owner Solutions can support your ambitions.

We’re delighted to be sponsoring the LMA Syndicated Loans Conference in London on 26 September 26.

Alter Domus’ Joe Knight, Juliana Ritchie, Lora Peloquin and Amit Varma look forward to meeting you at the forum to discuss the latest trends and developments shaping investment opportunities across today’s global debt capital markets.

If you’d like to find out more about how Alter Domus can support you in the syndicated loan and debt capital markets space, stop by our booth to discuss with our experts.

The US Broadly Syndicated Loan market (“BSL”) is a proven asset class with a track record of over 30 years (from the earliest leverage loan ‘prime rate’ mutual funds). CLOs are significant investors in this $1,5tn+ market. Other investors include regulated mutual funds and ETFs, and private account investors.

While BSLs offer high yields and could be attractive under an environment of increasing interest rates, they also come with several risk factors that can affect their pricing and complicate the management of a portfolio of BSLs.

Credit risk, interest rate risk, market risk, and prepayment risk are several key factors that BSL managers consider. In this article, we’ll explore the drivers of leveraged corporate loan prepayments and how BSL portfolio managers (whether it is a CLO, ETF, open end mutual fund, or private account) can potentially improve their performance and better manage investor expectations by using more sophisticated quantitative models.

Specifically, we’ll examine some of the key factors that would drive critical quantitative models to estimate prepayment risk and how these models can help BSL managers make more informed decisions to improve investment performance. 

Why prepayments matter for investors

The benefits of accurately anticipated prepayments in a BSL portfolio could be significant for investors in terms of better performance, lowers costs and more efficient portfolio management. A few examples of how a market participant could benefit from a model driven approach to better anticipate and manage prepayment risk include:

• Managing prepayment cashflows can allow BSL managers to better prepare for their next investment decisions (e.g., complying with reinvestment criteria, avoiding incurring transactions costs to meet a purchase commitment) or to better manage investor redemptions – as in the case of open-end mutual funds.
• Any time a prepayment occurs, the loan’s correspond rating bucket will make up a lower portion of the total portfolio (this is especially relevant for CLOs where ratings criteria are critical drivers for ensuring compliance with trading rules). This balance shift can have downstream consequences on whether the portfolio manager can meet the ratings portfolio criteria (i.e., WARF, Caa/CCC limit). Being able to anticipate the distribution of prepayments by rating buckets can allow a manager to better plan for these risks and enhance their ability to preserve the credit risk profile of a fund/portfolio.
• Prepayment modeling can complement the underlying investor’s expectations of returns and cash flows. For example, in static private accounts or for vehicles that are no longer in their reinvestment period, the portfolio prepayments will have a significant impact on the underlying cash flows distributed to investors.
• Proper management and projections of prepayments will assist managers to more efficiently redeploy those funds to minimize negative carry. Negative carry could have a significant impact on a BSL portfolio’s returns.
• For funds that are part of a larger fund (some CLOs and SPVs for example are consolidated into a larger fund) can incorporate prepayment management into the asset-liability management of a parent fund.
• As BSL investors increasingly demand sophisticated and quantitative approaches before investing in a particular fund, a manager that utilizes cutting-edge methods increases their opportunity of raising funds and meeting investors’ expectations – above and beyond direct links to better performance, such as market best practices, surveillance, forecasting, etc.

Factors that can influence loan prepayments

BSL prepayments are driven by a host of factors. Our analysis shows that the most pertinent fall into three categories – (i) Age of Loan, (ii) Loan Spreads and Prices, and (iii) Loan’s Recent Prepayment Activity.

Age of Loan

The first significant driver is the number of months-on-book. Recently originated loans are highly unlikely to prepay. However, as prepayments continue to increase linearly with time, there are some nuances to consider.

The linear trajectory of prepayments may have a different slope depending on the loan’s term structure, with shorter-term loans having steeper slopes. Additionally, towards the end of a loan’s term structure, there is a substantial increase in prepayment as loans get closer to their final repayment.

Loan Spreads and Prices

Loan Spreads – at the loan level

The loan’s spread is another factor worth discussing. The loan’s spread can influence prepayments in either direction. A higher spread can indicate the credit worthiness of a borrower who must allocate more cash to consider a prepayment, making doing so more challenging. However, higher spreads can also increase the borrower’s incentive to prepay, as the potential to reduce interest payments is higher.

This can especially be the case where a borrower’s credit profile has improved. Our findings suggest that higher spreads have a positive relationship with prepayment, indicating that the benefits of prepayment outweigh the costs of higher rates.

Market Loan Spread

However, loan spreads don’t affect the loan in isolation. The average spread of other loans on the market is important. The lower the average market spread, the higher the current loan’s propensity to prepay holding all else equal. This relationship suggests that when spreads are lower elsewhere, borrowers attempt to prepay either to refinance or partially take advantage of lower spreads elsewhere.

We found that when the broader market’s spread is included in a model, the current loan’s spread exhibits a stronger effect. This change suggests that the difference between the two spreads plays a key role in driving prepayments. However, this relationship becomes complicated once the next factor – Price – is introduced into the model.

Loan Price – Current

A loan’s current price is a strong predictor of prepayment probability. More specifically, a loan has a marked increase in prepayment rates if its price is between 99 and 100.5, with the peak occurring at 100. A very large proportion of all prepayments occur when the price of the loan is within that range.

Furthermore, certain factors including the loan’s spread and the market spread become irrelevant once price is controlled for – this is not surprising since the specific loan facility spread and market spreads are generally considered in the price. Thus, even though spreads likely play an important causal role in driving prepayments, the market is accounting for loan spreads and market spread when pricing loans.

Thus, when trying to predict prepayments, the loan’s price carries a good degree of information available in other loan characteristics and will generally dominate many other, though not all, explanatory variables.

Loan Price History

Another factor that matters for a loan’s prepayment activity is the loan’s price history. If a loan is priced at 100 because it was recently issued, one wouldn’t expect that loan to prepay. However, once the loan has been on the books for some time and experienced price fluctuations, then its higher price becomes more meaningful.

Recent Loan Repayment Activity

A loan’s recent behavior can affect the loan’s propensity to prepay in one of two ways. A borrower could have just made a substantial prepayment and consequently have little capacity to prepay for some time. Or they could stagger their prepayments across multiple periods. We found the latter to happen more often. Thus a loan’s most recent period’s prepayment indicates a higher likelihood to prepay again.

A few words about private debt prepayments 

In this paper we provide some insight into prepayment modeling for BSLs, and we find that a very powerful indicator of prepayments in the price of the BSL. For pure private lending, however, market prices are not readily available. In such cases more modeling around fundamental prepayment drivers – such as economic variables, broad market variable or even underlying company financial statements – would need to be explored and analyzed.

Alter Domus has performed extensive fundamental prepayment analysis for private loan portfolios. Our Alter Domus Risk Modeler provides the platform to perform and deliver such analytics. Risk Modeler is also applicable to the broader BSL marketplace for advanced analytics, including prepayment.

Conclusion

The large, established and growing BSL market presents both opportunities and challenges for underlying investors and their respective portfolio managers. Prepayment risk is a critical factor to consider, as it can impact a BSL’s portfolio performance. By employing a model-driven approach to managing prepayment risk, BSL managers can better plan their investment decisions, maintain the risk profile of a fund, manage investor cash flows (including redemptions) and better cater to investor expectations.

Key factors that influence prepayment activity for BSLs include months-on-book, loan and market spreads, current and historical loan prices, and recent prepayment behavior. By understanding these factors and their relationship with prepayment probabilities, BSL managers can make more informed decisions to optimize their portfolios. For private loan investors, modeling and managing prepayment risk may be a little more challenging given the lack of observable market prices. However, more fundamental techniques around prepayments is a field where Alter Domus has significant expertise and capabilities through our alter Domus Risk Modeler platform.

Embracing advanced quantitative models and keeping abreast of factors affecting loan pricing and prepayment risk will ensure that BSL managers can successfully navigate the complexities of the market, ultimately benefiting both the portfolios they manage and their investors. While the insights above based on aggregated market data can provide value to Alter Domus’ clients, a deep dive into an individual portfolio can significantly improve a manager’s ability to better manage prepayment risk. 

We’ve heard loud and clear from our customers that their ability to continue to compete and succeed in the alternatives marketplace is increasingly dependent upon their capability to integrate advanced technologies into their data management and decision-making processes. This was the driving factor behind our acquisition of Solvas from Deloitte in May of this year.

Empowering insight and client growth

Having Solvas’ best-in-class product suite as part of our operating model enables us to support and accelerate the growth of our partners like never before.

Backed by experienced, specialist teams, these scalable, flexible software solutions provide clients with a clearer, holistic view of their portfolios, helping them to get ahead of regulatory change and to better manage risk for a more resilient future.

Digitize Intelligent automation of data extraction and document management for CLOs.	Portfolio Multi-asset class portfolio administration and reporting solution for asset managers.
Accounting Financial accounting and reporting software package – the only solution built purposely for the loan space.	Compliance A rules-based compliance engine for advanced risk monitoring, providing clear calculations for CLOs compliance limits.
ALLL+ Risk modelling and analytics to support CECL and IFR9 accounting regulations for banks, insurers, and credit unions.	Risk Modeler Risk analysis software with advanced modeling capabilities for financial institutions.

Please do not hesitate to contact us at [email protected]. if you have questions or would like to learn more about how our data and analytics services can help support your business.

We are proud to announce that Alter Domus has won the award for Best Debt/Loan Administrator at Private Equity Wire’s US Credit Awards 2023. Held in New York City on June 22nd, the award ceremony recognizes fund performance and service provider excellence across credit funds in the United States.

Alter Domus was selected as the winner of the “Best Debt/Loan Administrator” based on a widespread survey of more than 100 credit fund managers.

We’re thrilled to have been recognized by our credit clients for outstanding service. This is one of many recent awards the US private debt team has won, and we’re incredibly proud of the added value we’re providing our clients day in and day out. A big thank you to everyone at Alter Domus North America for helping to make this achievement possible!

Tom Gandolfo, Head of Sales & Relationship Management North America

Alter Domus has previously demonstrated through a simple example that total credit losses from an underlying collateralized loan obligation (CLO) portfolio would equal to the total credit losses across the CLO’s notes (including equity). In that same research Alter Domus pointed out, however, that CLOs typically include structural features where excess interest, which would otherwise have been distributed in traditional portfolios, such as bank balance sheets or insurance company general accounts, can be diverted to provide credit support for CLO tranches upon an economic downturn.

This diversion of excess interest income means that total portfolio credit losses are reduced by the amount of excess interest diverted. In particular, the formula in estimating CLO tranche credit losses, as a function of the portfolio credit losses, is: Losses Allocated to CLO Tranches = Total Portfolio Credit Losses – Excess Interest

In this article, we expand our previous research by conducting an analysis to assess the impact of the relative benefit of excess interest, which is highly dependent on the overcollateralization (OC) test, as it translates to the reduction in the expected loss (EL) of the underlying loan portfolio.

Specifically, the purpose of our research is to estimate the ‘Excess Interest’ from the formula above. As expected, we find that excess interest can mitigate credit losses in the underlying portfolio, but the extent to which is sensitive to several variables, such as the average credit quality of the portfolio, the available amount of excess interest over the life of the CLO, and portfolio weighted average life (WAL).

The results of our research indicate that a reasonable range of the reduction in portfolio EL can be from 6%-14%, with an 11% average, depending on the level of ‘stress’ one wants to apply in their analysis. Other factors, such as prevailing market conditions, could also impact this range. Based on our findings, we could rewrite the formula above as follows:

• Low Value: Losses Allocated to CLO Tranches = 86%* Total Portfolio Credit Losses 
• High Value: Losses Allocated to CLO Tranches = 94%* Total Portfolio Credit Losses 
• Average Loss: Losses Allocated to CLO Tranches = 89%* Total Portfolio Credit Losses 

A review of the OC test

CLO market observers understand that a critical feature in CLOs is the benefit of excess interest generated by the underlying CLO portfolio to provide credit support for the CLO tranches. But the benefit of excess interest in reducing the EL for CLO tranches is highly dependent on several variables.

A critical structural feature in CLOs that helps ‘trap’ this excess interest to support the CLO tranches is the OC test (note that CLO structures will typically have several OC tests at various points in the waterfall – in this paper the singular term ‘OC Test’ refers to all OC Tests collectively).

With no OC Test, the benefit of excess interest will be limited because most of the excess interest will leak out to the equity investors – this would mirror to a certain extent the treatment of excess interest for a portfolio of loans on a bank’s balance sheet or an insurance company’s general account, where the income could be used for other purposes by those institutions and not trapped and secured for the benefit of the CLO noteholders.

OC tests are generally incorporated within the CLO cashflow waterfall. Upon a breach of such test, they are intended to divert available interest proceeds first, then principal proceeds if needed, to pay down the notes in order of priority until cured. Typically, there is a test for each class of notes except for the class that was initially the senior most. In that case, any proceeds diverted are at the expense of the equity including the relevant junior notes depending on which test is breached.

The OC test is usually calculated based on the ratio of the total par amount of the underlying portfolio (plus principal proceeds) over the outstanding amount of the relevant class of notes (including those that are senior to it). However, there are adjustments in the form of haircuts that are commonly applied to the portfolio.

These typically include those that are classified to be in default or purchased at a deep discount as well as the total amount of those rated Caa/CCC in excess of a predefined limit. OC tests are more likely to be breached upon credit losses and/or excessive negative credit migration.

It is important to note however that the ultimate benefit of this feature can depend on many factors as CLOs are complex and the behavior of the economic environment is uncertain. Some examples of these factors include:

• Available excess interest (e.g., weighted average spread of underlying loans, cost of debt across the CLO notes, behavior of reference rates)
• Amortization profile, prepayments (i.e., introduces reinvestment risk)
• Timing of defaults (e.g., front loaded defaults may trigger tests earlier)
• ‘Tightness’ of OC test trigger levels (e.g., lower trigger levels may reduce effectiveness of tests)
• Portfolio manager trading behavior (e.g., can increase – or decrease – par amount of portfolio through substitutions particularly when an OC test is failing marginally)

Assessing the benefit of the OC tests – a simple analysis 

We have just described the basic mechanics of how OC tests work and highlighted a few examples that can impact their benefit, several of which are quite uncertain and challenging to forecast. Therefore, we choose to control for most of those variables and start from a simple stylized CLO as the intention of our analysis is to demonstrate the economic benefit of excess interest.

The benefit from this form of potential credit enhancement can be explained as to the reduction of the underlying portfolio EL. The analysis will also include an examination of the sensitivity while changing several variables that are straightforward.

To begin, we created a stylized CLO by reviewing a sample of recent CLO structures. The objective of this step is simply to assess how much excess interest is generated in a CLO structure. However, it is safe to say that excess interest is a prerequisite for any CLO structure.

Although the amount of excess interest would change over time based on market conditions, we could generalize that a CLO structure would not be issued when market conditions do not offer sufficient excess interest. See Tables 1-3 for assumptions made for our hypothetical CLO.

As we had noted in previous research, the collateral quality test matrix, typically incorporated in CLOs, is a clear indicator of the benefit of excess interest – the EL of the CLO notes would not be expected to materially change even as the credit risk of the portfolio increases because of the reduction in losses resulting, for example and most commonly, from an increase in excess interest.

The Matrix Scenarios provided in Table 3 were calibrated so that the tranche ELs of our hypothetical CLO were relatively consistent in each of the designed scenarios. CLO matrices typically provide many more options than the modeled scenarios presented here.

The portfolio is assumed to be static, consisting entirely of floating rate first-lien senior secured loans. Recoveries are also assumed to be realized immediately upon a default.

Table 1: Hypothetical Capital Structure

Table 2: Subset of Collateral Quality Tests

Table 3: Collateral Quality Test Matrix

With this stylized CLO, we assumed that the reference rate is 0%. This assumption allowed us to get a clearer picture of the excess spread that could be applied to reduce the portfolio EL. As reference rates rise, the amount of excess interest, depending on a given scenario of losses, can increase thus potentially offering greater credit enhancement.

For our analysis, we ran our model for each matrix scenario under a range of different stresses. We also ran various combinations of default rates, recovery rates and portfolio WAL for each of the matrix scenarios. The results of the various combinations are shown in Tables 4 and 5.

For each of the four matrix combinations, we ran the CLO cash flows assuming various levels of default rates (historical averages based on ‘idealized’ levels) and stressed default rates. We also ran two different recovery rate scenarios – again stressed and closer to historical averages.

We also ran two combinations of portfolio WAL. This broad range of assumptions allowed us to study the sensitivity of the excess interest in reducing portfolio EL. The column labeled ‘% Reduction of Portfolio EL – Note Paydowns Only’ represents the probability-weighted amount of interest proceeds that are diverted to pay down the notes, excluding any payments to the first-loss equity tranche. We believe that this number is a reasonable estimate of the portfolio EL reduction attributed to excess interest.

Table 4: Portfolio WAL of 7 years

As we mentioned earlier, the collateral quality matrix offers the portfolio manager to choose amongst different combinations of portfolio characteristics (e.g., average rating, WAS, diversification) while maintaining the credit quality of the notes. In other words, and as we had noted in our previous research, the collateral quality matrix is the proof and our guide in trying to assess the reduction in portfolio EL resulting from excess interest.

In Table 4 we note that for the four different scenarios, the average reduction in Portfolio EL attributed to the excess interest ranges from 6.4% to 17.1%. As expected, the percent reduction of the portfolio EL increases as the portfolio EL increases within each set of matrix scenarios. Indeed, this result is also indicated by the percent of the total interest proceeds that is diverted to pay down the notes due to the OC test. In the matrix scenarios presented here, the ‘required’ WAS, an indicator of excess interest, of the portfolio increases as the average credit rating decreases.

It is also worthwhile noting that the proportion of excess interest that is distributed between the equity and what is diverted due to the OC Test over the life of a CLO is subject to uncertainty, some examples of which we highlighted earlier. However, it can be said that holding all else equal, a greater proportion would be expected to be diverted, the higher the OC trigger level. In which case, it would lead to a further enhancement against the portfolio EL.

Table 5: Portfolio WAL of 5 years

Comparing across the results within Table 5 show a similar behavior as in Table 4. However, when comparing the results between the two, notice that both the percent reduction to the EL and the percent of the total interest proceeds that are diverted are lower. This can generally be explained by a combination of lower portfolio ELs from lower probabilities of default and that the expected amount of available excess interest over the life of the CLO is lower, both due to the shorter duration of the portfolio. The range of portfolio EL reduction due to excess interest is 5.1%-10%.

Nonetheless, the results from the tables above are compelling in demonstrating the case that excess interest reduces the portfolio EL and provides our first estimate of the ‘Excess Interest’ in terms of reduction of the portfolio EL.

Based on the assumptions above, and for the broad range of different model inputs, we found that excess interest can reduce the portfolio EL in the range of approximately 6%-14% with an average portfolio EL reduction of about 11%. We derived this range by simply averaging and rounding the ranges of the two different tables.

We have also shown that it is quite challenging to derive a precise point estimate of the benefit of excess interest given the complexity of the analysis, the various assumptions that could be made and changing market conditions. As a result, we provide our views of a reasonable range of the benefit of excess interest in reducing portfolio losses. Essentially, we attempt to answer the following question:

‘What is the benefit from Excess Interest based on the formula below?’

Losses Allocated to CLO Tranches = Total Portfolio Credit Losses – Excess Interest

We believe that, based on our example, a reasonable range could be somewhere from 86% to 94% of the gross portfolio EL as compared to assuming no excess interest in the CLO. 

A few notes

We have noted that our analysis included certain assumptions in order to demonstrate the impact of excess interest in reducing portfolio EL. Beyond the assumptions that have already been noted, other considerations that could impact our results include:

a. We assumed that none of the equity distributions reduce portfolio EL. This is a conservative assumption but given the complexity in trying to  estimate the composition of equity distributions between interest and principal are beyond the scope of this paper. Either way, the equity tranche assumes the bulk of the portfolio EL anyway – as demonstrated in our previous research paper.

b. Our hypothetical CLO – see Tables 1-3 – was created based on relatively recent market conditions. Hence, the relatively large spreads on the notes (and to a certain extent, the portfolio). Under different market conditions, the results of our findings could be different. But generally, with narrower spreads on the notes, the benefit of excess interest increases and thus further reducing portfolio EL.

c. We used a limited number of matrix combinations. Using a broader and finer range of matrix combinations may show different results under different collateral quality test combinations.

Conclusion

In this article, we have assessed and demonstrated the credit enhancing benefit of excess interest within CLOs as they typically incorporate a key structural feature, the OC test. In the event the OC test is breached, excess interest, which would have been distributed to equity, would be available to pay down the most senior note until cured.

Our analysis was based on several assumptions applied to a simple hypothetical CLO structure. Several scenarios were applied to observe the relative impact while changing several variables: the average credit quality of the portfolio, the available amount of excess interest over the life of the CLO, and the portfolio WAL.

The metric used to measure the relative benefit was based on the reduction in the EL of the underlying loan portfolio. As expected, we found that excess interest proceeds generated from the portfolio reduces the EL on the CLO tranches in all cases.

The results show an average reduction in portfolio EL of about 11%, though this amount is subject to a limited range of assumptions.

Although the results indicate that the impact can be sensitive, it is nonetheless clear and not surprising that this key feature of trapping excess interest, at a minimum, can provide additional credit enhancement.