The Evolving Landscape of Subscription Lines: Assessing Default Risks

In the first article of the series, “The Evolving Landscape of Subscription Lines”, we discussed the factors behind the explosive growth and continued evolution of capital call facilities. We also touched upon the rapidly changing fundamental characteristics that may lead to greater credit risks for lenders. We now discuss the key default risk factors to which lenders must pay attention.

Credit risks arise from two fronts

1.     Probability of default (PD) or default risk: Risk of LPs not answering calls on time and in full.

2.     Loss given default (LGD) risk: Risk of lenders not being able to fully recover money owed from defaulted LPs.

Lenders have traditionally concentrated on PD risk, often overlooking LGD risk. This is largely because defaults have historically been rare for these facilities. However, evolving fundamentals are now drawing equal attention to both PD and LGD risks (something we will discuss in a future blog in this series).

Ability and willingness to answer calls are equally important

PD credit risk factors can be grouped into two principal groups:

1.     Ability of LPs to make payment

2.     Willingness of LPs to make payment

LPs' willingness to respond to capital calls is critical, as it reflects more than just their financial capacity. Even if a borrower has the resources to repay a loan (strong ability to pay), they may still choose to default if they lack the commitment or intent to fulfill their obligations (weak willingness to pay). An LP with very little capital drawdown may have reduced willingness to answer capital calls during times of heightened volatility or poor fund performance, for example.

Key PD credit factors to assess

Figure 1 (below) captures the key PD (or default) risk factors for capital call facilities. On the vertical we split drivers into two groups. First impacting the ability of LPs to pay and the second impacting their willingness to answer capital calls.

PD risk / Collective ability to pay

The credit quality and diversification of an LP pool are key indicators of the overall credit strength of a fund’s investor base and its capacity to answer capital calls on time. Since funds will repay borrowings through capital calls to LPs, the financial robustness of the LPs offers valuable insight into the fund’s repayment ability. All else equal, an LP pool of investment-grade LPs will have a stronger capacity to answer calls than a pool of sub-investment grade LPs.

Moreover, the aggregate strength of the entire LP pool is influenced not only by individual credit quality but also by its diversity – particularly in terms of LP, industry and country concentration. Diversification lowers the likelihood that multiple LPs default simultaneously due to shared risk sensitivities. All else equal, a large and well-diversified pool will have a better chance to answer calls on time than a pool composed of a few LPs from the same industry and country. This is particularly the case in stressed economic conditions. For example, the 2020 COVID-19 pandemic disproportionality impacted the travel and hospitality industries.

The advance rate used by lenders for individual LPs within a pool is directly correlated to the extent of overcollateralization that is available to protect the facility against losses. All else equal, higher advance rates will typically be seen negatively, as the ability of the pool to absorb one or more LP defaults will be (relatively) constrained.

PD risk / Collective willingness to pay

Here the key factors are those that may motivate non-payment, in spite LPs having the financial ability to answer calls on time. This is particularly the case where there are no cross-default clauses, essentially opening the door for an LP to selectively default.

Behaviorist theories of motivation can be quite useful in understanding the roles of external rewards and punishments in influencing behavior. Hence, factors that act as the stick or (potential) punishment and factors that act as the carrot or (potential) reward are important to evaluate.

Some drivers are intuitive and direct such as penalties documented in the limited partner agreement (LPA). These include loss of share of NAV, equity dilution and other financial penalties, all which aim to incentivize LPs to answer calls on time.

Most elements are indirect and tend to arise as the fund moves through its cycle. For example, LPs are likely to continue to answer calls in cases where a significant amount of capital has already been called – this is known the sunk cost fallacy under the theory of cognitive bias in behavioral economics. The levels of GP capital commitments can serve as an indirect indicator of GP belief in the fund’s future performance – otherwise known as “skin in the game” in behavioral economics. Other factors are driven by the potential of future rewards, such as expected returns generated by historically successful GPs. The clear and timely reporting of the fund’s financial condition will also help to reduce uncertainty for LPs, further reinforcing the willingness to meet calls in a timely fashion.

Many of the factors in the willingness risk dimension have a strong behavioral or qualitative element so cannot be totally quantified. However, this should not lead to the exclusion of these factors in the assessment of default risk. On the contrary, this is the very reason why the inclusion of these factors is so important. These forward-looking, qualitative risk drivers are a cornerstone of shadow-rating based models as they capture elements of intent, culture, strategy and governance. These elements are not visible in the numbers but often determine which LPs respond positively during periods of systematic and idiosyncratic stress.

Brining it together…

At the end, no analysis of default risk is complete without looking through and combining all the above factors in a consistent and structured way. This leads to the question of importance of factors and combination protocols.

From the top down, the capacity and willingness risk dimensions should be combined dynamically, enabling a focus on the weakest-link. These mechanisms align with advanced credit practices, ensuring critical weaknesses are not masked by compensatory effects. For example, a strong LP capacity should not completely mask or wholly counterbalance the impact of diminished willingness due to very poor fund performance, which is what would occur with equal (50%) weights for each risk dimension. Furthermore, floating or dynamic weights will ensure model outcomes adjust to evolving market conditions, increasing responsiveness to real-time risk in an ever-more volatile global environment.

The same logic would also apply within each risk dimension to combine (sub) risk factors.

Watch out for the next article in the series in which we will discuss the key loss given default (LGD) risk factors for capital call facilities.