Key Takeaways
Defaulting on capital calls or funding failure represents the worst-case scenario for investors allocating to private assets, and the risk is compounded when private and public holdings must be managed together.
Understanding the probability of funding failure across a combined portfolio requires quantitative modeling, not estimation. Solovis® Risk provides that visibility through total portfolio asset growth simulation.
By combining Monte Carlo simulations for a liquidity sleeve and cash flow modeling for a private sleeve, Solovis Risk Pro models asset growth, the likelihood of funding failure, and asset allocation between private and public investments over time.
Life decisions sometimes require a single question: "What’s the worst that can happen?" Depending on the answer, one may continue, alter, or abandon a plan, weighing the worst-case scenario against its likelihood. This exercise can be a useful tool for managing risk.
In the world of private investments, the worst-case scenario is often associated with defaulting on capital calls or funding failure, which is essentially not having the liquid funds to pay private asset managers what investors contractually agreed to. This can spell both financial and reputational disaster for an investment plan. Risk Pro cash flow modeling can help manage this risk by projecting cash flows into the future.
While evaluating private assets on their own is useful, it is also important to consider that many investors have a public (liquid) portion of their portfolio. This liquidity sleeve can be utilized for both strategic and tactical funding, providing support to an overall investment strategy.
The core challenge is understanding the probability of funding failure in a combined private and public asset portfolio, and assessing how the changing composition of that portfolio affects the ability to maintain a desired asset allocation.
Many prudent investors utilize a liquidity sleeve when allocating to private assets. This approach provides funding potential beyond the distributions from private investments alone. Ideally, having a liquidity sleeve brings flexibility and stability to both the private investment process and an overall investment strategy.
Exhibit 1: The Role of a Liquidity Sleeve in Funding Private Assets
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Source: Solovis. For illustration purposes only.
Given the complexities of managing both public and private assets, data-driven risk assessment can be used to measure the probability of funding failure rather than relying on unstructured estimation. The modeling process can be thought of as two distinct methodologies working together:
Public Portfolio: Risk Pro uses a factor lens to generate a forecasted return and volatility. Monte Carlo simulations are then used to explore a range of possible future scenarios.
Private Portfolio: Future cash flows and net asset values are modeled using fund-specific inputs alongside calibrated model parameters.1
When considering worst-case scenarios, practical decisions must also account for the likelihood of those outcomes. By running hundreds of thousands of possible paths for the public sleeve via Monte Carlo simulations, Risk Pro can model the likelihood of funding failure for a portfolio of private and public assets.
Before examining the total portfolio, it is useful to establish context for the private side alone. In Exhibit 2, the net cash flow for a $25 million commitment to an anonymous vintage 2021 private equity fund is modeled. Since this models a single fund, a natural J-curve pattern is expected due to negative cash flows in the beginning of the period (more capital being called) and positive net cash flows later on (more distributions being made).
Exhibit 2: Historical and Projected Net Cash Flows for a Hypothetical Portfolio Allocation
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Source: Solovis.
Whether for a vintage diversified portfolio or a single fund, a negative historical or projected net cash flow indicates when a liquidity sleeve could be used to cover the shortfall. For example, in Exhibit 2, quarter-by-quarter cash flow projections are negative until Q1 2027. This means additional liquidity would be needed outside of this portfolio's projected distributions for each of those quarters.
In this example, a $5 million liquidity sleeve of public equities is used to facilitate the funding of the $25 million private asset commitment. The iShares Russell 2000 ETF is used given its higher volatility, which produces a wider range of outcomes.
Exhibit 3 shows the 99th (best case scenario), 50th (median), and 1st percentiles (worst case scenario). These outcomes account for potential growth of the liquidity sleeve as well as potential capital calls and distributions of the private asset sleeve (the relationship shown in Exhibit 1). They represent total portfolio asset growth simulation. The worst-case scenario stops in Q4 2025 when capital calls cannot be funded by the liquidity sleeve.
Exhibit 3: Asset Growth Percentiles for Public and Private Investments
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Source: Solovis.
Identifying the 1st percentile may seem like a final answer, as it reflects essentially the worst expected outcome. However, a practical interpretation of a worst-case scenario must include its likelihood.
As shown in the chart legend, 18.9% of simulations experienced funding failure, not just the 1st percentile. This is interpreted as an 18.9% probability of funding failure broadly.
To visualize this further, Exhibit 4 includes the 15th percentile. This percentile experiences funding failure, as it falls below 18.9%. However, the failure occurs nearly two years later than the 1st percentile, reflecting relatively better expected growth in this simulation. When hovering over the chart in Risk Pro, a description of expected capital calls and distributions is displayed alongside the value of the liquidity sleeve at the time of funding failure. In this example, the liquid portfolio was valued at approximately $42,000 when roughly $131,000 of capital was needed (capital called minus distributions).
Exhibit 4: Funding Failure for the 15th Percentile of a Hypothetical Portfolio
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Source: Solovis.
An allocator has a variety of choices with this information. For example, if a roughly 19% probability of funding failure is too high, funds calling for capital could be sold in the secondary market. A more practical approach may be to increase the starting value of the liquidity sleeve, or change the risk profile of the assets within it.
To evaluate the risk associated with different liquidity sleeves, Exhibit 5 analyzes the use of the iShares 0-3 Month Treasury Bond ETF as an alternative to the Russell 2000 ETF. The probability of funding failure for each is also detailed in the legend.
Exhibit 5: Evaluating How the Risk of a Liquidity Sleeve Can Affect Both Growth and Funding Failure
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Source: Solovis.
Using the Russell 2000 ETF as the liquidity sleeve yields an 18.9% probability of funding failure, but a best-case scenario ending NAV of approximately $100 million in 10 years. The best-case scenario for the short-term Treasury ETF as the liquidity sleeve results in a total portfolio value of $43 million in 10 years, but with 0% probability of funding failure. The median outcome is similar for both approaches, but the cash proxy is projected to carry no possibility of funding failure.
This represents a classic trade-off between risk and reward, now viewed through the lens of projected asset growth and the probability of funding failure for a total portfolio. Depending on the investment plan or investor risk tolerance, this type of analysis can significantly affect commitment strategies, funds set aside for liquidity, or the risk profile of the public portfolio.
The benefits of this type of total portfolio modeling extend beyond understanding the probability of funding failure. It can also help model asset allocation between private and public investments.
For example, consider a total portfolio targeting approximately 85% in privates and 15% in public investments. Using the median percentile (or whichever percentile is appropriate), Risk Pro can track when the private allocation is projected to exceed or fall short of this target.
Exhibit 6: Modeling the Changing NAV for a Private and Public Allocation Over Time (85% Private Allocation Target Using the 50th Percentile)
[Exhibit 6 image placeholder]
Source: Solovis.
Exhibit 6 shows that an 85% allocation to privates may be reached in Q1 2025, with a potential over-allocation persisting until Q2 2029.
After this period, the private asset continues its distributions and thus lowers its NAV. This highlights the importance of an informed recommitment strategy to maintain the desired private asset allocation. Without newer private funds to commit capital to, the public sleeve begins to dominate the allocation and eventually comprises nearly 100% of the total portfolio in 10 years.
Institutional investors managing portfolios that include both private and public assets face a fundamental question: what is the worst that can happen, and how likely is it? This question is central to risk management, particularly when balancing the liquidity needs of illiquid assets.
Risk Pro total portfolio asset growth simulation addresses this challenge directly. By combining cash flow modeling for private assets with Monte Carlo simulations for the public sleeve, Risk Pro enables allocators to quantify the probability of funding failure, evaluate liquidity sleeve composition, and maintain target asset allocation across market scenarios.
The goal is to equip institutional investors with the tools needed to achieve better investment outcomes. Purpose-built analytics for private and public assets enable clients to navigate the complexities of their holistic portfolios with context, transparency, and confidence.
Contact Solovis to learn more or request a demo.
1 Model parameters are calibrated using historical private market data.
References
1 Inputs can include fund start time stamp, as of date time stamp, capital committed, net asset value as of when projections start, and paid-in capital as of when projections start. Applied parameters to this data include rate of contribution, fund life expectancy, a factor describing changes in the rate of distribution over time, quarterly yield (%), and quarterly growth rate (%).