Total Portfolio Strategy: Managing Liquidity Across Private and Public Investments
Solovis Risk Pro’s Total Portfolio Asset Growth Simulation (TPAGS) allows investors to model the probability of funding failure in portfolios combining private and public assets
We walk through the hypothetical XYZ Endowment's decision-making process, showing how strategic adjustments to asset allocation, commitment levels, and liquidity can meaningfully decrease funding failure risk, in our example from 30.1% to 1.0% over 20 years
We explore the impact of incorporating capital market assumptions (CMAs) and stress testing cash flow model parameters, highlighting the impact of customized inputs in risk assessment
We illustrate the value of data-driven insights and purpose-built technology in navigating the complexities of private asset investing
Risk Pro’s Total Portfolio Asset Growth Simulation (TPAGS) enables investors to model their worst-case scenario: being unable to meet future capital calls, whether through their private-asset distributions or a liquidity sleeve (typically composed of public market assets).
In this piece, we assume the role of a hypothetical investment team from “XYZ Endowment.” Our primary focus is to investigate strategies that can reduce the probability of funding failure across our private and public portfolio. We aim to gain a deeper understanding of how our decisions impact our investment strategy, potentially leading to a shift in our approach. Additionally, we discuss ways to reflect our views by incorporating CMAs or fine-tuning cash flow pacing model inputs.
Before diving into potential decisions, let’s start with context for the XYZ Endowment model portfolios:
Private Asset Sleeve: Currently unallocated, we plan to commit $60m in capital across every vintage year from 2026 to 2031, with $10m in each fund. Our model includes future commitments using example funds found in Risk Pro’s data library, spanning private equity, debt, and infrastructure.
Liquidity Sleeve: To assist in funding our Private Asset Sleeve, we plan to use an 80/20 equity/fixed income portfolio with a starting NAV of $25m.1
Before making any changes, TPAGS in Risk Pro’s Private Asset Lab models our probability of funding failure to be 30.1% over 20 years (Exhibit 1). In the worst-case scenario (1% risk percentile), where our liquidity sleeve experiences major headwinds, we could experience funding failure as soon as Q1 2030. This risk is clearly outside of our tolerance and needs to be addressed.
Exhibit 1: Total Portfolio Asset Growth Simulation for the XYZ Endowment
Source: Risk Pro.
Our investment team aims to reduce this risk, targeting a probability of funding failure of less than 2%. However, the path to this goal isn’t clear. We’re uncertain of the relative impact of potential decisions, making it challenging to identify the most efficient strategies. Where do we start?
Our first thought is that an 80/20 might be too aggressive for our liquidity sleeve. This concern stems from our experience in 2022, when equity and bond correlations rose as markets fell. This left us believing that we need more diversification as a way to reduce risk. We’ve decided to explore how a more diversified 50/30/20 allocation (50% equities, 30% fixed income, and 20% in alternatives) might affect our probability of funding failure over the next 20 years.2
Exhibit 2 shows our liquidity sleeve adjusted from an 80/20 to a 50/30/20 allocation.
This decreased our forecasted return from 7.83% to 6.86% but reduced volatility from 13.89% to 8.82%, improving the forecasted Sharpe ratio.3
In turn, the probability of funding failure dropped from 30.1% (left) to 22.4% (right), meaning that 22.4% of Risk Pro’s 200,000 simulations failed.
Exhibit 2: Changing Our Liquidity Sleeve From an 80/20 to a 50/30/20
Source: Risk Pro.
While reducing our probability of funding failure from 30.1% to 22.4% is progress, it falls short of our expectations. Since we are not willing to change the composition of our liquidity sleeve further, let’s review some other options.
One thought was to adjust our commitment strategy: what if we reduced our 2026 vintage commitment by $5m, bringing our overall commitment down to $55m?
Our hypothesis is that less committed capital early on will reduce upcoming capital calls, and in turn the probability of funding failure. Moreover, reducing our chance of funding failure at the outset might improve our ability to overcome any subpar performance over time.
The left chart in Exhibit 3 shows where we left off, that is, 22.4% chance of funding failure.
The right chart illustrates the impact of reducing our 2026 vintage year commitment by $5m, labeled as Private Asset Sleeve V2.
This adjustment meaningfully lowers our probability of funding failure from 22.4% to just 13.3%.
Exhibit 3: Reducing Our 2026 Vintage Commitment by $5M
Source: Risk Pro.
However, this risk reduction comes with a trade-off: a slightly lower potential long-term portfolio value. Specifically, the most likely outcome (50% risk percentile) after 20 years drops from $108.4m when committing $60m in capital (Exhibit 2, right) to $107.7m when committing only $55m (Exhibit 3, right).
It is worth noting that we plan to explore further changes to our commitment strategy at a later date. Different asset classes and strategies possess unique characteristics, including varying investment periods, growth rates, and capital call/distribution patterns. Consequently, our choices of vintage year and asset classes for commitments will impact our portfolio’s overall liquidity and growth.
Another consideration we had was to increase our liquidity sleeve NAV by $5m to provide extra cushioning to meet the upcoming capital calls.4
The left chart in Exhibit 4 once again shows where we left off, at 13.3% probability of funding failure.
The right chart increases our starting liquidity sleeve NAV by $5m, ultimately reducing our probability of funding failure to below 2%.
Exhibit 4: Increasing Starting Liquidity NAV by $5M
Source: Risk Pro.
This is the kind of impact we were hoping for and is well within our risk tolerance. Moreover, we plan to reassess our model quarterly to allow for ongoing adjustments. This makes our less-than-2% probability of funding failure a conservative starting point, in our view.
Once we achieved our sub-2% goal, we realized our choices throughout this exercise would impact our asset allocation. As a result, we wanted to understand what percent of our total portfolio is expected to be in our private asset sleeve relative to our liquidity sleeve at various points in time.
In Exhibit 5, we see that in the most likely scenario the average allocation to private assets over the 20-year period is expected to be around 43.4%.
Zooming in on a specific quarter, in Q3 2032 the private asset sleeve is expected to be as high as 72.0% of the total portfolio.
Exhibit 5: Asset Allocation Breakdown in the Most Likely Scenario
Source: Risk Pro.
For this exercise, we do not currently have a strict target ratio between our private asset and liquidity sleeve. However, this data underscores the importance of monitoring our allocation expectations going forward and making sure they align with our mandate.
Applying CMAs
Having achieved less than 2% probability of funding failure, we wanted to test how our total portfolio is expected to perform under different market scenarios. For example, the 200,000 simulations Risk Pro ran for our liquidity sleeve relied on the forecasted return and volatility of 6.86% and 8.82%, respectively. These were generated using Risk Pro’s default forecasts.
However, our hypothetical team has its own CMAs for both global equities and bonds that might affect forecasted return: we believe global equities will achieve 5% annualized return over 20 years, with global bonds at 4%.
Using Risk Pro, we can apply these CMAs such that our liquidity sleeve’s forecasted return will reflect those views.
For instance, our 50/30/20 portfolio’s forecasted return drops from 6.86% to 4.96%, increasing our probability of funding failure to 3.1% (Exhibit 6).
Exhibit 6: TPAGS Using our CMAs to Generate Liquidity Sleeve Forecasts
Source: Risk Pro.
We will later decide whether it makes sense to further reduce our funding failure probability using these CMAs, but for now, this information is valuable. We have learned that our probability of funding failure does not change dramatically when incorporating our expected market performance.
Adjusting Cash Flow Pacing Parameters
Returning to our original scenario that utilized Risk Pro’s default forecasts (no user-updated CMAs), let’s examine another layer of potential model customization.
Risk Pro’s cash flow pacing model estimates future contributions, distributions, and the NAV for each of our commitments using fund-specific parameters. We can adjust these parameters to see how they might affect our simulations. For example, what if annual NAV growth for each fund will be 1% lower than we modeled? What about 1% higher?
Exhibit 7 illustrates how funding failure probability changes when the annual NAV growth rate is plus or minus 1% across all private asset funds.
We found that the impact of these different levels of NAV growth was minimal in terms of probability of funding failure.
This is just one example of the customization that Risk Pro allows for when it comes to underlying cash flow model parameters.
Exhibit 7: TPAGS with +/- 1% NAV Growth Applied to Each Private Asset Fund
Source: Risk Pro. Annual growth of all private asset vintages were increased or decreased by 1% from Risk Pro’s calibrated values.
Navigating the complexity of private asset portfolios demands tools that can model uncertainty at scale and surface actionable insights without sacrificing rigor. Risk Pro meets that need, delivering institutional-level quantitative analysis in a clear, accessible, and actionable way.
This capability is built into Risk Pro’s Private Asset Lab, where users can dissect how individual components and decisions might impact funding failure risk and asset allocation over time. Investors can also adjust model inputs for both the private and public sides of a portfolio separately, with just a few clicks. This flexibility supports more informed asset allocation decisions and greater confidence in tackling private-asset complexity with data-driven insights.
Investing in private markets demands greater resources and expertise compared to traditional assets. As private assets grow in importance for both institutional and non-institutional portfolios, we believe their complexity underscores the value of intuitive, purpose-built technology to facilitate decision-making and scale.
1 Liquidity sleeve consists of the iShares MSCI ACWI ETF as the equity allocation and iShares Core US Aggregate Bond ETF as the fixed income allocation, rebalanced to 80%/20% weights on a quarterly basis.
2 50/30/20 liquidity sleeve is modeled using iShares MSCI ACWI ETF as the equity allocation, iShares Core US Aggregate Bond ETF as the fixed income allocation, and HFRI Macro (Total) Index as the alternatives allocation, rebalanced to 50%/30%/20% weights on a quarterly basis.
3 It is also worth noting that when implementing forecasts throughout this piece, we assume that the recent cash return of 4.32% will continue into the future.
4 For this hypothetical example, assume that we source this $5m from a separate cash reserve.
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