Liquidity Risk Management for Consumption Foundations

Institutional investors with long-dated liabilities and illiquid allocations face a structurally elevated liquidity risk, especially when capital calls or liability payments coincide with market stress and depressed asset prices. The methodology described below builds a statistically grounded, vintage‑diversified capital‑call model that allows such investors to set liquidity buffers to avoid forced sales while limiting unnecessary cash drag.

Background and Motivation

Large institutional investors with defined payout profiles and no dependable external inflows (foundations, sovereign funds or endowments) typically finance their objectives through multi‑asset portfolios that include public and private investments. In market downturns, falling liquid asset values, scheduled liability payments and capital calls from private‑market funds can amplify creating acute funding pressure and the danger of forced asset sales at crisis valuations [Franzoni et al. 2012].

During the Global Financial Crisis, distributions fell sharply and capital calls rose, creating liquidity gaps for limited partners. Capital calls may spike exactly when public markets fall or uncertainty rises [Maurin et al. 2023]. Empirical work on past crises shows that capital‑call patterns can deviate from long‑run averages around large drawdowns, and that distributions from private equity tend to decline sharply at the same time [O’Shea 2025]. For liability‑driven investors this implies that liquidity risk management cannot rely solely on historical averages but must consider stressed scenarios and asset‑liability‑management (ALM) interactions.

Liquidity risk in ALM

In the ALM context, three sources of liquidity demand dominate: recurring operating expenses, contractual liability payments and uncertain capital calls from illiquid commitments. Expenses and liabilities cash flows can usually be projected over many years and pre‑funded through the liquid part of the portfolio, where typical settlement periods of a few days allow timely rebalancing.

Best practice is to maintain a forward‑looking liquidity ladder that matches high‑certainty outflows, pre-plan buffers and regularly stress test liquidity. However, while such tools work well for planned cash flows, they are less suited to capital calls whose timing and size are inherently stochastic and manager‑specific.

Data and J‑curve modelling

Private equity funds are well known to follow a J‑curve pattern: in early years, capital calls dominate and net cash flows are negative, whereas distributions from exits and recapitalisations usually materialise only after several years [Cumming et al. 2005].

To incorporate this in the analysis, a broad panel of net capital flows from the Preqin database is used, which provides fund‑level cash‑flow data for private equity, private debt, infrastructure and real estate funds across vintages and regions (Source: Preqin, a part of BlackRock) [Preqin, 2025]. Funds are then aligned by vintage and fund age and aggregated across strategies, enabling estimation of the empirical distribution of net capital flows (capital calls minus payouts) by vintage and fund age (Figure 1). The resulting empirical profile resembles the stylised J‑curve: net outflows peak in the first three to five years after fund inception and gradually turn neutral and then positive as distributions dominate. For investors not diversified across all private asset classes, separate calibrations are advisable because the slope and timing of the J-curve can differ materially across strategies.

Vintage Diversification

A simple application of the single‑fund J‑curve distribution to an institutional portfolio would overstate the required liquidity, because it ignores diversification across vintage years. Historical research shows that private‑equity performance and cash‑flow profiles vary strongly by vintage, reflecting cyclical entry valuations and exit environments [Rudin et al. 2019]. Diversifying commitments across many vintages helps smooth these cycles and mitigates timing risk.

To capture this effect, pairwise correlations of annual net capital flows between different vintages are estimated, resulting in a vintage‑by‑vintage correlation matrix (Figure 2). While flows in adjacent vintages are positively correlated, reflecting shared macro conditions, correlations decrease markedly as vintage distances increase, consistent with the view that different fund cohorts experience different phases of the business cycle.

An illustrative simulation considers a stylised investor that commits the same amount to a new vintage each year, thereby building an equally weighted stream of vintages over time (Figure 3). For such a strategy, aggregate net capital flows remain negative in early years but improve as the portfolio matures. Already with nine active vintages the expected net distribution of the total programme becomes positive, and with eleven vintages even the 95 % value‑at‑risk of net flows turns positive while the 99 % quantile of net outflows remains small. This demonstrates quantitatively how vintage diversification transforms the liquidity profile of an illiquid portfolio from one dominated by drawdowns to one that, in the long run, is a net liquidity provider to the institution.

Implications for liquidity risk management

Taken together, empirical J‑curve estimation by vintage age, measurement of vintage‑year correlations and portfolio‑level cash‑flow simulation allow institutional investors to calibrate liquidity buffers tailored to their specific ALM context.

For operating expenses and scheduled liability payments, buffers can be set using deterministic projections plus conservative stress overlays, funded primarily from the liquid portfolio; for capital calls, buffers are sized using the simulated distribution of net flows, targeting high confidence levels (≥ 95%) over relevant horizons.

To be suitable for liquidity risk management at KENFO’s large endowment with long‑term liabilities and significant private‑market exposure, one layer of complexity had to be added which concerns the payments for nuclear waste storage. Whilst these are generally stable due to the ten-year planning horizon of the related federal companies, sudden additional payments may arise which need to be funded. A good example for this is “Project Metall” which gave a short term opportunity to save significant costs in the long term by paying an additional EUR 390 Mio in 2021 to ORANO for a change in the waste treatment schedule and related transport arrangements. Flexible liquidity buffer and a liquidation waterfall for liquid assets helps ensure that changing illiquid commitments as well as unplanned waste storage costs remain compatible with the institution’s ability to service its obligations without forced selling. At the same time, by explicitly recognising diversification across vintages and asset classes, the model avoids excessive liquidity hoarding and reduces cash drag, thereby supporting a more efficient long‑term investment strategy. For KENFO, explicitly modelling vintage diversification reduces the required liquidity reserve by around EUR 900 million compared with a non-diversified or independence-assumption approach.