PitchBook Benchmarks aim to help both LPs and GPs better understand fund performance relative to broader asset classes and other private market strategies. We present performance through several lenses—including internal rates of return (IRRs) and cash multiples—to provide a holistic view for assessing performance within and between strategies, as well as across vintage years. Furthermore, the returns of private market funds are measured relative to easily accessible public market substitutes using a public market equivalent (PME) metric. Each edition of our Benchmarks also includes a section that highlights a specific aspect of fund performance. In the conclusion to our series on cash flow management, we bring together pieces from prior analyses to introduce new commitment pacing and cash flow models.
- Commitment pacing model: This model utilizes PitchBook data from thousands of private market funds to produce a commitment schedule based on an LP’s target allocation size and timeframe. The model accounts for the disparate nature of cash flow profiles between private market strategies and can be tailored to specific characteristics, such as fund size and location.
- Fund cash flow model: This model leverages PitchBook data to model cash flows for individual private market funds and/or an entire portfolio. The model generates theoretical cash flows based purely on hypothetical inputs or can be used to model future cash flows for existing funds from any stage of their lifecycle.
- PB CCaR: One of the outputs of the fund cash flow model is a new calculation called PB Capital Call at Risk (PB CCaR). This metric allows an LP to set a certain threshold (typically 90%, 95%, or 99%) to answer the question, “What is the most capital the fund/portfolio will call down 90%/95%/99% of the time?” For example, a PB CCaR of $10.0 million at 90% implies that there is a 10% probability that the fund will call $10.0 million or more in a given period. We produce a version of PB CCaR based on observed data (i.e. Historical), as well as a version calculated using the statistical mean and standard deviation (i.e. Parametric).