Nowcasting Net Asset Values: The Case of Private Equity
59 Pages Posted: 14 Jan 2020 Last revised: 19 Mar 2021
Date Written: April 16, 2020
We apply advances in analysis of mixed frequency and sparse data methods to estimate ``unsmoothed'' private equity (PE) Net Asset Values (NAVs) at the weekly frequency for individual funds. Using simulations and a large sample of buyout and venture funds, we show that our method yields superior estimates of NAVs relative to a simple approach based on comparable public asset and as-reported NAVs. The market beta of an average buyout [venture] fund, around 1.05 [1.20], is notably lower than previous studies suggest. The overall risk of a median buyout [venture] fund is 34% [40%] per year if measured in terms of the standard deviation of total returns, an increase of 10  percentage points relative to the series based on as-reported NAVs. However, we find significant variation in systematic and idiosyncratic risk within and across vintages, and the risk-return profile based on the samples from the 1990s is not representative of currently operating funds. Our method easily accommodates additional data, such as individual holdings details, and related M&A activity. As an example application, we show how the cross-section of appraisal biases and nowcast errors evolved around the 2008 Financial crisis.
Keywords: Private Equity, Venture Capital, Leveraged Buyouts, Institutional Investors, State Space Models, Machine Learning
JEL Classification: G23, G24, G30
Suggested Citation: Suggested Citation