Monitoring Daily Hedge Fund Performance When Only Monthly Data is Available

This paper introduces a new approach to monitoring the daily risk of investing in hedge funds. Specifically, we use low-frequency (monthly) models to forecast high-frequency (daily) hedge fund returns. This approach addresses the common problem that confronts investors who wish to monitor their hedge funds on a daily basis - namely, that disclosure of returns by funds occurs only at a monthly frequency, usually with a time lag. We use monthly returns on investable assets or factors to fit monthly hedge fund returns, then forecast daily returns of hedge funds during the following month using the publicly observed daily returns on the explanatory assets. We show that our replication approach can be used to forecast daily returns of long-short hedge funds. In addition, for diversified portfolios such as hedge fund indexes and funds of hedge funds, our approach forecasts daily returns very accurately. We illustrate how our simple replication approach can be used to 1) hedge daily hedge fund risk and 2) estimate and control value-at-risk