28 Pages Posted: 20 Mar 2009 Last revised: 9 Jan 2013
Date Written: December 1, 2012
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 — 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 indices and funds-of-hedge-funds, it 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.
Keywords: hedge funds, hedging, replication, portfolio management, risk management, VaR
JEL Classification: G11, G12, G13, G23, G24
Suggested Citation: Suggested Citation
Li, Daniel and Markov, Michael and Wermers, Russ, Monitoring Daily Hedge Fund Performance When Only Monthly Data is Available (December 1, 2012). Available at SSRN: https://ssrn.com/abstract=1362265 or http://dx.doi.org/10.2139/ssrn.1362265
By Bing Liang