Markov Processes International

Monitoring Daily Hedge Fund Performance When Only Monthly Data is Available

This academic paper, co-authored by Michael Markov and Daniel Li of MPI and Russ Wermers, Associate Professor of Finance at University of Maryland, introduces a new method for investors to forecast daily returns in advance of month-end reporting. The approach enables investors to infer daily results from monthly returns data. Synthetic replication portfolios are created by employing our Dynamic Style Analysis (DSA) to accurately replicate a given fund. Once the replication is achieved, daily returns for the underlying indices are used to project the fund’s daily returns. This replication approach can be used to better hedge risks and estimate Value at Risk (VAR).

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