Working Paper: CEPR ID: DP8479
Authors: Andrew J. Patton; Tarun Ramadorai
Abstract: We propose a new method to model hedge fund risk exposures using relatively high frequency conditioning variables. In a large sample of funds, we find substantial evidence that hedge fund risk exposures vary across and within months, and that capturing within-month variation is more important for hedge funds than for mutual funds. We consider different within-month functional forms, and uncover patterns such as day-of-the-month variation in risk exposures. We also find that changes in portfolio allocations, rather than changes in the risk exposures of the underlying assets, are the main drivers of hedge funds' risk exposure variation.
Keywords: Beta; Hedge Funds; Mutual Funds; Performance Evaluation; Time-Varying Risk; Window-Dressing
JEL Codes: C22; G11; G23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Changes in portfolio allocations (G11) | Risk exposure variation (D81) |
| Market events (G14) | Risk exposure adjustments (G22) |
| Time-varying beta model (C22) | Performance measurement (C52) |
| Day-of-the-month effect (C22) | Risk exposures (G22) |