The macroeconomic drivers in hedge fund beta management |
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| Affiliation: | 1. HEC Liège, Management School of the University of Liège, Rue Louvrex 14, Bldg. N1, 4000 Liège, Belgium;2. Léonard de Vinci Pôle Universitaire, Research Center, 92 916, Paris La Défense, France;1. Department of Management, Università Politecnica delle Marche, Ancona, Italy;2. Department of Economics and Social Science, Università Politecnica delle Marche, Ancona, Italy;1. Texas A&M University, Department of Finance, Mays Business School, College Station, TX, 77843, USA;2. University of Valladolid (Spain), NRU Higher School of Economics (Russia), School of Business and Economics, Avda. Valle Del Esgueva 6, 47011, Valladolid, Spain;3. University of Valladolid, School of Business and Economics, Avda. Valle Del Esgueva 6, 47011, Valladolid, Spain;1. Department of Financial Engineering, Ajou University, Suwon, 16499, Republic of Korea;2. Department of Applied Mathematics & Institute of Natural Science, Kyung Hee University, Yongin, 17104, Republic of Korea;3. Department of Mathematical Sciences, Seoul National University, Seoul, 08826, Republic of Korea;1. Faculty of Economics, Fukuoka University, 8-19-1, Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan;2. Faculty of International Politics and Economics, Nishogakusha University, 6-16 Sanbancho, Chiyoda-ku, Tokyo, 102-8336, Japan |
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| Abstract: | We investigate how macroeconomic indicators alter the dynamic risk exposure of different hedge fund style strategies. We implement a multifactor model to estimate the unobservable time-varying risk exposure conditional on macroeconomic information and a VAR to measure the impact of macroeconomic predictors on different time horizons. Using monthly returns on a cross-section of 10 different style indices from February 1997 to August 2019, we find that, on average, macroeconomic indicators explain approximately 30%, 55%, and 75% of the variability of betas at 1-, 6-, and 36-month horizons, respectively. Although macroeconomic predictors play a critical role at every horizon, at 1 month, the dominating effect comes from idiosyncratic shocks, which indicates that in the short run, hedge fund managers rely mostly on their own reallocation signals. Moreover, consistent with the fundamental drivers of the smart beta factors, we find that the interest rate level and GDP growth similarly impact hedge fund exposures across styles. |
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| Keywords: | Kalman filter Macroeconomic indicators Factor tilting Conditional betas G10 G11 G12 G23 |
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