Monthly Beta Forecasting with Low‐, Medium‐ and High‐Frequency Stock Returns |
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| Authors: | Tolga Cenesizoglu Qianqiu Liu Jonathan J Reeves Haifeng Wu |
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| Institution: | 1. Department of Finance, HEC Montreal, Canada;2. Alliance Manchester Business School, University of Manchester, Manchester, UK;3. Shidler College of Business, University of Hawaii, Honolulu, USA;4. Center for Economics, Finance and Management Studies (CEFMS), Hunan University, China;5. UNSW Business School, University of New South Wales, Sydney, Australia |
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| Abstract: | This paper evaluates the accuracy of 1‐month‐ahead systematic (beta) risk forecasts in three return measurement settings; monthly, daily and 30 minutes. It was found that the popular Fama–MacBeth beta from 5 years of monthly returns generates the most accurate beta forecast among estimators based on monthly returns. A realized beta estimator from daily returns over the prior year generates the most accurate beta forecast among estimators based on daily returns. A realized beta estimator from 30‐minute returns over the prior 2 months generates the most accurate beta forecast among estimators based on 30‐minute returns. In environments where low‐, medium‐ and high‐frequency returns are accurately available, beta forecasting with low‐frequency returns are the least accurate and beta forecasting with high‐frequency returns are the most accurate. The improvements in precision of the beta forecasts are demonstrated in portfolio optimization for a targeted beta exposure. Copyright © 2016 John Wiley & Sons, Ltd. |
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| Keywords: | CAPM portfolio optimization systematic risk time‐series modeling |
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