Monthly Beta Forecasting with Low-, Medium- and High-Frequency Stock Returns

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• 作者：Tolga Cenesizoglu ; Qianqiu Liu ; Jonathan J. Reeves and Haifeng Wu
• 刊名：Journal of Forecasting
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：35
• 期：6
• 页码：528-541
• 全文大小：641K
• ISSN：1099-131X

文摘

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