Volatility forecasting for low-volatility investing

Abstract

Low-volatility investing is typically implemented by sorting stocks based on simple risk measures; for example, the empirical standard deviation of last year’s daily returns. In contrast, we understand identifying next-month’s ranking of volatilities as a forecasting problem aimed at the ex-post optimal sorting. We show that time series models based on intraday data outperform simple risk measures in anticipating the cross-sectional ranking in real time. The corresponding portfolios are more similar to the ex-ante infeasible optimal portfolio in multiple dimensions. Moreover, the increased signal in our improved volatility sorts survives portfolio weight smoothing for mitigating transaction costs.