Forecasting and Managing Volatility: An S&P 500 Case Study

Vol. 23, No. 1, 2025
by Wei Dai, Xing Hong, Robert C. Merton and Mathieu Pellerin

Using daily and intraday data from 1997 to 2023, we study strategies that stabilize volatility around a target by rebalancing between the S&P 500 and Treasury bills based on a broad set of volatility forecasts. Somewhat counterintuitively, lower forecasting errors do not necessarily result in more stable strategy volatility. Simple forecasts with fewer parameters can stabilize volatility as well as more complex models. In particular, combinations of implied volatility and simple estimators based on past returns exhibit good volatility control and lower turnover. On the implementation front, we show that the target volatility strategies we study are viable in the presence of realistic trading costs, delays between forecasting and rebalancing, or constraints on rebalancing frequency. Collectively, our findings can help design target volatility strategies that improve upon portfolios with constant target weights (e.g., a 60/40 portfolio) in achieving and maintaining investors’ desired volatility exposures over time.