PT - JOURNAL ARTICLE AU - Mirko Cardinale AU - Narayan Y. Naik AU - Varun Sharma TI - Forecasting Long-Horizon Volatility for Strategic Asset Allocation AID - 10.3905/jpm.2021.1.212 DP - 2021 Jan 28 TA - The Journal of Portfolio Management PG - jpm.2021.1.212 4099 - https://pm-research.com/content/early/2021/01/28/jpm.2021.1.212.short 4100 - https://pm-research.com/content/early/2021/01/28/jpm.2021.1.212.full AB - Long-term volatility is a key forecasting input for strategic asset allocation analysis, yet most studies on volatility models have focused on short horizons. The authors use a large sample of global equity and bond indexes since 1934 to test the predictive power of different long-horizon volatility models. Their findings suggest that the best approach to forecasting long-horizon volatility is to use a long historical window and capture both long-term mean reversion and short-term volatility clustering properties. The results show that the authors’ model specification does a better job of reducing forecasting errors than does a naïve model based on the simple extrapolation of historical volatility.TOPICS: Portfolio construction, volatility measures, quantitative methods, statistical methods, performance measurementKey Findings▪ This study tests the predictive power of different long-horizon volatility models using a large sample of global equity and bond indexes since 1934.▪ The best approach to forecasting long-horizon volatility is to use a long historical window and capture both long-term mean reversion and short-term volatility clustering properties. ▪ The results show that the proposed model specification does a better job of reducing forecasting errors than does a naïve model based on the simple extrapolation of historical volatility.