Forecasting Long-Horizon Volatility for Strategic Asset Allocation

Abstract

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, statistical methods, performance measurement

Key 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.