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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, quantitative methods, 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.
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US and Overseas: +1 646-931-9045
UK: 0207 139 1600