Forecasting Long-Horizon Volatility for Strategic Asset Allocation
研究使用1934年以来的全球股票和债券指数数据，测试不同长期波动率模型的预测能力，发现结合长期均值回归和短期波动聚集性的模型预测误差更小，对战略资产配置有参考价值。
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. <b>TOPICS:</b>Portfolio construction, volatility measures, statistical methods, performance measurement <b>Key Findings</b> ▪ 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.