Volatility Forecasting With Range-Based EGARCH Models
提出将指数广义自回归条件异方差模型与价格区间数据结合，用于预测股票收益波动率。基于标普500指数1983-2004年数据，发现长记忆模型和区间数据能显著提升预测效果，且波动率可预测期限长达一年，挑战了短期预测的既有结论。
We provide a simple, yet highly effective framework for forecasting return volatility by combining exponential generalized autoregressive conditional heteroscedasticity models with data on the range. Using Standard and Poor's 500 index data for 1983–2004, we demonstrate the importance of a long-memory specification, based on either a two-factor structure or fractional integration, that allows for some asymmetry between market returns and volatility innovations. Out-of-sample forecasts reinforce the value of both this specification and the use of range data in the estimation. We find substantial forecastability of volatility as far as 1 year from the end of the estimation period, contradicting the return-based conclusions of West and Cho and of Christoffersen and Diebold that predicting volatility is possible only for short horizons.