Longer-Term Time-Series Volatility Forecasts
研究发现，在预测长期波动率时，历史数据比近期数据更重要，并提出了两种优于传统GARCH类模型的新预测模型，适用于期权定价和长期风险价值模型。
Abstract Option pricing models and longer-term value-at-risk (VaR) models generally require volatility forecasts over horizons considerably longer than the data frequency. The typical recursive procedure for generating longer-term forecasts keeps the relative weights of recent and older observations the same for all forecast horizons. In contrast, we find that older observations are relatively more important in forecasting at longer horizons. We find that the Ederington and Guan (2005) model and a modified EGARCH (exponential generalized autoregressive conditional heteroskedastic) model in which parameter values vary with the forecast horizon forecast better out-of-sample than the GARCH (generalized autoregressive conditional heteroskedastic), EGARCH, and Glosten, Jagannathan, and Runkle (GJR) models across a wide variety of markets and forecast horizons.