The contribution of jump signs and activity to forecasting stock price volatility
提出一种按活动类型（有限/无限）和符号分解已实现跳跃测度的方法，发现无限跳跃改善短期预测、有限跳跃改善长期预测，而符号跳跃贡献有限；噪声稳健测度在高频下提升预测，但300秒频率的标准波动测度均方预测误差最小；模型平均波动率预测优于基准和单一最优模型。
We propose a novel approach to decompose realized jump measures by type of activity (finite/infinite) and sign, and also provide noise-robust versions of the ABD jump test (Andersen et al., 2007b) and realized semivariance measures. We find that infinite (finite) jumps improve the forecasts at shorter (longer) horizons; but the contribution of signed jumps is limited. As expected, noise-robust measures deliver substantial forecast improvements at higher sampling frequencies, although standard volatility measures at the 300-s frequency generate the smallest MSPEs. Since no single model dominates across sampling frequency and forecasting horizon, we show that model averaged volatility forecasts – using time-varying weights and models from the model confidence set – generally outperform forecasts from both the benchmark and single best extended HAR model. Finally, forecasts using volatility and jump measures based on transaction sampling are inferior to the forecasts from clock-based sampling.