Revisiting the puzzle of jumps in volatility forecasting: The new insights of high‐frequency jump intensity
利用标记Hawkes过程提取高频跳跃强度信息，在异质自回归框架下构建新模型，显著提升了波动率预测的样本内拟合和样本外预测精度，并验证了其经济意义和稳健性。
Abstract Motivated by the puzzling null impact of high‐frequency‐based jumps on future volatility, this paper exploits the rich information content in high‐frequency jump intensity with a mark structure under the heterogeneous autoregressive framework. Our proposed model shows that harnessing jump intensity information from the marked Hawkes process leads to significantly superior in‐sample fit and out‐of‐sample forecasting accuracy. In addition to statistical significance evidence, we also illustrate the economic significance in terms of trading efficiency. Our findings hold for a variety of competing models and under different market conditions, underlying the robustness of our results.