Revisiting the puzzle of jumps in volatility forecasting: The new insights of high-frequency jump intensity

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.