Forecasting energy futures volatility with threshold augmented heterogeneous autoregressive jump models
研究用高频数据将能源期货波动分解为连续部分和跳跃，再结合交易量改进异质自回归模型，发现油气波动率非线性依赖公共信息、私有信息和交易量，新模型预测效果优于传统模型。
This study forecasts the volatility of two energy futures markets (oil and gas), using high-frequency data. We, first, disentangle volatility into continuous volatility and jumps. Second, we apply wavelet analysis to study the relationship between volume and the volatility measures for different horizons. Third, we augment the heterogeneous autoregressive (HAR) model by nonlinearly including both jumps and volume. We then propose different empirical extensions of the HAR model. Our study shows that oil and gas volatilities nonlinearly depend on public information (jumps), private information (continuous volatility), and trading volume. Moreover, our threshold augmented HAR model with heterogeneous jumps and continuous volatility outperforms HAR model in forecasting volatility.