The Realized generalized autoregressive conditional heteroskedasticity (GARCH) model proposed by Hansen is often applied to forecast volatility in high-frequency financial data. It is frequently found, however, that the distribution of the estimated residuals from Realized GARCH models has peak fat-tail characteristics. Considering this feature may be a result of neglected additive outliers (AOs) and innovative outliers (IOs), this paper proposes the Realized GARCH model with additive outlier and innovative outlier (Realized GARCH-AI model) for forecasting volatility. This model can detect and correct abnormal returns and realized volatility by estimating the coefficients of volatility models and calculating the outlier test statistics. In the process of simulation, this paper considers different outlier cases in the GARCH model and the Realized GARCH model, and evaluates the performance of the proposed procedure through the accuracy of parameter estimation under different critical values. We find that the critical values will affect the results of outlier detection and correction. When the value is in a suitable range, the proposed procedure based on high-frequency data can obtain unbiased parameter estimation, and the estimation result is close to those of the intervention model containing outlier information. Finally, we use the MCS test proposed by Hansen et al. (Econometrica, 2011, 79(2), 453–497) to study the volatility prediction accuracy, value at risk, and expected shortfall prediction ability of the new model. The empirical analysis demonstrates that the proposed model produces better prediction effects than the traditional Realized GARCH model.

中文翻译：

---

使用 Realized GARCH 模型中的异常值预测波动率

Hansen 提出的 Realized 广义自回归条件异方差（GARCH）模型常用于预测高频金融数据的波动性。然而，经常发现来自 Realized GARCH 模型的估计残差分布具有峰值肥尾特征。考虑到这一特征可能是忽略加性异常值（AOs）和创新异常值（IOs）的结果，本文提出了具有加性异常值和创新异常值的Realized GARCH模型（Realized GARCH-AI模型）用于预测波动率。该模型可以通过估计波动率模型的系数和计算异常值检验统计量来检测和纠正异常收益和已实现波动率。在模拟过程中，本文考虑了 GARCH 模型和 Realized GARCH 模型中不同的异常情况，并通过不同临界值下参数估计的准确性来评估所提出程序的性能。我们发现临界值会影响异常值检测和校正的结果。当该值在合适的范围内时，所提出的基于高频数据的程序可以获得无偏的参数估计，估计结果接近包含异常信息的干预模型的估计结果。最后，我们使用 Hansen 等人提出的 MCS 测试。( 当该值在合适的范围内时，所提出的基于高频数据的程序可以获得无偏的参数估计，估计结果接近包含异常信息的干预模型的估计结果。最后，我们使用 Hansen 等人提出的 MCS 测试。( 当该值在合适的范围内时，所提出的基于高频数据的程序可以获得无偏的参数估计，估计结果接近包含异常信息的干预模型的估计结果。最后，我们使用 Hansen 等人提出的 MCS 测试。(Econometrica , 2011, 79 (2), 453–497) 研究新模型的波动率预测准确性、风险价值和预期短缺预测能力。实证分析表明，所提出的模型比传统的Realized GARCH模型产生了更好的预测效果。