Volatility asymmetry is a hot topic in high-frequency financial market. This article proposes a new econometric model, which could describe volatility asymmetry based on high-frequency data and low-frequency data. After providing the quasi-maximum likelihood estimators for the parameters, we establish their asymptotic properties. We also conduct a series of simulation studies to check the finite sample performance and volatility forecasting performance of the proposed model and method. And a real data example is demonstrated that the new model has more substantial volatility prediction power than GARCH-Itô model in the literature.

中文翻译：

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一种新的波动率模型：GQARCH-ItÔ 模型

波动性不对称是高频金融市场的热门话题。本文提出了一种新的计量经济学模型，该模型可以基于高频数据和低频数据来描述波动率的不对称性。在为参数提供准最大似然估计量之后，我们建立了它们的渐近特性。我们还进行了一系列模拟研究，以检查所提出模型和方法的有限样本性能和波动率预测性能。并通过一个真实的数据实例证明了新模型比文献中的 GARCH-Itô 模型具有更强的波动预测能力。