This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data. The model reduces the number of unknown parameters and the computational complexity substantially. The model parameterization and iterative multistep-ahead forecasts are discussed and the targeting reparameterization is also presented. Quasi-likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation experiments are carried out to assess the performance of the estimation in finite samples. An empirical example is demonstrated that the proposed model outperforms the network GARCH model, with the gains being particularly significant at short forecast horizons.

中文翻译：

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具有网络结构的高频波动率模型

本文介绍了一种新的多元波动率模型，该模型可以适应基于低频和高频数据的适当定义的网络结构。该模型大大减少了未知参数的数量和计算复杂度。讨论了模型参数化和迭代多步提前预测，并提出了目标再参数化。提出了参数估计的拟似然函数，并建立了它们的渐近性质。进行了一系列模拟实验来评估有限样本中估计的性能。一个实证例子表明，所提出的模型优于网络 GARCH 模型，其收益在短期预测范围内特别显着。