论文标题
具有网络结构的基于高频的波动率模型
High-Frequency-Based Volatility Model with Network Structure
论文作者
论文摘要
本文介绍了一个新的多元波动率模型，该模型可以基于低频和高频数据来适应适当定义的网络结构。该模型大大降低了未知参数的数量和计算复杂性。还讨论了模型参数化和迭代多步骤预测，并提出了靶向重新聚集化。提出了用于参数估计的准可能性函数，并建立了它们的渐近性能。进行了一系列仿真实验，以评估有限样品中估计的性能。一个经验的例子证明，所提出的模型优于网络GARCH模型，在短期预测范围内，收益尤为重要。
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.
