论文标题
高频财务数据风格化事实的波动率模型
Volatility Models for Stylized Facts of High-Frequency Financial Data
论文作者
论文摘要
本文介绍了新型的波动扩散模型，以说明高频财务数据的风格化事实，例如波动性聚类，日内U形和杠杆作用。例如，所提出的波动过程的每日综合波动率具有实现的GARCH结构，对记录归还具有不对称作用。为了进一步解释财务数据的重型尾巴，我们假设返回原木的$ 2B $ - $ b \ in（1,2] $。然后，我们提出了一个Huber回归估计器，其最佳收敛速度为$ n^{（1-b）/b} $。我们还从herber sotive中介绍了偏见，并展示了如何调整偏见的责任和AS-atmpt ansimp。
This paper introduces novel volatility diffusion models to account for the stylized facts of high-frequency financial data such as volatility clustering, intra-day U-shape, and leverage effect. For example, the daily integrated volatility of the proposed volatility process has a realized GARCH structure with an asymmetric effect on log-returns. To further explain the heavy-tailedness of the financial data, we assume that the log-returns have a finite $2b$-th moment for $b \in (1,2]$. Then, we propose a Huber regression estimator which has an optimal convergence rate of $n^{(1-b)/b}$. We also discuss how to adjust bias coming from Huber loss and show its asymptotic properties.
