Volatility models for stylized facts of high‐frequency financial data
本文提出了新的波动率扩散模型，以解释高频金融数据的波动率聚集、日内U形和杠杆效应等典型特征，并引入Huber回归估计量处理厚尾数据，讨论了偏差调整和渐近性质。
This article introduces novel volatility diffusion models to account for the stylized facts of high‐frequency financial data such as volatility clustering, intraday 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 th moment for . Then, we propose a Huber regression estimator that has an optimal convergence rate of . We also discuss how to adjust bias coming from Huber loss and show its asymptotic properties.