Volatility Models for Stylized Facts of High-Frequency Financial Data

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∈(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