论文标题
因素过夜Garch-Itô模型
Factor Overnight GARCH-Itô Models
论文作者
论文摘要
本文介绍了一个统一的因素，过夜GARCH-ITô模型，用于大波动矩阵估计和预测。为了说明整天的市场动态，提议的模型在开放至关闭和接近开放的周期中具有两个不同的瞬时因子波动过程，而每个过程都嵌入了离散的多元garch模型结构。为了估计潜在因子的波动性，我们假设较低的等级加上稀疏结构并采用非参数估计程序。然后，基于离散时间模型结构与连续时间扩散过程之间的联系，我们提出了使用非参数因子波动性估计器进行加权最小二乘估计过程，并建立其渐近定理。
This paper introduces a unified factor overnight GARCH-Itô model for large volatility matrix estimation and prediction. To account for whole-day market dynamics, the proposed model has two different instantaneous factor volatility processes for the open-to-close and close-to-open periods, while each embeds the discrete-time multivariate GARCH model structure. To estimate latent factor volatility, we assume the low rank plus sparse structure and employ nonparametric estimation procedures. Then, based on the connection between the discrete-time model structure and the continuous-time diffusion process, we propose a weighted least squares estimation procedure with the non-parametric factor volatility estimator and establish its asymptotic theorems.
