This paper introduces a unified factor overnight GARCH-It\^o 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.

翻译：摘要：本文提出了一种用于大规模波动率矩阵估计与预测的统一因子隔夜GARCH-Itô模型。为刻画全天市场动态，该模型针对开盘-收盘与收盘-开盘时段分别设定两个不同的瞬时因子波动率过程，每个过程均嵌入离散时间多元GARCH模型结构。为估计潜在因子波动率，我们假设低秩加稀疏结构并采用非参数估计方法。进而基于离散时间模型结构与连续时间扩散过程之间的关联，提出一种利用非参数因子波动率估计量的加权最小二乘估计方法，并建立了其渐近理论。