Factor Overnight GARCH-Itô Models
提出一个统一的因子隔夜GARCH-Itô模型，用于估计和预测大规模波动率矩阵，通过非参数方法估计潜在因子波动率，并建立加权最小二乘估计的渐近理论。
Abstract This article 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.