Recently several large volatility matrix estimation procedures have been developed for factor-based Ito processes whose … thresholding method (POET). We also investigate the asymptotic properties of the proposed estimation procedure and demonstrate its …

Several novel large volatility matrix estimation methods have been developed based on the high-frequency financial data … matrix and facilitates estimation of large volatility matrices. However, for predicting future volatility matrices, these … a quasi-maximum likelihood estimation method for the parameter of the factor GARCH-Ito model. We also apply it to …

novel statistical methods have been introduced to address large volatility matrix estimation problems from a high … Huber loss function with a diverging threshold to develop a robust realized volatility estimation. We show that it has the … the proposed estimation methods …

conditional quantile estimation. Specifically, we model the conditional standard deviation as a realized GARCH model and employ … proposed dynamic quantile models. We devise a two-step estimation procedure to estimate the conditional quantile parameters …. The first step applies a quasi-maximum likelihood estimation procedure, with the realized volatility as a proxy for the …

establish matching upper and lower bounds to show that the ARP estimation procedure is optimal. To estimate large integrated …

volatility. We call it the state heterogeneous GARCH-Ito (SG-Ito) model. We suggest a quasi-likelihood estimation procedure with …

This paper introduces a unified multivariate overnight GARCH-Ito model for volatility matrix estimation and prediction … weighted least squares estimation procedure for estimating model parameters with open-to-close high-frequency and close … the proposed estimation and prediction methods.The empirical analysis is carried out to compare the performance of the …

estimation method for a high-dimensional VAR model. We apply the robust estimator to predicting large volatility matrices and … estimation and prediction methods. Using high-frequency trading data, we apply the proposed method to large volatility matrix …