@article{Ruan2026, 
author = {Yuwen Ruan and Xingfa Zhang and Yujiao Liu and Yan Wang and Tianli Lei},
title = {A time-varying latent factor model with GARCH noise for high-dimensional covariance matrix estimation},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {3},
pages = {5575-5599},
keywords = {time-varying factor model, multivariate GARCH, high-dimensionality, conditional covariance matrix},
url = {https://www.sciopen.com/article/10.3934/math.2026230},
doi = {10.3934/math.2026230},
abstract = {In this paper, we established a time-varying latent factor model with GARCH (generalized autoregressive conditional heteroskedasticity) noise to study volatilities (conditional covariance matrix) under the high-dimensional framework, when factors are unobservable, factor loadings are time-varying, and the idiosyncratic error term shows heteroskedasticity. A projection method was proposed for more-precise estimation of the conditional covariance matrix. We demonstrated that our model is robust when the dimensionality and sample size are large. Asymptotic theories were developed for the proposed estimation. A simulation study was conducted to evaluate the performance of the proposed model, and a real example was provided to illustrate this approach.}
}