Block-diagonal idiosyncratic covariance estimation in high-dimensional factor models for financial time series
<|reference_start|>Block-diagonal idiosyncratic covariance estimation in high-dimensional factor models for financial time series: Estimation of high-dimensional covariance matrices in latent factor models is an important topic in many fields and especially in finance. Since the number of financial assets grows while the estimation window length remains of limited size, the often used sample estimator yields noisy estimates which are not even positive definite. Under the assumption of latent factor models, the covariance matrix is decomposed into a common low-rank component and a full-rank idiosyncratic component. In this paper we focus on the estimation of the idiosyncratic component, under the assumption of a grouped structure of the time series, which may arise due to specific factors such as industries, asset classes or countries. We propose a generalized methodology for estimation of the block-diagonal idiosyncratic component by clustering the residual series and applying shrinkage to the obtained blocks in order to ensure positive definiteness. We derive two different estimators based on different clustering methods and test their performance using simulation and historical data. The proposed methods are shown to provide reliable estimates and outperform other state-of-the-art estimators based on thresholding methods.<|reference_end|>
@article{žignić2024block-diagonal,
    title={Block-diagonal idiosyncratic covariance estimation in high-dimensional
  factor models for financial time series},
    author={Lucija v{Z}igni'c, Stjepan Beguv{s}i'c, and Zvonko Kostanjv{c}ar},
    journal={Journal of Computational Science, Volume 81, 2024, 102348},
    year={2024},
    doi={10.1016/j.jocs.2024.102348},
    archivePrefix={arXiv},
    eprint={2407.03781},
    primaryClass={q-fin.ST cs.CE q-fin.MF stat.ME}
}