---
title: Block-diagonal idiosyncratic covariance estimation in high-dimensional factor models for financial time series
url: https://www.ml-quant.com/papers/doi/10-1016-j-jocs-2024-102348/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: doi:10.1016/j.jocs.2024.102348
source_url: http://dx.doi.org/10.1016/j.jocs.2024.102348
featured: 2024-07-10
citations: 3
topic: Asset Pricing & Factors
---


# Block-diagonal idiosyncratic covariance estimation in high-dimensional factor models for financial time series

The research proposes a method for estimating high-dimensional covariance matrices in latent factor models by clustering residual series, focusing on the idiosyncratic component.

- Source: http://dx.doi.org/10.1016/j.jocs.2024.102348
- Identifier: doi:10.1016/j.jocs.2024.102348
- Released: 2024-07-04
- First featured: Quant Letter No. 56 (2024-07-10): https://www.ml-quant.com/issues/2024-07-10/
- Citations (Semantic Scholar): 3
- Published in: J. Comput. Sci.
- Topic: Asset Pricing & Factors

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