---
title: Reviewing Large Dynamic Covariance Matrices
url: https://www.ml-quant.com/papers/repec/eee-ecosta-v-29-y-2024-i-c-p-16-30/
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: RePEc:eee:ecosta:v:29:y:2024:i:c:p:16-30
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS2452306221000587%3Bh%3Drepec%3Aeee%3Aecosta%3Av%3A29%3Ay%3A2024%3Ai%3Ac%3Ap%3A16-30
featured: 2024-01-09
citations: unknown
topic: Econometrics & Forecasting
---


# Reviewing Large Dynamic Covariance Matrices

The article discusses recent advancements in estimating large, time-varying dynamic covariance matrices, with a focus on GARCH model extensions and identifying structural breaks in large covariance structures.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS2452306221000587%3Bh%3Drepec%3Aeee%3Aecosta%3Av%3A29%3Ay%3A2024%3Ai%3Ac%3Ap%3A16-30
- Identifier: RePEc:eee:ecosta:v:29:y:2024:i:c:p:16-30
- Released: 2024-01-09
- First featured: Quant Letter No. 32 (2024-01-09): https://www.ml-quant.com/issues/2024-01-09/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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