# Estimation of Large Financial Covariances: A Cross-Validation Approach

arXiv: https://arxiv.org/abs/2012.05757 · HTML: https://mlsanity.com/paper/2012.05757/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization](https://mlsanity.com/paper/2502.00828/) (2025)
- [End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning](https://mlsanity.com/paper/2507.01918/) (2025)
- [WeSpeR: Computing non-linear shrinkage formulas for the weighted sample covariance](https://mlsanity.com/paper/2410.14413/) (2024)
- [Holdout cross-validation for large non-Gaussian covariance matrix estimation using Weingarten calculus](https://mlsanity.com/paper/2509.13923/) (2025)

## Abstract (arXiv)

We introduce a novel covariance estimator for portfolio selection that adapts to the non-stationary or persistent heteroskedastic environments of financial time series by employing exponentially weighted averages and nonlinearly shrinking the sample eigenvalues through cross-validation. Our estimator is structure agnostic, transparent, and computationally feasible in large dimensions. By correcting the biases in the sample eigenvalues and aligning our estimator to more recent risk, we demonstrate that our estimator performs well in large dimensions against existing state-of-the-art static and dynamic covariance shrinkage estimators through simulations and with an empirical application in active portfolio management.