We develop a nonparametric, kernel-based joint estimator for conditional mean and covariance matrices in large and unbalanced panels. The estimator is supported by rigorous consistency results and finite-sample guarantees, ensuring its reliability for empirical applications. We apply it to an extensive panel of monthly US stock excess returns from 1962 to 2021, using macroeconomic and firm-specific covariates as conditioning variables. The estimator effectively captures time-varying cross-sectional dependencies, demonstrating robust statistical and economic performance. We find that idiosyncratic risk explains, on average, more than 75% of the cross-sectional variance.
View on arXiv@article{filipovic2025_2410.21858,
title={ Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels },
author={ Damir Filipovic and Paul Schneider },
journal={arXiv preprint arXiv:2410.21858},
year={ 2025 }
}