Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels
Methodology
2025-03-28 v5 Machine Learning
Statistical Finance
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.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}
}