We propose a nonparametric, kernel-based joint estimator for conditional mean and covariance matrices in large unbalanced panels. Our estimator, with proven consistency and finite-sample guarantees, is applied to a comprehensive panel of monthly US stock excess returns from 1962 to 2021, conditioned on macroeconomic and firm-specific covariates. The estimator captures time-varying cross-sectional dependencies effectively, demonstrating robust statistical performance. In asset pricing, it generates conditional mean-variance efficient portfolios with out-of-sample Sharpe ratios that substantially exceed those of equal-weighted benchmarks.
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 }
}