Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels

Damir Filipovic and Paul Schneider

Papers from arXiv.org

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.

Date: 2024-10, Revised 2025-03
New Economics Papers: this item is included in nep-ecm
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