Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels
This provides a reliable tool for empirical finance applications, though it is incremental as it builds on existing nonparametric methods for panel data.
The paper tackled the problem of estimating conditional mean and covariance in large, unbalanced panels by developing a nonparametric kernel-based joint estimator, which they applied to US stock returns from 1962 to 2021 and found that idiosyncratic risk explains over 75% of cross-sectional variance.
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.