提出非参数联合估计方法，精准捕捉股票收益的时变相关性。
Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels
- 基于核方法联合估计条件均值与协方差矩阵
- 实证显示个股风险占横截面方差超75%
- 适合金融时间序列建模与风险管理研究者
我们开发了一种非参数、基于核的联合估计方法，用于大型非平衡面板数据中条件均值与协方差矩阵的估计。该方法具备严格的相合性结果和有限样本保证，确保其在实证应用中的可靠性。我们将该方法应用于1962至2021年美国月度股票超额收益的广泛面板数据，以宏观经济和公司特定变量作为条件变量。估计结果有效捕捉了时变的横截面依赖关系，表现出稳健的统计与经济性能。研究发现，非系统性风险平均解释了超过75%的横截面方差。
原文摘要 · Abstract (English)
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.
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