Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912297097625600 |
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| author | Filipovic, Damir Schneider, Paul |
| author_facet | Filipovic, Damir Schneider, Paul |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_21858 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels Filipovic, Damir Schneider, Paul Methodology Machine Learning Statistical Finance (primary) 62G05 (secondary) 62G20, 46E40, 46E22 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. |
| title | Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels |
| topic | Methodology Machine Learning Statistical Finance (primary) 62G05 (secondary) 62G20, 46E40, 46E22 |
| url | https://arxiv.org/abs/2410.21858 |