Daniele Massacci, Lucio Sarno, Lorenzo Trapani, Pierluigi Vallarino
arXiv 23 Jul 2025 · Econometrics
arXiv:2507.17599 · PDF · DOI · OpenAlex · Extracted main text
We propose a methodology to construct tests for the null hypothesis that the pricing errors of a panel of asset returns are jointly equal to zero in a linear factor asset pricing model -- that is, the null of "zero alpha". We consider, as a leading example, a model with observable, tradable factors, but we also develop extensions to accommodate for non-tradable and latent factors. The test is based on equation-by-equation estimation, using a randomized version of the estimated alphas, which only requires rates of convergence. The distinct features of the proposed methodology are that it does not require the estimation of any covariance matrix, and that it allows for both N and T to pass to infinity, with the former possibly faster than the latter. Further, unlike extant approaches, the procedure can accommodate conditional heteroskedasticity, non-Gaussianity, and even strong cross-sectional dependence in the error terms. We also propose a de-randomized decision rule to choose in favor or against the correct specification of a linear factor pricing model. Monte Carlo simulations show that the test has satisfactory properties and it compares favorably to several existing tests. The usefulness of the testing procedure is illustrated through an application of linear factor pricing models to price the constituents of the S&P 500.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Feng, Long and Lan, Wei and Liu, Binghui and Ma, Yanyuan (2022) High-dimensional test for alpha in linear factor pricing models with sparse alternatives | 1.000 | 35 | 8 | 100% |
| 2 | Fan, Jianqing and Liao, Yuan and Yao, Jiawei (2015) Power enhancement in high-dimensional cross-sectional tests | 1.000 | 13 | 5 | 100% |
| 3 | Pesaran, M Hashem and Yamagata, Takashi (2024) Testing for alpha in linear factor pricing models with a large number of securities | 1.000 | 11 | 7 | 100% |
| 4 | Giglio, Stefano and Liao, Yuan and Xiu, Dacheng (2021) Thousands of alpha tests | 1.000 | 11 | 5 | 100% |
| 5 | Gagliardini, Patrick and Ossola, Elisa and Scaillet, Olivier (2016) Time-varying risk premium in large cross-sectional equity data sets | 1.000 | 7 | 5 | 100% |
| 6 | Ardia, David and Sessinou, Rosnel (2024) Robust Inference in Large Panels and Markowitz Portfolios | 1.000 | 7 | 4 | 100% |
| 7 | He, Yong and Kong, Xinbing and Trapani, Lorenzo and Yu, Long (2023) One-way or two-way factor model for matrix sequences? self | 1.000 | 6 | 3 | 100% |
| 8 | Trapani, Lorenzo (2018) A randomized sequential procedure to determine the number of factors self | 0.843 | 3 | 3 | 100% |
| 9 | Bailey, Natalia and Kapetanios, George and Pesaran, M Hashem (2021) Measurement of factor strength: Theory and practice | 0.737 | 3 | 2 | 100% |
| 10 | Potra, Florian A (1985) On superadditive rates of convergence | 0.644 | 4 | 1 | 100% |
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