| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP16307 |
| DP16307 Test Assets and Weak Factors | |
| Stefano Giglio; Dacheng Xiu; Dake Zhang | |
| 发表日期 | 2021-06-28 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | Estimation and testing of factor models in asset pricing requires choosing a set of test assets. The choice of test assets determines how well different factor risk premia can be identified: if only few assets are exposed to a factor, that factor is weak, which makes standard estimation and inference incorrect. In other words, the strength of a factor is not an inherent property of the factor: it is a property of the cross-section used in the analysis. We propose a novel way to select assets from a universe of test assets and estimate the risk premium of a factor of interest, as well as the entire stochastic discount factor, that explicitly accounts for weak factors and test assets with highly correlated risk exposures. We refer to our methodology as supervised principal component analysis (SPCA), because it iterates an asset selection step and a principal-component estimation step. We provide the asymptotic properties of our estimator, and compare its limiting behavior with that of alternative estimators proposed in the recent literature, which rely on PCA, Ridge, Lasso, and Partial Least Squares (PLS). We find that the SPCA is superior in the presence of weak factors, both in theory and in finite samples. We illustrate the use of SPCA by applying it to estimate the risk premia of several tradable and nontradable factors, to evaluate asset managers’ performance, and to de-noise asset pricing factors. |
| 主题 | Financial Economics |
| URL | https://cepr.org/publications/dp16307 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/545272 |
| 推荐引用方式 GB/T 7714 | Stefano Giglio,Dacheng Xiu,Dake Zhang. DP16307 Test Assets and Weak Factors. 2021. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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