Improved inference for fund alphas using high-dimensional cross-sectional tests
Improved inference for fund alphas using high-dimensional cross-sectional tests
复制标题
使用高维横截面测试改进基金阿尔法的推断
DOI：
10.1016/j.jempfin.2020.12.002
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发表时间：
2021-01-18
影响因子：
2.6
通讯作者：
Yan, Yayi
中科院分区：
文献类型：
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作者：
Cheng, Tingting;Yan, Cheng;Yan, Yayi
传统的逐基金阿尔法推断存在各种计量经济学问题（例如，横截面独立性假设、缺乏功效、时不变系数假设、多重假设检验）。认识到基金行业的面板性质，我们定制了四个高维的横截面测试，以阐明零阿尔法假设和非零阿尔法的比例。特别是，我们用时变alpha估计量增强了Gagliardini et al.（2016）。我们的研究结果拒绝了零α联合假设，因为α的统计显著性太高，无法用运气来解释。在控制了运气因素后，我们的实证研究表明，权力的增强有助于识别很大一部分重要的基金阿尔法，这是无法实现使用通常的沃尔德测试。同时，时变方法表明，在2000年代末的全球金融危机期间，基金阿尔法发散，这是不能观察到的使用时不变模型。总体而言，相对于文献，我们绘制了一个更准确和完整的图片，并为未来的研究提供了几个强大的工具。
The traditional fund-by-fund alpha inference suffers from various econometric problems (e.g., cross-sectional independence assumption, lack of power, time-invariant coefficient assumption, multiple-hypothesis-testing). Recognizing the panel nature of fund industries, we tailor four high-dimensional cross-sectional tests to shed light into both the zero-alpha hypothesis and ratio of non-zero alphas. Particularly, we augment Gagliardini et al. (2016) with a time-varying alpha estimator. Our results reject the zero-alpha joint hypothesis as the statistical significance of alphas is too high to be explained by luck. After controlling for luck, our empirical studies show that the power enhancement helps to identify a large portion of significant fund alphas, which cannot be achieved using the usual Wald test. Meanwhile, the time-varying approach shows that fund alphas diverge during the late 2000s Global Financial Crisis, which cannot be observed using the time-invariant model. Overall, relative to the literature, we draw a more accurate and complete picture, and provide several powerful tools for future research.