Fabian Hollstein, Marcel Prokopczuk, Victoria Voigts
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引用次数: 1
摘要
我们全面研究了众所周知的因子模型对改变因子形成断点的鲁棒性。偏离标准的30和70百分位选择,我们使用一组广泛的异常测试组合来揭示两个主要发现:首先,在规范与多样化之间存在权衡。更集中的断点往往导致更少的(特殊的)风险。更极端的投资类型会对潜在的异常现象产生更大的风险敞口,从而产生更高的平均回报。其次,模型具有不同程度的鲁棒性。Hou, Xue, and Zhang(2015)模型比Fama-French模型对断点的变化更敏感。
How Robust are Empirical Factor Models to the Choice of Breakpoints?
We comprehensively investigate the robustness of well-known factor models to altered factor-formation breakpoints. Deviating from the standard 30th and 70th percentile selection, we use an extensive set of anomaly test portfolios to uncover two main findings: First, there is a trade-off between specification versus diversification. More centered breakpoints tend to result in less (idiosyncratic) risk. More extreme sorts create stronger exposures to the underlying anomalies and, thus, higher average returns. Second, the models are robust to different degrees. The Hou, Xue, and Zhang (2015) model is much more sensitive to changes in breakpoints than the Fama-French models.