论文标题
低挥发性异常和自适应多因素模型
The Low-volatility Anomaly and the Adaptive Multi-Factor Model
论文作者
论文摘要
该论文对低挥发性异常提供了新的解释。我们使用由群体可解释的基础选择（GIB）算法估计的自适应多因素（AMF）模型来找到与低波动率和高波动性投资组合显着相关的基本资产。这两个投资组合在非常不同的因素上加载，表明波动率不是独立的风险，而是与现有风险因素有关。低挥发性投资组合的表现是由于这些负载危险因素的（平衡）性能。 AMF模型的表现优于Fama-French 5因子模型，包括样本外和样本外。
The paper provides a new explanation of the low-volatility anomaly. We use the Adaptive Multi-Factor (AMF) model estimated by the Groupwise Interpretable Basis Selection (GIBS) algorithm to find those basis assets significantly related to low and high volatility portfolios. These two portfolios load on very different factors, indicating that volatility is not an independent risk, but that it's related to existing risk factors. The out-performance of the low-volatility portfolio is due to the (equilibrium) performance of these loaded risk factors. The AMF model outperforms the Fama-French 5-factor model both in-sample and out-of-sample.
