The Low-volatility Anomaly and the Adaptive Multi-Factor Model
Statistical Finance
2021-04-27 v2 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2003.08302,
title = {The Low-volatility Anomaly and the Adaptive Multi-Factor Model},
author = {Robert A. Jarrow and Rinald Murataj and Martin T. Wells and Liao Zhu},
journal= {arXiv preprint arXiv:2003.08302},
year = {2021}
}
Comments
29 pages, 11 figures, 10 tables