The Low-volatility Anomaly and the Adaptive Multi-Factor Model

TL;DR

This paper explains the low-volatility anomaly using an adaptive multi-factor model, showing that volatility relates to existing risk factors and that the model outperforms traditional models in predicting portfolio performance.

Contribution

It introduces the Adaptive Multi-Factor (AMF) model estimated by GIBS, providing a new explanation for the low-volatility anomaly and demonstrating its superior predictive power.

Findings

Low-volatility portfolios load on different risk factors.

The AMF model outperforms the Fama-French 5-factor model.

Volatility is linked to existing risk factors rather than independent risk.

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.