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

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  • Robert A. Jarrow
  • Rinald Murataj
  • Martin T. Wells
  • Liao Zhu

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.

Suggested Citation

  • Robert A. Jarrow & Rinald Murataj & Martin T. Wells & Liao Zhu, 2020. "The Low-volatility Anomaly and the Adaptive Multi-Factor Model," Papers 2003.08302, arXiv.org, revised Apr 2021.
  • Handle: RePEc:arx:papers:2003.08302
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    Cited by:

    1. Liao Zhu & Haoxuan Wu & Martin T. Wells, 2021. "A News-based Machine Learning Model for Adaptive Asset Pricing," Papers 2106.07103, arXiv.org.
    2. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.

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