THE LOW-VOLATILITY ANOMALY AND THE ADAPTIVE MULTI-FACTOR MODEL

Robert Jarrow (), Rinald Murataj (), Martin T. Wells () and Liao Zhu
Additional contact information
Rinald Murataj: T. Rowe Price, Baltimore, MD 21202, USA
Martin T. Wells: Department of Statistics and Data Science, Cornell University, Ithaca, NY 14853, USA
Liao Zhu: Department of Statistics and Data Science, Cornell University, Ithaca, NY 14853, USA

International Journal of Theoretical and Applied Finance (IJTAF), 2023, vol. 26, issue 04n05, 1-33

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 basis assets, indicating that volatility is not an independent risk, but that it is related to existing risk factors. The out-performance of the low-volatility portfolio is due to the (equilibrium) performance of these loaded risk factors, specifically, the better long-term performance of the asset classes bonds and real estate as contrasted with materials, precious metals, and the healthcare industry. Our methodology is applicable to any long–short anomaly but we focus on the low-volatility anomaly since it is formed explicitly on the risk characteristic rather than on embedded risks of other anomalies. The AMF model outperforms the Fama–French 5-factor model significantly both in-sample and out-of-sample.

Keywords: Low-volatility anomaly; AMF model; GIBS algorithm; high-dimensional statistics; machine learning; False Discovery Rate (search for similar items in EconPapers)
Date: 2023
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Working Paper: The Low-volatility Anomaly and the Adaptive Multi-Factor Model (2021)
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DOI: 10.1142/S0219024923500206

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