IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2003.08302.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2003.08302
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    2. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    3. Ang, Andrew & Hodrick, Robert J. & Xing, Yuhang & Zhang, Xiaoyan, 2009. "High idiosyncratic volatility and low returns: International and further U.S. evidence," Journal of Financial Economics, Elsevier, vol. 91(1), pages 1-23, January.
    4. McInish, Thomas H & Wood, Robert A, 1986. "Adjusting for Beta Bias: An Assessment of Alternative Techniques: A Note," Journal of Finance, American Finance Association, vol. 41(1), pages 277-286, March.
    5. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    6. Frazzini, Andrea & Pedersen, Lasse Heje, 2014. "Betting against beta," Journal of Financial Economics, Elsevier, vol. 111(1), pages 1-25.
    7. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    8. Bien, Jacob & Tibshirani, Robert, 2011. "Hierarchical Clustering With Prototypes via Minimax Linkage," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1075-1084.
    9. Shumway, Tyler, 1997. "The Delisting Bias in CRSP Data," Journal of Finance, American Finance Association, vol. 52(1), pages 327-340, March.
    10. Stephen Reid & Jonathan Taylor & Robert Tibshirani, 2018. "A General Framework for Estimation and Inference From Clusters of Features," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 280-293, January.
    11. Bali, Turan G. & Brown, Stephen J. & Murray, Scott & Tang, Yi, 2017. "A Lottery-Demand-Based Explanation of the Beta Anomaly," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(6), pages 2369-2397, December.
    12. Black, Fischer, 1972. "Capital Market Equilibrium with Restricted Borrowing," The Journal of Business, University of Chicago Press, vol. 45(3), pages 444-455, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
    2. Calvet, Laurent E. & Betermier, Sebastien & Jo, Evan, 2019. "A Supply and Demand Approach to Equity Pricing," CEPR Discussion Papers 13974, C.E.P.R. Discussion Papers.
    3. Liao Zhu & Robert A. Jarrow & Martin T. Wells, 2021. "Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-30, December.
    4. Khasawneh, Maher & McMillan, David G. & Kambouroudis, Dimos, 2024. "Left-tail risk and UK stock return predictability: Underreaction, overreaction, and arbitrage difficulties," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    5. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, June.
    6. Asness, Cliff & Frazzini, Andrea & Gormsen, Niels Joachim & Pedersen, Lasse Heje, 2020. "Betting against correlation: Testing theories of the low-risk effect," Journal of Financial Economics, Elsevier, vol. 135(3), pages 629-652.
    7. Poon, Percy & Yao, Tong & Zhang, Andrew (Jianzhong), 2022. "The alphas of beta and idiosyncratic volatility," Journal of Financial Markets, Elsevier, vol. 61(C).
    8. Rajnish Mehra & Sunil Wahal & Daruo Xie, 2021. "Is idiosyncratic risk conditionally priced?," Quantitative Economics, Econometric Society, vol. 12(2), pages 625-646, May.
    9. Asgar Ali & K. N. Badhani, 2021. "Beta-Anomaly: Evidence from the Indian Equity Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(1), pages 55-78, March.
    10. Vafai, Nima & Rakowski, David, 2024. "The sources of portfolio volatility and mutual fund performance," International Review of Financial Analysis, Elsevier, vol. 91(C).
    11. Liao Zhu & Sumanta Basu & Robert A. Jarrow & Martin T. Wells, 2020. "High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 1-52, December.
    12. Asness, Clifford & Frazzini, Andrea & Israel, Ronen & Moskowitz, Tobias J. & Pedersen, Lasse H., 2018. "Size matters, if you control your junk," Journal of Financial Economics, Elsevier, vol. 129(3), pages 479-509.
    13. Stephen A. Gorman & Frank J. Fabozzi, 2021. "The ABC’s of the alternative risk premium: academic roots," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 405-436, October.
    14. Bradrania, Reza & Veron, Jose Francisco & Wu, Winston, 2023. "The beta anomaly and the quality effect in international stock markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).
    15. Turan G. Bali & Robert F. Engle & Yi Tang, 2017. "Dynamic Conditional Beta Is Alive and Well in the Cross Section of Daily Stock Returns," Management Science, INFORMS, vol. 63(11), pages 3760-3779, November.
    16. Zhong, Angel, 2018. "Idiosyncratic volatility in the Australian equity market," Pacific-Basin Finance Journal, Elsevier, vol. 50(C), pages 105-125.
    17. Bradrania, Reza & Veron, Jose Francisco, 2023. "The beta anomaly in the Australian stock market and the lottery demand," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    18. Shiyang Huang & Xin Liu & Dong Lou & Christopher Polk, 2024. "The Booms and Busts of Beta Arbitrage," Management Science, INFORMS, vol. 70(8), pages 5367-5385, August.
    19. Lee, Kuan-Hui & Yang, Cheol-Won, 2022. "The world price of tail risk," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    20. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "A diagnostic criterion for approximate factor structure," Journal of Econometrics, Elsevier, vol. 212(2), pages 503-521.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2003.08302. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.