IDEAS home Printed from https://ideas.repec.org/a/rnp/ecopol/ec2502.html
   My bibliography  Save this article

Downside Market Risk: A Key Determinant of Cryptocurrency Returns
[Риск Синхронного Падения Как Ключевой Фактор Доходности Криптовалют]

Author

Listed:
  • Kusliaikin, Aleksandr

    (National Research University Higher School of Economics)

Abstract

This paper is one of the first studies to investigate how downside market risk affects cryptocurrency returns. Based on weekly data for more than 900 cryptocurrencies from 2014 to 2018, downside market risk is considered in three different forms as it arises in the cryptocurrency market, the aggregate alternative investment market, and the stock market. The empirical part of the study employs regression analysis applied to each of three definitions of market risk. First, individual cryptocurrency betas are obtained with a rolling window approach. Second, factor portfolios are constructed based on individual betas to test factor strategies. Third, cross-sectional analysis is used to estimate risk premiums. The conclusion is that cryptocurrency returns are very sensitive to cryptocurrency market drops and insensitive to the dynamics of other financial assets. Downside market risk has a positive and significant effect on cryptocurrency returns, a result which is valid for both individual-instrument and portfolio investment. However, the effects of downside market risk for cryptocurrencies are not offset by any other risk factors, and the key conclusion to be drawn is that high cryptocurrency returns are fair compensation for elevated downside risk. A three-factor model that incorporates a downside market risk factor along with SMB and WML factors was the most useful of those considered, as it explained more than half of cross-sectional returns and surpassed other established models. These results may be useful for daily cryptocurrency trading as well as for further study of cryptocurrency returns.

Suggested Citation

  • Kusliaikin, Aleksandr, 2025. "Downside Market Risk: A Key Determinant of Cryptocurrency Returns [Риск Синхронного Падения Как Ключевой Фактор Доходности Криптовалют]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, issue 1, pages 30-55.
    • Handle: RePEc:rnp:ecopol:ec2502
    as

    Download full text from publisher

    File URL: https://repec.ranepa.ru/rnp/ecopol/ec2502.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Wei & Li, Yi & Xiong, Xiong & Wang, Pengfei, 2021. "Downside risk and the cross-section of cryptocurrency returns," Journal of Banking & Finance, Elsevier, vol. 133(C).
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    3. Liu, Weiyi & Liang, Xuan & Cui, Guowei, 2020. "Common risk factors in the returns on cryptocurrencies," Economic Modelling, Elsevier, vol. 86(C), pages 299-305.
    4. Lettau, Martin & Maggiori, Matteo & Weber, Michael, 2014. "Conditional risk premia in currency markets and other asset classes," Journal of Financial Economics, Elsevier, vol. 114(2), pages 197-225.
    5. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    6. Gul, Faruk, 1991. "A Theory of Disappointment Aversion," Econometrica, Econometric Society, vol. 59(3), pages 667-686, May.
    7. Scott Gilbert & Hio Loi, 2018. "Digital Currency Risk," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(2), pages 108-123, February.
    8. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    9. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2022. "Common Risk Factors in Cryptocurrency," Journal of Finance, American Finance Association, vol. 77(2), pages 1133-1177, April.
    Full references (including those not matched with items on IDEAS)

    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. Dobrynskaya, Victoria, 2024. "Is downside risk priced in cryptocurrency market?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    2. Liu, Yakun & Chen, Yan, 2024. "Skewness risk and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    3. Palwishah, Rana & Kashif, Muhammad & Rehman, Mobeen Ur & Al-Faryan, Mamdouh Abdulaziz Saleh, 2024. "Asymmetric liquidity risk and currency returns before and during COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 91(C).
    4. Atanasov, Victoria & Nitschka, Thomas, 2014. "Currency excess returns and global downside market risk," Journal of International Money and Finance, Elsevier, vol. 47(C), pages 268-285.
    5. Victoria Dobrynskaya, 2014. "Downside Market Risk of Carry Trades," Review of Finance, European Finance Association, vol. 18(5), pages 1885-1913.
    6. Asgar Ali & K. N. Badhani, 2023. "Downside risk matters once the lottery effect is controlled: explaining risk–return relationship in the Indian equity market," Journal of Asset Management, Palgrave Macmillan, vol. 24(1), pages 27-43, February.
    7. Liu, Jinjing, 2023. "A novel downside beta and expected stock returns," International Review of Financial Analysis, Elsevier, vol. 85(C).
    8. Farago, Adam & Tédongap, Roméo, 2018. "Downside risks and the cross-section of asset returns," Journal of Financial Economics, Elsevier, vol. 129(1), pages 69-86.
    9. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    10. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2022. "Realized semibetas: Disentangling “good” and “bad” downside risks," Journal of Financial Economics, Elsevier, vol. 144(1), pages 227-246.
    11. Long, Huaigang & Demir, Ender & Będowska-Sójka, Barbara & Zaremba, Adam & Shahzad, Syed Jawad Hussain, 2022. "Is geopolitical risk priced in the cross-section of cryptocurrency returns?," Finance Research Letters, Elsevier, vol. 49(C).
    12. Leong, Minhao & Kwok, Simon, 2023. "The pricing of jump and diffusive risks in the cross-section of cryptocurrency returns," Journal of Empirical Finance, Elsevier, vol. 74(C).
    13. Milan Fičura, 2023. "Impact of size and volume on cryptocurrency momentum and reversal," FFA Working Papers 5.003, Prague University of Economics and Business, revised 05 Apr 2023.
    14. Hoang, Lai & Vo, Duc Hong, 2024. "Google search and cross-section of cryptocurrency returns and trading activities," Journal of Behavioral and Experimental Finance, Elsevier, vol. 44(C).
    15. Jun Yuan & Qi Xu & Ying Wang, 2023. "Probability weighting in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(4), pages 516-548, April.
    16. Yang, Jen-Wei & Chiu, Shih-Yung & Yen, Kuang-Chieh, 2023. "Does the realized distribution-based measure dominate particular moments? Evidence from cryptocurrency markets," Finance Research Letters, Elsevier, vol. 51(C).
    17. Marianne Andries & Valentin Haddad, 2020. "Information Aversion," Journal of Political Economy, University of Chicago Press, vol. 128(5), pages 1901-1939.
    18. Zhao, Xiaojuan & Wang, Ye & Liu, Weiyi, 2024. "Someone like you: Lottery-like preference and the cross-section of expected returns in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    19. Sakkas, Athanasios & Urquhart, Andrew, 2024. "Blockchain factors," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 94(C).
    20. Fieberg, Christian & Günther, Steffen & Poddig, Thorsten & Zaremba, Adam, 2024. "Non-standard errors in the cryptocurrency world," International Review of Financial Analysis, Elsevier, vol. 92(C).

    More about this item

    Keywords

    alternative investments; cryptoassets; diversification; market risk; multifactor models;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:rnp:ecopol:ec2502. 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: RANEPA maintainer (email available below). General contact details of provider: https://edirc.repec.org/data/aneeeru.html .

    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.