IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v15y2022i10p427-d924660.html
   My bibliography  Save this article

Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures

Author

Listed:
  • Sulalitha Bowala

    (Department of Statistics, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
    These authors contributed equally to this work.)

  • Japjeet Singh

    (Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
    These authors contributed equally to this work.)

Abstract

Portfolio risk management plays an important role in successful investments. Portfolio standard deviation, value-at-risk, expected shortfall, and maximum absolute deviation are widely used portfolio risk measures. However, the existing portfolio risk measures are vulnerable to larger skewness and kurtosis of the asset returns. Moreover, the traditional assumption of normality of the portfolio returns leads to the underestimation of portfolio risk. Cryptocurrencies are a decentralized digital medium of exchange. In contrast to physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions. Due to the high volume and high frequency of cryptocurrency transactions, risk forecasting using daily data is not enough, and a high-frequency analysis is required. High-frequency data reveal a very high excess kurtosis and skewness for returns of cryptocurrencies. In order to incorporate larger skewness and kurtosis of the cryptocurrencies, a data-driven portfolio risk measure is minimized to obtain the optimal portfolio weights. A recently proposed data-driven volatility forecasting approach with daily data are used to study risk forecasting for cryptocurrencies with high-frequency (hourly) big data. The paper emphasizes the superiority of portfolio selection of cryptocurrencies by minimizing the recently proposed risk measure over the traditional minimum variance portfolio.

Suggested Citation

  • Sulalitha Bowala & Japjeet Singh, 2022. "Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures," JRFM, MDPI, vol. 15(10), pages 1-16, September.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:10:p:427-:d:924660
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/15/10/427/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/15/10/427/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zdravka Aljinović & Branka Marasović & Tea Šestanović, 2021. "Cryptocurrency Portfolio Selection—A Multicriteria Approach," Mathematics, MDPI, vol. 9(14), pages 1-21, July.
    2. Pätäri, Eero & Leivo, Timo & Honkapuro, Samuli, 2012. "Enhancement of equity portfolio performance using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 220(3), pages 786-797.
    3. Aerambamoorthy Thavaneswaran & Alex Paseka & Julieta Frank, 2020. "Generalized value at risk forecasting," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(20), pages 4988-4995, October.
    4. Ma, Yechi & Ahmad, Ferhana & Liu, Miao & Wang, Zilong, 2020. "Portfolio optimization in the era of digital financialization using cryptocurrencies," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    5. Baur, Dirk G. & Dimpfl, Thomas, 2018. "Asymmetric volatility in cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 148-151.
    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. Adel Benhamed & Ahlem Selma Messai & Ghassen El Montasser, 2023. "On the Determinants of Bitcoin Returns and Volatility: What We Get from Gets?," Sustainability, MDPI, vol. 15(3), pages 1-21, January.

    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. Aljinović Zdravka & Marasović Branka & Milićević Tea Kalinić, 2022. "The Risk and Return of Traditional and Alternative Investments Under the Impact of COVID-19," Business Systems Research, Sciendo, vol. 13(3), pages 8-22, October.
    2. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    3. Kakinaka, Shinji & Umeno, Ken, 2021. "Exploring asymmetric multifractal cross-correlations of price–volatility and asymmetric volatility dynamics in cryptocurrency markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    4. Pierre J. Venter & Eben Maré, 2020. "GARCH Generated Volatility Indices of Bitcoin and CRIX," JRFM, MDPI, vol. 13(6), pages 1-15, June.
    5. Juan F. Monge & Mercedes Landete & Jos'e L. Ruiz, 2016. "Sharpe portfolio using a cross-efficiency evaluation," Papers 1610.00937, arXiv.org, revised Oct 2016.
    6. Angerer, Martin & Hoffmann, Christian Hugo & Neitzert, Florian & Kraus, Sascha, 2021. "Objective and subjective risks of investing into cryptocurrencies," Finance Research Letters, Elsevier, vol. 40(C).
    7. Chen, Yan & Zhang, Lei & Bouri, Elie, 2024. "Can a self-exciting jump structure better capture the jump behavior of cryptocurrencies? A comparative analysis with the S&P 500," Research in International Business and Finance, Elsevier, vol. 69(C).
    8. Zhang, Chuanhai & Ma, Huan & Arkorful, Gideon Bruce & Peng, Zhe, 2023. "The impacts of futures trading on volatility and volatility asymmetry of Bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 86(C).
    9. Luo, Di & Mishra, Tapas & Yarovaya, Larisa & Zhang, Zhuang, 2021. "Investing during a Fintech Revolution: Ambiguity and return risk in cryptocurrencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    10. Böyükaslan, Adem & Ecer, Fatih, 2021. "Determination of drivers for investing in cryptocurrencies through a fuzzy full consistency method-Bonferroni (FUCOM-F’B) framework," Technology in Society, Elsevier, vol. 67(C).
    11. Panayiotis Theodossiou & Polina Ellina & Christos S. Savva, 2022. "Stochastic properties and pricing of bitcoin using a GJR-GARCH model with conditional skewness and kurtosis components," Review of Quantitative Finance and Accounting, Springer, vol. 59(2), pages 695-716, August.
    12. Shafiqah Azman & Dharini Pathmanathan & Aerambamoorthy Thavaneswaran, 2022. "Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter," Mathematics, MDPI, vol. 10(17), pages 1-15, September.
    13. Cao, Guangxi & Xie, Wenhao, 2022. "Asymmetric dynamic spillover effect between cryptocurrency and China's financial market: Evidence from TVP-VAR based connectedness approach," Finance Research Letters, Elsevier, vol. 49(C).
    14. Nguyen, Jeremy K., 2022. "Racial discrimination in non-fungible token (NFT) prices? CryptoPunk sales and skin tone," Economics Letters, Elsevier, vol. 218(C).
    15. Gupta, Nitish & Park, Hyunkyu & Phaal, Rob, 2022. "The portfolio planning, implementing, and governing process: An inductive approach," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    16. Klaus Grobys, 2021. "When the blockchain does not block: on hackings and uncertainty in the cryptocurrency market," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1267-1279, August.
    17. Katsiampa, Paraskevi & Corbet, Shaen & Lucey, Brian, 2019. "Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis," Finance Research Letters, Elsevier, vol. 29(C), pages 68-74.
    18. Chaim, Pedro & Laurini, Márcio P., 2019. "Nonlinear dependence in cryptocurrency markets," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 32-47.
    19. Brini, Alessio & Lenz, Jimmie, 2024. "Pricing cryptocurrency options with machine learning regression for handling market volatility," Economic Modelling, Elsevier, vol. 136(C).
    20. Eero P䴤ri & Mika Vilska, 2014. "Performance of moving average trading strategies over varying stock market conditions: the Finnish evidence," Applied Economics, Taylor & Francis Journals, vol. 46(24), pages 2851-2872, August.

    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:gam:jjrfmx:v:15:y:2022:i:10:p:427-:d:924660. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.