IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v12y2024i10p163-d1496782.html
   My bibliography  Save this article

Cryptocurrency Portfolio Allocation under Credibilistic CVaR Criterion and Practical Constraints

Author

Listed:
  • Hossein Ghanbari

    (Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran)

  • Emran Mohammadi

    (Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran)

  • Amir Mohammad Larni Fooeik

    (Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran)

  • Ronald Ravinesh Kumar

    (Department of Economics and Finance, The Business School, RMIT University, Saigon South Campus, Ho Chi Minh City 700000, Vietnam)

  • Peter Josef Stauvermann

    (School of Global Business & Economics, Changwon National University, Gyeongnam, 9, Sarim Dong, Changwon 641-773, Republic of Korea)

  • Mostafa Shabani

    (Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran)

Abstract

The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the model’s effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape.

Suggested Citation

  • Hossein Ghanbari & Emran Mohammadi & Amir Mohammad Larni Fooeik & Ronald Ravinesh Kumar & Peter Josef Stauvermann & Mostafa Shabani, 2024. "Cryptocurrency Portfolio Allocation under Credibilistic CVaR Criterion and Practical Constraints," Risks, MDPI, vol. 12(10), pages 1-25, October.
  • Handle: RePEc:gam:jrisks:v:12:y:2024:i:10:p:163-:d:1496782
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/12/10/163/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/12/10/163/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bouri, Elie & Shahzad, Syed Jawad Hussain & Roubaud, David, 2019. "Co-explosivity in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 29(C), pages 178-183.
    2. Nick James & Max Menzies, 2023. "Collective dynamics, diversification and optimal portfolio construction for cryptocurrencies," Papers 2304.08902, arXiv.org, revised Jun 2023.
    3. Sulalitha Bowala & Japjeet Singh, 2022. "Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures," JRFM, MDPI, vol. 15(10), pages 1-16, September.
    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. Chhatwani, Malvika & Parija, Arpit Kumar, 2023. "Who invests in cryptocurrency? The role of overconfidence among American investors," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 107(C).
    2. KOUAKOU, Thiédjé Gaudens-Omer, 2025. "Volatilité et régulation des cryptomonnaies : approche monétaire orthodoxe versus approche monétaire hétérodoxe [Volatility and regulation of cryptocurrencies: orthodox monetary approach versus het," MPRA Paper 123774, University Library of Munich, Germany.
    3. Cervera, Ignacio & Figuerola-Ferretti, Isabel, 2024. "Credit risk and bubble behavior of credit default swaps in the corporate energy sector," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 702-731.
    4. 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).
    5. Moratis, George, 2021. "Quantifying the spillover effect in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 38(C).
    6. Shahzad, Syed Jawad Hussain & Bouri, Elie & Ahmad, Tanveer & Naeem, Muhammad Abubakr & Vo, Xuan Vinh, 2021. "The pricing of bad contagion in cryptocurrencies: A four-factor pricing model," Finance Research Letters, Elsevier, vol. 41(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. Kingstone Nyakurukwa & Yudhvir Seetharam, 2023. "Higher moment connectedness of cryptocurrencies: a time-frequency approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 793-814, September.
    9. Fruehwirt, Wolfgang & Hochfilzer, Leonhard & Weydemann, Leonard & Roberts, Stephen, 2021. "Cumulation, crash, coherency: A cryptocurrency bubble wavelet analysis," Finance Research Letters, Elsevier, vol. 40(C).
    10. Yin, Libo & Nie, Jing & Han, Liyan, 2021. "Understanding cryptocurrency volatility: The role of oil market shocks," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 233-253.
    11. Dora Almeida & Andreia Dionísio & Paulo Ferreira & Isabel Vieira, 2023. "Impact of the COVID-19 Pandemic on Cryptocurrency Markets: A DCCA Analysis," FinTech, MDPI, vol. 2(2), pages 1-17, May.
    12. Kirill D. Shilov & Andrei V. Zubarev, 2023. "Factors of Ethereum Profitability as a Platform for Creating Decentrilized Applications," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 95-115, February.
    13. Bennett, Donyetta & Mekelburg, Erik & Williams, T.H., 2023. "BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing," Research in International Business and Finance, Elsevier, vol. 65(C).
    14. Erdinc Akyildirim & Ahmet Goncu & Ahmet Sensoy, 2021. "Prediction of cryptocurrency returns using machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 3-36, February.
    15. Marcin Wk{a}torek & Marcin Kr'olczyk & Jaros{l}aw Kwapie'n & Tomasz Stanisz & Stanis{l}aw Dro.zd.z, 2024. "Approaching multifractal complexity in decentralized cryptocurrency trading," Papers 2411.05951, arXiv.org.
    16. Muhammad MOHSIN & Sobia NASEEM & Larisa IVAȘCU & Lucian-Ionel CIOCA & Muddassar SARFRAZ & Nicolae Cristian STĂNICĂ, 2021. "Gauging the Effect of Investor Sentiment on Cryptocurrency Market: An Analysis of Bitcoin Currency," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 87-102, December.
    17. Marta Maciejasz & Robert Poskart, 2022. "Percepcja kryptowalut przez młodych uczestników rynku finansowego na przykładzie Polski i Niemiec," Bank i Kredyt, Narodowy Bank Polski, vol. 53(6), pages 625-650.
    18. Zhang, Lei & Bouri, Elie & Chen, Yan, 2023. "Co-jump dynamicity in the cryptocurrency market: A network modelling perspective," Finance Research Letters, Elsevier, vol. 58(PB).
    19. Ante, Lennart & Fiedler, Ingo & Strehle, Elias, 2021. "The impact of transparent money flows: Effects of stablecoin transfers on the returns and trading volume of Bitcoin," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    20. Bouraoui, Taoufik, 2020. "The drivers of Bitcoin trading volume in selected emerging countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 218-229.

    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:jrisks:v:12:y:2024:i:10:p:163-:d:1496782. 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.