Cryptocurrency Portfolio Allocation under Credibilistic CVaR Criterion and Practical Constraints

Hossein Ghanbari, Emran Mohammadi (), Amir Mohammad Larni Fooeik, Ronald Kumar, Peter Josef Stauvermann and Mostafa Shabani
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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
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

Risks, 2024, vol. 12, issue 10, 1-25

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.

Keywords: portfolio optimization; conditional value at risk; fuzzy uncertainty; credibility theory; cryptocurrency market; practical constraints (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
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