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Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures

Author

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

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