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Indices on cryptocurrencies: An evaluation

Author

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  • Häusler, Konstantin
  • Xia, Hongyu
Abstract
Several cryptocurrency (CC) indices track the dynamics of the rising CC sector, and soon ETFs will be issued on them. We conduct a qualitative and quantitative evaluation of the currently existing CC indices. As the CC sector is not yet consolidated, index issuers face the challenge of tracking the dynamics of a fast-growing sector that is under continuous transformation. We propose several criteria and various measures to compare the indices under review. Major differences between the indices lie in their weighting schemes, their coverage of CCs and the number of constituents, the level of transparency, and thus their accuracy in mapping the dynamics of the CC sector. Our analysis reveals that indices that adapt dynamically to this rising sector outperform their competitors. Interestingly, increasing the number of constituents does not automatically lead to a better fit of the CC sector.

Suggested Citation

  • Häusler, Konstantin & Xia, Hongyu, 2021. "Indices on cryptocurrencies: An evaluation," IRTG 1792 Discussion Papers 2021-014, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  • Handle: RePEc:zbw:irtgdp:2021014
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    References listed on IDEAS

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    1. Wolfgang Karl Härdle & Campbell R Harvey & Raphael C G Reule, 2020. "Understanding Cryptocurrencies," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 181-208.
    2. Georg Keilbar & Yanfen Zhang, 2021. "On cointegration and cryptocurrency dynamics," Digital Finance, Springer, vol. 3(1), pages 1-23, March.
    3. Wang, Bingling & Li, Yingxing & Härdle, Wolfgang Karl, 2022. "K-expectiles clustering," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    4. Trimborn, Simon & Härdle, Wolfgang Karl, 2018. "CRIX an Index for cryptocurrencies," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 107-122.
    5. Elizaveta Zinovyeva & Raphael C. G. Reule & Wolfgang Karl Hardle, 2021. "Understanding Smart Contracts: Hype or Hope?," Papers 2103.08447, arXiv.org.
    6. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    7. Jacob, Daniel, 2021. "CATE meets ML: Conditional average treatment effect and machine learning," IRTG 1792 Discussion Papers 2021-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Wei Zhang & Pengfei Wang & Xiao Li & Dehua Shen, 2018. "Some stylized facts of the cryptocurrency market," Applied Economics, Taylor & Francis Journals, vol. 50(55), pages 5950-5965, November.
    9. Albert S. Hu & Christine A. Parlour & Uday Rajan, 2019. "Cryptocurrencies: Stylized facts on a new investible instrument," Financial Management, Financial Management Association International, vol. 48(4), pages 1049-1068, December.
    10. Härdle, Wolfgang Karl & Trimborn, Simon, 2015. "CRIX or evaluating blockchain based currencies," SFB 649 Discussion Papers 2015-048, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Sha Wang & Jean-Philippe Vergne, 2017. "Buzz Factor or Innovation Potential: What Explains Cryptocurrencies’ Returns?," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-17, January.
    12. Ai Jun Hou & Weining Wang & Cathy Y H Chen & Wolfgang Karl Härdle, 2020. "Pricing Cryptocurrency Options," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 250-279.
    13. Chen, Yi-Hsuan & Vinogradov, Dmitri V., 2021. "Coins with benefits: On existence, pricing kernel and risk premium of cryptocurrencies," IRTG 1792 Discussion Papers 2021-006, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    14. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
    15. Li, Erqian & Härdle, Wolfgang & Dai, Xiaowen & Tian, Maozai, 2021. "Penalized weigted competing risks models based on quantile regression," IRTG 1792 Discussion Papers 2021-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    16. Igor Rivin & Carlo Scevola, 2018. "The CCI30 Index," Papers 1804.06711, arXiv.org.
    17. Alla A. Petukhina & Raphael C. G. Reule & Wolfgang Karl Härdle, 2021. "Rise of the machines? Intraday high-frequency trading patterns of cryptocurrencies," The European Journal of Finance, Taylor & Francis Journals, vol. 27(1-2), pages 8-30, January.
    18. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
    19. Wei Li & Florentina Paraschiv & Georgios Sermpinis, 2022. "A data-driven explainable case-based reasoning approach for financial risk detection," Quantitative Finance, Taylor & Francis Journals, vol. 22(12), pages 2257-2274, December.
    20. Ren, Rui & Althof, Michael & Härdle, Wolfgang Karl, 2020. "Tail Risk Network Effects in the Cryptocurrency Market during the COVID-19 Crisis," IRTG 1792 Discussion Papers 2020-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    21. Mihoci, Andrija & Althof, Michael & Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl, 2019. "FRM Financial Risk Meter," IRTG 1792 Discussion Papers 2019-021, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    22. Vogt, Annette, 2021. "Von den Mühen der Ebenen und der Berge in den Wissenschaften," IRTG 1792 Discussion Papers 2021-009, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    23. Wolfgang Karl Hardle & Campbell R. Harvey & Raphael C. G. Reule, 2020. "Editorial: Understanding Cryptocurrencies," Papers 2007.14702, arXiv.org.
    24. Narasimhan Jegadeesh & Sheridan Titman, 2001. "Profitability of Momentum Strategies: An Evaluation of Alternative Explanations," Journal of Finance, American Finance Association, vol. 56(2), pages 699-720, April.
    25. Khowaja, Kainat & Shcherbatyy, Mykhaylo & Härdle, Wolfgang Karl, 2021. "Surrogate Models for Optimization of Dynamical Systems," IRTG 1792 Discussion Papers 2021-001, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    26. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
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    Cited by:

    1. Luis Lorenzo & Javier Arroyo, 2022. "Analysis of the cryptocurrency market using different prototype-based clustering techniques," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-46, December.
    2. Bilel Sanhaji & Julien Chevallier, 2023. "Tracking ‘Pure’ Systematic Risk with Realized Betas for Bitcoin and Ethereum," Econometrics, MDPI, vol. 11(3), pages 1-36, August.
    3. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Role of hedging on crypto returns predictability: A new habit-based explanation," Finance Research Letters, Elsevier, vol. 55(PB).
    4. Wang, Yizhi, 2022. "Volatility spillovers across NFTs news attention and financial markets," International Review of Financial Analysis, Elsevier, vol. 83(C).
    5. Konstantin Hausler, 2022. "ETF construction on CRIX," Papers 2211.15260, arXiv.org, revised Mar 2023.

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    Keywords

    Cryptocurrency; Index; Market Dynamics; Bitcoin;
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