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CRIX an Index for cryptocurrencies

Author

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  • Trimborn, Simon
  • Härdle, Wolfgang Karl

Abstract

The cryptocurrency market is unique on many levels: Very volatile, frequently changing market structure, emerging and vanishing of cryptocurrencies on a daily level. Following its development became a difficult task with the success of cryptocurrencies (CCs) other than Bitcoin. For fiat currency markets, the IMF offers the index SDR and, prior to the EUR, the ECU existed, which was an index representing the development of European currencies. Index providers decide on a fixed number of index constituents which will represent the market segment. It is a challenge to fix a number and develop rules for the constituents in view of the market changes. In the frequently changing CC market, this challenge is even more severe. A method relying on the AIC is proposed to quickly react to market changes and therefore enable us to create an index, referred to as CRIX, for the cryptocurrency market. CRIX is chosen by model selection such that it represents the market well to enable each interested party studying economic questions in this market and to invest into the market. The diversified nature of the CC market makes the inclusion of altcoins in the index product critical to improve tracking performance. We have shown that assigning optimal weights to altcoins helps to reduce the tracking errors of a CC portfolio, despite the fact that their market cap is much smaller relative to Bitcoin. The codes used here are available via www.quantlet.de.

Suggested Citation

  • Trimborn, Simon & Härdle, Wolfgang Karl, 2020. "CRIX an Index for cryptocurrencies," IRTG 1792 Discussion Papers 2020-009, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    • Handle: RePEc:zbw:irtgdp:2020009
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    References listed on IDEAS

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    1. Wilko Bolt & Maarten R.C. Van Oordt, 2020. "On the Value of Virtual Currencies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(4), pages 835-862, June.
    2. Horton, Nicholas J. & Kleinman, Ken P., 2007. "Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models," The American Statistician, American Statistical Association, vol. 61, pages 79-90, February.
    3. 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.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. repec:hum:wpaper:sfb649dp2016-038 is not listed on IDEAS
    6. repec:hum:wpaper:sfb649dp2015-048 is not listed on IDEAS
    7. Kanazawa, Yuichiro, 1993. "Hellinger distance and Kullback--Leibler loss for the kernel density estimator," Statistics & Probability Letters, Elsevier, vol. 18(4), pages 315-321, November.
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    Keywords

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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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