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CRIX or evaluating blockchain based currencies

Author

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  • Härdle, Wolfgang Karl
  • Trimborn, Simon

Abstract

More and more companies start offering digital payment systems. Smartphones evolve to a digital wallet such that it seems like we are about to enter the era of digital finance. In fact we are already inside an digital economy. The market of e-x (x = "finance", "money", "book", you name it ... ) has not only picked up enormous momentum but has become standard for driving innovative activities of the global economy. A few clicks at y and payment at z brings our purchase to location w. Own currencies for the digital market were therefore just a matter of time. The idea of the Nobel Laureate Hayek, see [1], to let companies offer concurrent currencies seemed for a long time scarcely probabilistic, but the invention of the Blockchain made it possible to fill his vision with life. Cryptocurrencies (abbr. cryptos) came up and widened the angle towards this new level of economic interaction. Since bitcoins' appearance a bunch of new cryptos spread the web and offered new ways of proliferation. The crypto market then fanned out and showed clear signs of acceptance and deep liquidity so that one has to look closer at the general moves and dynamics.

Suggested Citation

  • 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.
    • Handle: RePEc:zbw:sfb649:sfb649dp2015-048
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    References listed on IDEAS

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    1. Potapov, Alex & Muirhead, Jim R. & Lele, Subhash R. & Lewis, Mark A., 2011. "Stochastic gravity models for modeling lake invasions," Ecological Modelling, Elsevier, vol. 222(4), pages 964-972.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
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    Cited by:

    1. Ilyas Agakishiev & Wolfgang Karl Härdle & Denis Becker & Xiaorui Zuo, 2025. "Regime switching forecasting for cryptocurrencies," Digital Finance, Springer, vol. 7(1), pages 107-131, March.

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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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