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Price Stability of Cryptocurrencies as a Medium of Exchange

Author

Listed:
  • Tatsuru Kikuchi

    (Faculty of Economics, The University of Tokyo)

  • Toranosuke Onishi

    (Tokio Marine & Nichido Fire Insurance Co., Ltd.)

  • Kenichi Ueda

    (Faculty of Economics, The University of Tokyo)

Abstract

We present positive evidence of price stability of cryptocurrencies as a medium of exchange. For the sample years from 2016 to 2020, the prices of major cryptocurrencies are found to be stable, relative to major financial assets. Specifically, after filtering out the less-than-one-month cycles, we investigate the daily returns in US dollars of the major cryptocurrencies (i.e., Bitcoin, Ethereum, and Ripple) as well as their comparators (i.e., major legal tenders, the Euro and Japanese yen, and the major stock indexes, S&P 500 and MSCI World Index). We examine the stability of the filtered daily returns using three different measures. First, the Pearson correlations increased in later years in our sample. Second, based on the dynamic time-warping method that allows lags and leads in relations, the similarities in the daily returns of cryptocurrencies with their comparators have been present even since 2016. Third, we check whether the cumulative sum of errors to predict cryptocurrency prices, assuming stable relations with comparators’ daily returns, does not exceeds the bounds implied by 1 the Black-Scholes model. This test, in other words, does not reject the efficient market hypothesis.

Suggested Citation

  • Tatsuru Kikuchi & Toranosuke Onishi & Kenichi Ueda, 2021. "Price Stability of Cryptocurrencies as a Medium of Exchange," CARF F-Series CARF-F-526, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  • Handle: RePEc:cfi:fseres:cf526
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    References listed on IDEAS

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    1. Achim Zeileis, 2004. "Alternative boundaries for CUSUM tests," Statistical Papers, Springer, vol. 45(1), pages 123-131, January.
    2. Kunitomo, Naoto & Sato, Seisho, 2013. "Separating Information Maximum Likelihood estimation of the integrated volatility and covariance with micro-market noise," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 282-309.
    3. Yhlas Sovbetov, 2018. "Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 2(2), pages 1-27.
    4. Leisch, Friedrich & Hornik, Kurt & Kuan, Chung-Ming, 2000. "Monitoring Structural Changes With The Generalized Fluctuation Test," Econometric Theory, Cambridge University Press, vol. 16(6), pages 835-854, December.
    5. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    6. Anne Morrison Piehl & Suzanne J. Cooper & Anthony A. Braga & David M. Kennedy, 2003. "Testing for Structural Breaks in the Evaluation of Programs," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 550-558, August.
    7. Graham Elliott & Ulrich K. Muller, 2006. "Efficient Tests for General Persistent Time Variation in Regression Coefficients," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 907-940.
    8. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    9. Lior Sidi, 2020. "Improving S&P stock prediction with time series stock similarity," Papers 2002.05784, arXiv.org.
    10. Philip Hans Franses & Thomas Wiemann, 2020. "Intertemporal Similarity of Economic Time Series: An Application of Dynamic Time Warping," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 59-75, June.
    11. Kiyotaki, Nobuhiro & Wright, Randall, 1989. "On Money as a Medium of Exchange," Journal of Political Economy, University of Chicago Press, vol. 97(4), pages 927-954, August.
    12. Chwe, Michael Suk-Young, 1999. "The Reeded Edge and the Phillips Curve: Money Neutrality, Common Knowledge, and Subjective Beliefs," Journal of Economic Theory, Elsevier, vol. 87(1), pages 49-71, July.
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    Cited by:

    1. Cong, Lin William & Mayer, Simon, 2022. "The Coming Battle of Digital Currencies," Applied Economics and Policy Working Paper Series 320020, Cornell University, Department of Applied Economics and Management.

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