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Analyzing the Volatility Dynamics of Crypto Currency and the Occurrence of Speculative Bubbles: The Examples of Bitcoin, Ethereum, and Ripple

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  • Utku Altunoz

    (Sinop University Boyabat Economics Faculty of Administrative Sciences, Economics, Sinop, Turkiye)

Abstract

This study aims to model the volatility features of Bitcoin, Ethereum, and Ripple, which are the cryptocurrencies with the greatest volumes that have come to the agenda since the global crisis, and to determine the presence and dates of price bubbles.After running the ADF and Ng-Perron unit root tests, the EGARCH model was analyzed as the best for Bitcoin and TGARCH for the Ethereum and Ripple. According to the obtained results, negative coefficients for Bitcoin imply that negative shocks will increase volatility more than positive shocks. This means that a leverage effect is present. No leverage effect was reached for Ethereum or Ripple, and positive shocks are understood to increase volatility for them compared to negative shocks. In addition, continuous speculative bubble pricing occurred for all three cryptocurrencies, with much higher bubble prices being understood to have occurred with Ethereum and Bitcoin compared to Ripple.

Suggested Citation

  • Utku Altunoz, 2023. "Analyzing the Volatility Dynamics of Crypto Currency and the Occurrence of Speculative Bubbles: The Examples of Bitcoin, Ethereum, and Ripple," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 73(73-1), pages 615-643, June.
  • Handle: RePEc:ist:journl:v:73:y:2023:i:1:p:615-643
    DOI: 10.26650/ISTJECON2023-1021393
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    References listed on IDEAS

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    1. Charles, Amélie & Darné, Olivier, 2019. "Volatility estimation for Bitcoin: Replication and robustness," International Economics, Elsevier, vol. 157(C), pages 23-32.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Peter C. B. Phillips & Jun Yu, 2011. "Dating the timeline of financial bubbles during the subprime crisis," Quantitative Economics, Econometric Society, vol. 2(3), pages 455-491, November.
    4. Olivier J. Blanchard & Mark W. Watson, 1982. "Bubbles, Rational Expectations and Financial Markets," NBER Working Papers 0945, National Bureau of Economic Research, Inc.
    5. Hart, Oliver D & Kreps, David M, 1986. "Price Destabilizing Speculation," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 927-952, October.
    6. Peter M. DeMarzo & Ron Kaniel & Ilan Kremer, 2008. "Relative Wealth Concerns and Financial Bubbles," The Review of Financial Studies, Society for Financial Studies, vol. 21(1), pages 19-50, January.
    7. Shiller, Robert J, 1981. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," American Economic Review, American Economic Association, vol. 71(3), pages 421-436, June.
    8. Kohn, Meir, 1978. "Competitive Speculation," Econometrica, Econometric Society, vol. 46(5), pages 1061-1076, September.
    9. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    12. Campbell, John Y & Shiller, Robert J, 1987. "Cointegration and Tests of Present Value Models," Journal of Political Economy, University of Chicago Press, vol. 95(5), pages 1062-1088, October.
    13. Jamal Bouoiyour & Refk Selmi, 2016. "Bitcoin: a beginning of a new phase?," Economics Bulletin, AccessEcon, vol. 36(3), pages 1430-1440.
    14. Dyhrberg, Anne Haubo, 2016. "Hedging capabilities of bitcoin. Is it the virtual gold?," Finance Research Letters, Elsevier, vol. 16(C), pages 139-144.
    15. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item

    Keywords

    Cryptocurrency; Volatility; Financial Bubble; Ethereum; Ripple; Bitcoin JEL Classification : C01 ; C13 ; C51 ; E42;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System

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