IDEAS home Printed from https://ideas.repec.org/a/ist/journl/v73y2023i1p615-643.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/AEB6139028434A0F86F8C77C75810430
    Download Restriction: no
    File URL: https://iupress.istanbul.edu.tr/en/journal/ije/article/kripto-paralarin-volatilite-dinamiklerinin-ve-spekulatif-balon-varliginin-analizi-bitcoin-ethereum-ve-ripple-ornegi
    Download Restriction: no
    File URL: https://libkey.io/10.26650/ISTJECON2023-1021393?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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," 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    2. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben & Chevallier, Julien, 2020. "Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models," Finance Research Letters, Elsevier, vol. 35(C).
    3. Tetsuya Takaishi, 2021. "Time-varying properties of asymmetric volatility and multifractality in Bitcoin," Papers 2102.07425, arXiv.org.
    4. Amin Aminimehr & Ali Raoofi & Akbar Aminimehr & Amirhossein Aminimehr, 2022. "A Comprehensive Study of Market Prediction from Efficient Market Hypothesis up to Late Intelligent Market Prediction Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 781-815, August.
    5. Efthymios Pavlidis & Alisa Yusupova & Ivan Paya & David Peel & Enrique Martínez-García & Adrienne Mack & Valerie Grossman, 2016. "Episodes of Exuberance in Housing Markets: In Search of the Smoking Gun," The Journal of Real Estate Finance and Economics, Springer, vol. 53(4), pages 419-449, November.
    6. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911.
    7. Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
    8. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    9. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, June.
    10. Hira Aftab & A. B. M. Rabiul Alam Beg, 2021. "Does Time Varying Risk Premia Exist in the International Bond Market? An Empirical Evidence from Australian and French Bond Market," IJFS, MDPI, vol. 9(1), pages 1-13, January.
    11. Efthymios Pavlidis & Alisa Yusupova & Ivan Paya & David Peel & Enrique Martinez-Garcia & Adrienne Mack & Valerie Crossman, 2014. "Episodes of exuberance in housing markets," Working Papers 64908732, Lancaster University Management School, Economics Department.
    12. Bohl, Martin T. & Diesteldorf, Jeanne & Siklos, Pierre L., 2015. "The effect of index futures trading on volatility: Three markets for Chinese stocks," China Economic Review, Elsevier, vol. 34(C), pages 207-224.
    13. Caporale, Guglielmo Maria & Zekokh, Timur, 2019. "Modelling volatility of cryptocurrencies using Markov-Switching GARCH models," Research in International Business and Finance, Elsevier, vol. 48(C), pages 143-155.
    14. Vahidin Jeleskovic & Mirko Meloni & Zahid Irshad Younas, 2020. "Cryptocurrencies: A Copula Based Approach for Asymmetric Risk Marginal Allocations," MAGKS Papers on Economics 202034, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    15. Aharon, David Y. & Butt, Hassan Anjum & Jaffri, Ali & Nichols, Brian, 2023. "Asymmetric volatility in the cryptocurrency market: New evidence from models with structural breaks," International Review of Financial Analysis, Elsevier, vol. 87(C).
    16. Pinar Deniz & Thanasis Stengos, 2020. "Cryptocurrency Returns before and after the Introduction of Bitcoin Futures," JRFM, MDPI, vol. 13(6), pages 1-21, June.
    17. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    18. Cristina Chinazzo & Vahidin Jeleskovic, 2024. "Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches," Papers 2401.02049, arXiv.org.
    19. Roy Cerqueti & Massimiliano Giacalone & Raffaele Mattera, 2020. "Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling," Papers 2004.11674, arXiv.org.
    20. Keunbae Ahn, 2021. "Predictable Fluctuations in the Cross-Section and Time-Series of Asset Prices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2021.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ist:journl:v:73:y:2023:i:1:p:615-643. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ertugrul YASAR (email available below). General contact details of provider: https://edirc.repec.org/data/ifisttr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.