IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/65580.html
   My bibliography  Save this paper

Bitcoin Price: Is it really that New Round of Volatility can be on way?

Author

Listed:
  • Bouoiyour, Jamal
  • Selmi, Refk

Abstract

To the mass public, Bitcoin is well known since its creation by its extreme volatility. However, Bitcoin’s declining fluctuations since the start 2015 has revived our attention to assess whether there is a coming Bitcoin market phase. Using an optimal GARCH model on daily data, we show that the volatility of Bitcoin price decreases notably when comparing the periods [December 2010-June 2015] and [January 2015-June 2015]. During the first interval, the Threshold- GARCH estimates reveal that there is a great duration of persistence and thus tends to follow a long memory process. For the second period, the chosen specification (Exponential-GARCH) displays less volatility persistence. Despite this remarkable volatility’s decrease, we cannot argue that Bitcoin market is mature, since the degree of asymmetry remains strong; Specifically, Bitcoin is likely to be driven by negative rather than positive shocks.

Suggested Citation

  • Bouoiyour, Jamal & Selmi, Refk, 2015. "Bitcoin Price: Is it really that New Round of Volatility can be on way?," MPRA Paper 65580, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:65580
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/65580/1/MPRA_paper_65580.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bauwens Luc & Storti Giuseppe, 2009. "A Component GARCH Model with Time Varying Weights," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-33, May.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    4. Jamal Bouoiyour & Refk Selmi, 2015. "What Does Bitcoin Look Like?," Annals of Economics and Finance, Society for AEF, vol. 16(2), pages 449-492, November.
    5. David Yermack, 2013. "Is Bitcoin a Real Currency? An economic appraisal," NBER Working Papers 19747, National Bureau of Economic Research, Inc.
    6. Bouoiyour, Jamal & Selmi, Refk, 2015. "Greece withdraws from Euro and runs on Bitcoin; April Fools Prank or Serious Possibility?," MPRA Paper 65317, University Library of Munich, Germany.
    7. Bouoiyour, Jamal & Selmi, Refk, 2014. "What Bitcoin Looks Like?," MPRA Paper 58091, University Library of Munich, Germany.
    8. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    9. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Higgins, Matthew L & Bera, Anil K, 1992. "A Class of Nonlinear ARCH Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 33(1), pages 137-158, February.
    12. Jamal Bouoiyour & Refk Selmi, 2014. "Commodity price uncertainty and manufactured exports in Morocco and Tunisia: Some insights from a novel GARCH model," Economics Bulletin, AccessEcon, vol. 34(1), pages 220-233.
    13. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Panagiotidis, Theodore & Papapanagiotou, Georgios & Stengos, Thanasis, 2022. "On the volatility of cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 62(C).
    2. Klaus Grobys, 2021. "When the blockchain does not block: on hackings and uncertainty in the cryptocurrency market," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1267-1279, August.
    3. Bildirici, Melike E. & Sonustun, Bahri, 2021. "Chaotic behavior in gold, silver, copper and bitcoin prices," Resources Policy, Elsevier, vol. 74(C).
    4. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    5. Leandro Maciel, 2021. "Cryptocurrencies value‐at‐risk and expected shortfall: Do regime‐switching volatility models improve forecasting?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4840-4855, July.
    6. Salisu, Afees A. & Ogbonna, Ahamuefula E., 2022. "The return volatility of cryptocurrencies during the COVID-19 pandemic: Assessing the news effect," Global Finance Journal, Elsevier, vol. 54(C).
    7. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    8. Noshaba Zulfiqar & Saqib Gulzar, 2021. "Implied volatility estimation of bitcoin options and the stylized facts of option pricing," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    9. Nader Trabelsi, 2018. "Are There Any Volatility Spill-Over Effects among Cryptocurrencies and Widely Traded Asset Classes?," JRFM, MDPI, vol. 11(4), pages 1-17, October.
    10. Bergsli, Lykke Øverland & Lind, Andrea Falk & Molnár, Peter & Polasik, Michał, 2022. "Forecasting volatility of Bitcoin," Research in International Business and Finance, Elsevier, vol. 59(C).
    11. Caporale, Guglielmo Maria & Gil-Alana, Luis & Plastun, Alex, 2018. "Persistence in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 46(C), pages 141-148.
    12. Zhou, Siwen, 2018. "Exploring the Driving Forces of the Bitcoin Exchange Rate Dynamics: An EGARCH Approach," MPRA Paper 89445, University Library of Munich, Germany.
    13. Nidhi Malhotra & Saumya Gupta, 2019. "Volatility Spillovers and Correlation Between Cryptocurrencies and Asian Equity Market," International Journal of Economics and Financial Issues, Econjournals, vol. 9(6), pages 208-215.
    14. Gregor Dorfleitner & Carina Lung, 2018. "Cryptocurrencies from the perspective of euro investors: a re-examination of diversification benefits and a new day-of-the-week effect," Journal of Asset Management, Palgrave Macmillan, vol. 19(7), pages 472-494, December.
    15. Chappell, Daniel, 2018. "Regime heteroskedasticity in Bitcoin: A comparison of Markov switching models," MPRA Paper 90682, University Library of Munich, Germany.
    16. Kumar, Anoop S. & Anandarao, S., 2019. "Volatility spillover in crypto-currency markets: Some evidences from GARCH and wavelet analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 448-458.
    17. Fakhfekh, Mohamed & Jeribi, Ahmed, 2020. "Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models," Research in International Business and Finance, Elsevier, vol. 51(C).
    18. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    19. Gil-Alana, Luis Alberiko & Abakah, Emmanuel Joel Aikins & Rojo, María Fátima Romero, 2020. "Cryptocurrencies and stock market indices. Are they related?," Research in International Business and Finance, Elsevier, vol. 51(C).
    20. Beata Szetela & Grzegorz Mentel & Stanislaw Gedek, 2016. "Dependency analysis between Bitcoin and selected global currencies," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 16, pages 133-144.
    21. M. Safiullin R. & A. Abdukaeva A. & L. El’shin A. & М. Сафиуллин Р. & А. Абдукаева А. & Л. Ельшин А., 2018. "Методические Подходы К Прогнозированию Динамики Курса Криптовалют С Применением Инструментов Стохастического Анализа (На Примере Биткоина) // Methodological Approaches To Forecasting Dynamics Of Crypt," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 22(4), pages 38-51.
    22. Shazia Salamat & Niu Lixia & Sobia Naseem & Muhammad Mohsin & Muhammad Zia-ur-Rehman & Sajjad Ahmad Baig, 2020. "Modeling cryptocurrencies volatility using GARCH models: a comparison based on Normal and Student's T-Error distribution," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(3), pages 1580-1596, March.
    23. Ze Shen & Qing Wan & David J. Leatham, 2021. "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN," JRFM, MDPI, vol. 14(7), pages 1-18, July.
    24. Ahmed Jeribi & Mohamed Fakhfekh, 2021. "Portfolio management and dependence structure between cryptocurrencies and traditional assets: evidence from FIEGARCH-EVT-Copula," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 224-239, May.
    25. Jeffrey Chu & Stephen Chan & Saralees Nadarajah & Joerg Osterrieder, 2017. "GARCH Modelling of Cryptocurrencies," JRFM, MDPI, vol. 10(4), pages 1-15, October.

    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. Jamal Bouoiyour & Refk Selmi, 2016. "Bitcoin: a beginning of a new phase?," Economics Bulletin, AccessEcon, vol. 36(3), pages 1430-1440.
    2. Bouoiyour, Jamal & Miftah, Amal & Selmi, Refk, 2014. "Do Financial Flows raise or reduce Economic growth Volatility? Some Lessons from Moroccan case," MPRA Paper 57258, University Library of Munich, Germany.
    3. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    4. Jamal Bouoiyour & Refk Selmi, 2017. "Ether: Bitcoin's competitor or ally?," Papers 1707.07977, arXiv.org.
    5. Köksal, Bülent, 2009. "A Comparison of Conditional Volatility Estimators for the ISE National 100 Index Returns," MPRA Paper 30510, University Library of Munich, Germany.
    6. Siwen Zhou, 2021. "Exploring the driving forces of the Bitcoin currency exchange rate dynamics: an EGARCH approach," Empirical Economics, Springer, vol. 60(2), pages 557-606, February.
    7. Marcel Bräutigam & Marie Kratz, 2019. "Bivariate FCLT for the Sample Quantile and Measures of Dispersion for Augmented GARCH(p, q) processes," Working Papers hal-02176276, HAL.
    8. Marcel, Bräutigam & Marie, Kratz, 2019. "Bivariate FCLT for the Sample Quantile and Measures of Dispersion for Augmented GARCH(p, q) processes," ESSEC Working Papers WP1909, ESSEC Research Center, ESSEC Business School.
    9. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.
    10. Waldemar Tarczyński & Sebastian Majewski & Małgorzata Tarczyńska-Łuniewska & Agnieszka Majewska & Grzegorz Mentel, 2021. "The Impact of Weather Factors on Quotations of Energy Sector Companies on Warsaw Stock Exchange," Energies, MDPI, vol. 14(6), pages 1-14, March.
    11. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    12. Azimi, Mohammad Naim, 2015. "Modelling the Clustering Volatility of India's Wholesales Price Index and the Factors Affecting it," MPRA Paper 70267, University Library of Munich, Germany.
    13. Brooks, Robert D. & Faff, Robert W. & McKenzie, Michael D. & Mitchell, Heather, 2000. "A multi-country study of power ARCH models and national stock market returns," Journal of International Money and Finance, Elsevier, vol. 19(3), pages 377-397, June.
    14. Jamal Bouoiyour & Refk Selmi, 2015. "Exchange volatility and export performance in Egypt: New insights from wavelet decomposition and optimal GARCH model," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 24(2), pages 201-227, March.
    15. Vacca, Gianmarco & Zoia, Maria Grazia & Bagnato, Luca, 2022. "Forecasting in GARCH models with polynomially modified innovations," International Journal of Forecasting, Elsevier, vol. 38(1), pages 117-141.
    16. Turgut Kısınbay, 2010. "Predictive ability of asymmetric volatility models at medium-term horizons," Applied Economics, Taylor & Francis Journals, vol. 42(30), pages 3813-3829.
    17. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    18. Gerrit Reher & Bernd Wilfling, 2016. "A nesting framework for Markov-switching GARCH modelling with an application to the German stock market," Quantitative Finance, Taylor & Francis Journals, vol. 16(3), pages 411-426, March.
    19. Stelios Arvanitis & Antonis Demos, 2004. "Time Dependence and Moments of a Family of Time‐Varying Parameter Garch in Mean Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(1), pages 1-25, January.
    20. Milton Abdul Thorlie & Lixin Song & Muhammad Amin & Xiaoguang Wang, 2015. "Modeling and forecasting of stock index volatility with APARCH models under ordered restriction," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 329-356, August.

    More about this item

    Keywords

    Bitcoin; volatility; optimal GARCH model.;
    All these keywords.

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • F3 - International Economics - - International Finance

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:65580. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.