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GARCH Modelling of High-Capitalization Cryptocurrencies' Impacts During Bearish Markets

Author

Listed:
  • Anastasiadis Panagiotis

    (Department of Economics, University of Thessaly, Volos, Greece)

  • Katsaros Efthymios

    (Department of Economics, University of Thessaly, Volos, Greece)

  • Koutsioukis Anastasios-Taxiarchis

    (Department of Economics, University of Thessaly, Volos, Greece)

  • Pandazis Athanasios

    (Department of Economics, University of Thessaly, Volos, Greece)

Abstract

This study investigates how twelve cryptocurrencies with large capitalization get influenced by the three cryptocurrencies with the largest market capitalization (Bitcoin, Ethereum, and Ripple). Twenty alternative specifications of ARCH, GARCH as well as DCC-GARCH are employed. Daily data covers the period from 1 January 1 2018 to 16 September 2018, representing the intense bearish cryptocurrency market. Empirical outcomes reveal that volatility among digital currencies is not best described by the same specification but varies according to the currency. It is evident that most cryptocurrencies have a positive relationship with Bitcoin, Ethereum and Ripple, therefore, there is no great possibility of hedging for cryptocurrency portfolio managers and investors in distressed times.

Suggested Citation

  • Anastasiadis Panagiotis & Katsaros Efthymios & Koutsioukis Anastasios-Taxiarchis & Pandazis Athanasios, 2020. "GARCH Modelling of High-Capitalization Cryptocurrencies' Impacts During Bearish Markets," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(3), pages 87-106.
  • Handle: RePEc:cbk:journl:v:9:y:2020:i:3:p:87-106
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    References listed on IDEAS

    as
    1. Nikolaos A. Kyriazis, 2020. "Is Bitcoin Similar to Gold? An Integrated Overview of Empirical Findings," JRFM, MDPI, vol. 13(5), pages 1-19, May.
    2. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    3. Nikola Fabris, 2018. "Challenges for Modern Monetary Policy," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 7(2), pages 5-24.
    4. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    5. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    6. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    7. Bouri, Elie & Azzi, Georges & Dyhrberg, Anne Haubo, 2017. "On the return-volatility relationship in the Bitcoin market around the price crash of 2013," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 11, pages 1-16.
    8. Dyhrberg, Anne Haubo, 2016. "Hedging capabilities of bitcoin. Is it the virtual gold?," Finance Research Letters, Elsevier, vol. 16(C), pages 139-144.
    9. Ammous, Saifedean, 2018. "Can cryptocurrencies fulfil the functions of money?," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 38-51.
    10. Kyriazis, Nikolaos & Papadamou, Stephanos & Corbet, Shaen, 2020. "A systematic review of the bubble dynamics of cryptocurrency prices," Research in International Business and Finance, Elsevier, vol. 54(C).
    11. Blau, Benjamin M., 2018. "Price dynamics and speculative trading in Bitcoin," Research in International Business and Finance, Elsevier, vol. 43(C), pages 15-21.
    12. Angelo Corelli, 2018. "Cryptocurrencies and Exchange Rates: A Relationship and Causality Analysis," Risks, MDPI, vol. 6(4), pages 1-11, October.
    13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    14. Beneki, Christina & Koulis, Alexandros & Kyriazis, Nikolaos A. & Papadamou, Stephanos, 2019. "Investigating volatility transmission and hedging properties between Bitcoin and Ethereum," Research in International Business and Finance, Elsevier, vol. 48(C), pages 219-227.
    15. Stephen Chan & Jeffrey Chu & Saralees Nadarajah & Joerg Osterrieder, 2017. "A Statistical Analysis of Cryptocurrencies," JRFM, MDPI, vol. 10(2), pages 1-23, May.
    16. Marc Gronwald, 2014. "The Economics of Bitcoins - Market Characteristics and Price Jumps," CESifo Working Paper Series 5121, CESifo.
    17. Nikolaos A. Kyriazis, 2019. "A Survey on Empirical Findings about Spillovers in Cryptocurrency Markets," JRFM, MDPI, vol. 12(4), pages 1-17, November.
    18. Corbet, Shaen & Lucey, Brian & Urquhart, Andrew & Yarovaya, Larisa, 2019. "Cryptocurrencies as a financial asset: A systematic analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 182-199.
    19. Troster, Victor & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Macedo, Demian Nicolás, 2019. "Bitcoin returns and risk: A general GARCH and GAS analysis," Finance Research Letters, Elsevier, vol. 30(C), pages 187-193.
    20. 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

    Bitcoin; Ethereum; Ripple; Garch; Volatility; Bear market;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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