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On the dynamic equicorrelations in cryptocurrency market

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  • Demiralay, Sercan
  • Golitsis, Petros

Abstract

This paper investigates the time-varying co-movements in cryptocurrency market, employing a Dynamic Equicorrelation GARCH (DECO-GARCH) model, before and during the COVID-19 pandemic. Our results suggest that the equicorrelations are time-varying and highly responsive to major events, such as hacker attacks and government bans. The results lend support to the recent claim that interlinkages among cryptocurrencies have become stronger, particularly after mid-2017, with substantially increased trading activity in the market. The equicorrelations reach their peak in March 2020, after the official declaration of the World Health Organization (WHO) that novel coronavirus outbreak becomes a global pandemic, indicating potential contagion effects. We also examine the determinants of the market linkages and find that increased Bitcoin trading volume, attention-driven demand for Bitcoin and risk aversion significantly increase the equicorrelations during the COVID-19 bear market. Our results provide potential implications for investors, traders and policy makers and help improve their understanding of the cryptocurrency market’s behavior during times of extreme market stress.

Suggested Citation

  • Demiralay, Sercan & Golitsis, Petros, 2021. "On the dynamic equicorrelations in cryptocurrency market," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 524-533.
  • Handle: RePEc:eee:quaeco:v:80:y:2021:i:c:p:524-533
    DOI: 10.1016/j.qref.2021.04.002
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    2. Samia Nasreen & Aviral Kumar Tiwari & Seong-Min Yoon, 2021. "Dynamic Connectedness and Portfolio Diversification during the Coronavirus Disease 2019 Pandemic: Evidence from the Cryptocurrency Market," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
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    4. Golitsis, Petros & Gkasis, Pavlos & Bellos, Sotirios K., 2022. "Dynamic spillovers and linkages between gold, crude oil, S&P 500, and other economic and financial variables. Evidence from the USA," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    5. Kumar, Ashish & Iqbal, Najaf & Mitra, Subrata Kumar & Kristoufek, Ladislav & Bouri, Elie, 2022. "Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    6. Assaf, Ata & Charif, Husni & Demir, Ender, 2022. "Information sharing among cryptocurrencies: Evidence from mutual information and approximate entropy during COVID-19," Finance Research Letters, Elsevier, vol. 47(PA).
    7. Elsayed, Ahmed H. & Ahmed, Habib & Husam Helmi, Mohamad, 2023. "Determinants of financial stability and risk transmission in dual financial system: Evidence from the COVID pandemic," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    8. Lee, Seungju & Lee, Jaewook & Lee, Yunyoung, 2023. "Dissecting the Terra-LUNA crash: Evidence from the spillover effect and information flow," Finance Research Letters, Elsevier, vol. 53(C).
    9. Jinxin Cui & Aktham Maghyereh, 2022. "Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-56, December.
    10. Nuray Tosunoğlu & Hilal Abacı & Gizem Ateş & Neslihan Saygılı Akkaya, 2023. "Artificial neural network analysis of the day of the week anomaly in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-24, December.
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    More about this item

    Keywords

    Cryptocurrencies; DECO-GARCH; Trading volume; Investor attention;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G19 - Financial Economics - - General Financial Markets - - - Other

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