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Volatility spillovers and other dynamics between cryptocurrencies and the energy and bond markets

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  • Bouteska, Ahmed
  • Sharif, Taimur
  • Abedin, Mohammad Zoynul

Abstract

On a univariate setting, this study aims to: (a) model the volatility of Bitcoin, Dash, Monero, and Stellar, (b) check the eventual existence of structural breaks in their volatility, and (c) investigate the interconnection amid the cryptocurrency volatilities, the US equity and bond markets’ volatility, and the COVID-19 impacts. To accomplish these objectives, we adopt a comparative approach to select the GARCH model, use Inclán and Tiao’s (1994) Iterated Cumulative Sums of Squares (ICSS) algorithm, and then estimate a Simultaneous Equation Model (SEM), respectively. We find convincing evidence of the existence of return-volatility spillovers amid Bitcoin, Dash, and Stellar, and the role of Monero as the principal transmitter of shocks. We also observe a nexus of the cryptocurrency market with the US energy market but do not see any connectivity with the US bond market. Furthermore, we suggest that the observed period of high financial uncertainty, low economic sentiment, and the pandemic-led problems in the US energy market exert significant impact on the prices of Bitcoin, Dash, and Stellar, mainly receiving short-lived shocks. The findings of this paper have implications for fund managers and policymakers to ensure a right timing of their intervention and minimize risks during uncertainties in the wake of the Black Swan events, such as the COVID-19, the Russia-Ukraine conflict, etc.

Suggested Citation

  • Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "Volatility spillovers and other dynamics between cryptocurrencies and the energy and bond markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 1-13.
    • Handle: RePEc:eee:quaeco:v:92:y:2023:i:c:p:1-13
    DOI: 10.1016/j.qref.2023.07.008
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    More about this item

    Keywords

    Cryptocurrencies; Energy market; Bond market; Volatility spillovers; COVID-19;
    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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