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Bubbles and crashes in cryptocurrencies: Interdependence, contagion, or asset rotation?

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  • Chowdhury, Md Shahedur R.
  • Damianov, Damian S.
  • Elsayed, Ahmed H.

Abstract

Using a quantile vector autoregressive model to capture return dynamics in extreme market conditions, we find that the cryptocurrency market exhibits a high level of market connectedness. Bitcoin is a net transmitter of return spillovers during busts and a net receiver during booms. Analysis of the timing of bubble and crash periods uncovers the presence of interdependence and contagion effects. Asset dynamics is driven to a great extent by the technology, in particular the consensus protocol of cryptocurrencies. There is only limited evidence for asset rotation, and it involves mostly Ripple.

Suggested Citation

  • Chowdhury, Md Shahedur R. & Damianov, Damian S. & Elsayed, Ahmed H., 2022. "Bubbles and crashes in cryptocurrencies: Interdependence, contagion, or asset rotation?," Finance Research Letters, Elsevier, vol. 46(PB).
    • Handle: RePEc:eee:finlet:v:46:y:2022:i:pb:s1544612321004700
    DOI: 10.1016/j.frl.2021.102494
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    References listed on IDEAS

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    Cited by:

    1. Sun, Yiqun & Ji, Hao & Cai, Xiurong & Li, Jiangchen, 2023. "Joint extreme risk of energy prices-evidence from European energy markets," Finance Research Letters, Elsevier, vol. 56(C).
    2. Thomas F. P. Wiesen & Lakshya Bharadwaj, 2023. "Cryptocurrency Connectedness: Does Controlling for the Cross-Correlations Matter?," Applied Economics Letters, Taylor & Francis Journals, vol. 30(20), pages 2873-2880, November.
    3. 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.
    4. Cevik, Emrah Ismail & Gunay, Samet & Zafar, Muhammad Wasif & Destek, Mehmet Akif & Bugan, Mehmet Fatih & Tuna, Fatih, 2022. "The impact of digital finance on the natural resource market: Evidence from DeFi, oil, and gold," Resources Policy, Elsevier, vol. 79(C).
    5. Jareño, Francisco & Yousaf, Imran, 2023. "Artificial intelligence-based tokens: Fresh evidence of connectedness with artificial intelligence-based equities," International Review of Financial Analysis, Elsevier, vol. 89(C).
    6. Jie Cheng, 2023. "Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies," Empirical Economics, Springer, vol. 65(2), pages 899-924, August.
    7. Emmanuel Joel Aikins Abakah & Aviral Kumar Tiwari & Chi‐Chuan Lee & Matthew Ntow‐Gyamfi, 2023. "Quantile price convergence and spillover effects among Bitcoin, Fintech, and artificial intelligence stocks," International Review of Finance, International Review of Finance Ltd., vol. 23(1), pages 187-205, March.
    8. Bazán-Palomino, Walter & Svogun, Daniel, 2023. "On the drivers of technical analysis profits in cryptocurrency markets: A Distributed Lag approach," International Review of Financial Analysis, Elsevier, vol. 86(C).

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    More about this item

    Keywords

    Cryptocurrencies; Interdependence; Contagion; Rotation; Bubbles; Crashes;
    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
    • F3 - International Economics - - International Finance
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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