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Correlated at the Tail: Implications of Asymmetric Tail-Dependence Across Bitcoin Markets

Author

Listed:
  • Stelios Bekiros

    (European University Institute)

  • Axel Hedström

    (Linköping University)

  • Evgeniia Jayasekera

    (National College of Ireland)

  • Tapas Mishra

    (University of Southampton)

  • Gazi Salah Uddin

    (Linköping University)

Abstract

This paper is the first to fully characterize the relationship among cross-market Bitcoin prices to provide a complete picture of directional predictability of Bitcoin traded in various currencies across five developed markets. To exploit full-distributional dynamics, we employ Cross-quantilogram based Correlation and Dependence model to delve deep into the estimates an asymmetric tail dependence across quantiles would reflect on heterogeneous movement pattern of Bitcoin prices. A cross-quantilogram-based analysis reveals new empirical evidence of a heterogeneous tail dependence pattern: whereas Bitcoin-USD and the Northeast Asian market (viz., Japan) depicts a strong co-movement, smaller markets display weak connectedness and strong market-efficiency.

Suggested Citation

  • Stelios Bekiros & Axel Hedström & Evgeniia Jayasekera & Tapas Mishra & Gazi Salah Uddin, 2021. "Correlated at the Tail: Implications of Asymmetric Tail-Dependence Across Bitcoin Markets," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1289-1299, December.
    • Handle: RePEc:kap:compec:v:58:y:2021:i:4:d:10.1007_s10614-020-10058-6
    DOI: 10.1007/s10614-020-10058-6
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    References listed on IDEAS

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    Cited by:

    1. Muhammad Abubakr Naeem & Sitara Karim & Aviral Kumar Tiwari, 2023. "Risk Connectedness Between Green and Conventional Assets with Portfolio Implications," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 609-637, August.
    2. Liao, Xin & Li, Qin & Chan, Stephen & Chu, Jeffrey & Zhang, Yuanyuan, 2024. "Interconnections and contagion among cryptocurrencies, DeFi, NFT and traditional financial assets: Some new evidence from tail risk driven network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 647(C).
    3. Shikta Sing & Supun Chandrasena & Yue Shi & Abdullah Alhussain & Claude DIEBOLT & Martin Enilov & Tapas Mishra, 2024. "A Learning Model with Memory in the Financial Markets," Working Papers 06-24, Association Française de Cliométrie (AFC).

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