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An extreme value analysis of the tail relationships between returns and volumes for high frequency cryptocurrencies

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  • Chan, Stephen
  • Chu, Jeffrey
  • Zhang, Yuanyuan
  • Nadarajah, Saralees

Abstract

This paper investigates both the extreme dependence and correlation between high frequency cryptocurrency (Bitcoin and Ethereum, versus the Euro and US Dollar) returns and transaction volumes, at the extreme tails associated with booms and busts in the cryptocurrency markets. We apply an extreme value theory (EVT) approach, and highlight how these results assist traders and practitioners who rely on such technical indicators in their trading strategies – especially in times of extreme market turbulence or irrational market exuberance. Our findings contradict the belief in Wall Street that volume can significantly influence price levels and from an economic perspective our model reveals weak positive correlation between return and volume at the tails, which suggests that a misinterpretation among market participants can cause cryptocurrency markets to be relatively illiquid, thus leading to extreme price movements. Relating our statistical findings to economic models, we find that our empirical results are consistent with the explanation of market crashes based on trade misinterpretation.

Suggested Citation

  • Chan, Stephen & Chu, Jeffrey & Zhang, Yuanyuan & Nadarajah, Saralees, 2022. "An extreme value analysis of the tail relationships between returns and volumes for high frequency cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 59(C).
  • Handle: RePEc:eee:riibaf:v:59:y:2022:i:c:s0275531921001628
    DOI: 10.1016/j.ribaf.2021.101541
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    2. Katsiampa, Paraskevi & Yarovaya, Larisa & Zięba, Damian, 2022. "High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    3. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    4. Federico D'Amario & Milos Ciganovic, 2022. "Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach," Papers 2210.00883, arXiv.org.
    5. Ko, Hyungjin & Son, Bumho & Lee, Jaewook, 2024. "Portfolio insurance strategy in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 67(PA).
    6. Zhaoshi Geng & Xiaofeng Ji & Rui Cao & Mengyuan Lu & Wenwen Qin, 2022. "A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways," Sustainability, MDPI, vol. 14(18), pages 1-24, September.
    7. González-Sánchez, Mariano & Nave Pineda, Juan M., 2023. "Where is the distribution tail threshold? A tale on tail and copulas in financial risk measurement," International Review of Financial Analysis, Elsevier, vol. 86(C).
    8. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).

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