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Ethereum synchronicity, upside volatility and Bitcoin crash risk

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  • Ma, Yu
  • Luan, Zhiqian

Abstract

The fundamental value of Bitcoin is difficult to evaluate because there is no underlying company or cashflow. Extant literature documented various macro-economic factors and technical factors to price Bitcoin. However, for a highly speculative instrument, investors’ behavior plays an important role in asset pricing. This paper incorporates Bitcoin Ethereum synchronicity conditional on upside volatility of Bitcoin as a proxy for the fear of high Bitcoin prices. Empirical results reveal that when upside volatility is high, Ethereum synchronicity exerts a significant positive influence on Bitcoin crash risk.

Suggested Citation

  • Ma, Yu & Luan, Zhiqian, 2022. "Ethereum synchronicity, upside volatility and Bitcoin crash risk," Finance Research Letters, Elsevier, vol. 46(PA).
    • Handle: RePEc:eee:finlet:v:46:y:2022:i:pa:s1544612321003573
    DOI: 10.1016/j.frl.2021.102352
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    Cited by:

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    4. 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.
    5. Waqas Hanif & Hee-Un Ko & Linh Pham & Sang Hoon Kang, 2023. "Dynamic connectedness and network in the high moments of cryptocurrency, stock, and commodity markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-40, December.
    6. Zhang, Lei & Bouri, Elie & Chen, Yan, 2023. "Co-jump dynamicity in the cryptocurrency market: A network modelling perspective," Finance Research Letters, Elsevier, vol. 58(PB).
    7. Wang, Jying-Nan & Liu, Hung-Chun & Hsu, Yuan-Teng, 2024. "A U-shaped relationship between the crypto fear-greed index and the price synchronicity of cryptocurrencies," Finance Research Letters, Elsevier, vol. 59(C).
    8. Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022. "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, vol. 114(C).

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