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Returns, volatility and the cryptocurrency bubble of 2017–18

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  • Cross, Jamie L.
  • Hou, Chenghan
  • Trinh, Kelly

Abstract

Research on cryptocurrencies has focused on price and volatility formation in isolation, however knowledge about their interdependence is important for risk management and asset allocation. We investigate the existence and nature of such a relationship in four commonly traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple, during the cryptocurrency bubble of 2017–18. Using a generalized asset pricing model, we find evidence of a risk premium effect in Litecoin and Ripple during the boom of 2017, and that adverse news effects were an important driver of the cryptocurrency crash of 2018 in all four cryptocurrencies. In an out-of-sample forecasting exercise, we find that allowing for stochastic volatility and a heavy tailed distribution provides more accurate return and volatility forecasts compared to a random walk benchmark. This suggests that cryptocurrency markets were not weak-form efficient during this period.

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  • Cross, Jamie L. & Hou, Chenghan & Trinh, Kelly, 2021. "Returns, volatility and the cryptocurrency bubble of 2017–18," Economic Modelling, Elsevier, vol. 104(C).
    • Handle: RePEc:eee:ecmode:v:104:y:2021:i:c:s0264999321002327
    DOI: 10.1016/j.econmod.2021.105643
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    6. Ahmed, Mohamed Shaker & El-Masry, Ahmed A. & Al-Maghyereh, Aktham I. & Kumar, Satish, 2024. "Cryptocurrency volatility: A review, synthesis, and research agenda," Research in International Business and Finance, Elsevier, vol. 71(C).
    7. Yousaf, Imran & Goodell, John W., 2023. "Linkages between CBDC and cryptocurrency uncertainties, and digital payment stocks," Finance Research Letters, Elsevier, vol. 54(C).
    8. Pham, Linh & Huynh, Toan Luu Duc & Hanif, Waqas, 2023. "Time-varying asymmetric spillovers among cryptocurrency, green and fossil-fuel investments," Global Finance Journal, Elsevier, vol. 58(C).
    9. Huang, Chuangxia & Cai, Yaqian & Yang, Xiaoguang & Deng, Yanchen & Yang, Xin, 2023. "Laplacian-energy-like measure: Does it improve the Cross-Sectional Absolute Deviation herding model?," Economic Modelling, Elsevier, vol. 127(C).
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    11. Alessio Brini & Jimmie Lenz, 2024. "A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes," Papers 2404.04962, arXiv.org.
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    14. Jingyang Wu & Xinyi Zhang & Fangyixuan Huang & Haochen Zhou & Rohtiash Chandra, 2024. "Review of deep learning models for crypto price prediction: implementation and evaluation," Papers 2405.11431, arXiv.org, revised Jun 2024.
    15. Hall, Mauri K. & Jasiak, Joann, 2024. "Modelling common bubbles in cryptocurrency prices," Economic Modelling, Elsevier, vol. 139(C).
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    More about this item

    Keywords

    Cryptocurrencies; Returns and volatility; Stochastic volatility; Time-varying parameter model; Forecasting;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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