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Volatility cascades in cryptocurrency trading

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  • Gradojevic, Nikola
  • Tsiakas, Ilias

Abstract

This paper studies volatility cascades across multiple trading horizons in cryptocurrency markets. Using one-minute data on Bitcoin, Ethereum and Ripple against the US dollar, we implement the wavelet Hidden Markov Tree model. This model allows us to estimate the transition probability of high or low volatility at one time scale (horizon) propagating to high or low volatility at the next time scale. We find that when moving from long to short horizons, volatility cascades tend to be symmetric: low volatility at long horizons is likely to be followed by low volatility at short horizons, and high volatility is likely to be followed by high volatility. In contrast, when moving from short to long horizons, volatility cascades are strongly asymmetric: high volatility at short horizons is now likely to be followed by low volatility at long horizons. These results are robust across time periods and cryptocurrencies.

Suggested Citation

  • Gradojevic, Nikola & Tsiakas, Ilias, 2021. "Volatility cascades in cryptocurrency trading," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 252-265.
  • Handle: RePEc:eee:empfin:v:62:y:2021:i:c:p:252-265
    DOI: 10.1016/j.jempfin.2021.04.005
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    Cited by:

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    4. 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.

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    More about this item

    Keywords

    Cryptocurrencies; Bitcoin; Ethereum; Ripple; Volatility cascade; Wavelet Hidden Markov Tree model;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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