Volatility cascades in cryptocurrency trading

Nikola Gradojevic and Ilias Tsiakas

Journal of Empirical Finance, 2021, vol. 62, issue C, 252-265

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.

Keywords: Cryptocurrencies; Bitcoin; Ethereum; Ripple; Volatility cascade; Wavelet Hidden Markov Tree model (search for similar items in EconPapers)
JEL-codes: C58 F31 G15 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:62:y:2021:i:c:p:252-265

DOI: 10.1016/j.jempfin.2021.04.005

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