Naslov (eng)

Volatility cascades in cryptocurrency trading

Autor

Gradojevic, Nikola
Tsiakas, Ilias

Opis (eng)

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.

Jezik

engleski

Datum

2021

Licenca

Ovo delo je licencirano pod uslovima licence
Creative Commons CC BY 4.0 - Creative Commons Autorstvo 4.0 International License.

http://creativecommons.org/licenses/by/4.0/legalcode

Predmet

Keywords: Cryptocurrencies; Bitcoin; Ethereum; Ripple; Volatility Cascade; Wavelet Hidden Markov Tree model.

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