Trading Volume, Returns, and Volatility of Bitcoin:
An Asymmetric Conditional Heteroskedasticity Model (2017–2024)
DOI:
https://doi.org/10.24275/Keywords:
Bitcoin, Volatility, Trading volume, GJR-GARCHAbstract
This study analyzes the relationship between trading volume, volatility, and Bitcoin returns, demonstrating that volatility is often overestimated when trading volume is not considered. The asymmetric GJR-GARCH model is employed to capture the dynamics of conditional volatility and assess its reaction to positive and negative shocks. The analysis covers the period from 2017 to 2024 and four subsamples identified using the Bai-Perron test to detect structural breaks. The results indicate that the high volatility observed in previous studies is largely due to low trading volume. Additionally, a positive relationship was found between volume and returns, as well as between volume and volatility across all subsamples. Finally, a traditional leverage effect was identified during crisis periods, while an inverse leverage effect was observed in expansion phases, highlighting the complex dynamics of the Bitcoin market.
JEL Classification: G12, G15, C58, D53.
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