2025 Volume 54 Issue 2 Pages 129-144
This study employs a stochastic volatility model to analyze hourly data during 2024 for seven major cryptocurrencies. The model incorporates explanatory variables derived from trading volume and returns, and Bernstein polynomials are used to flexibly capture intraday and intramonth seasonalities. Estimation results show that the regression coefficients for these explanatory variables are not statistically significant. This suggests that the typical relationships among volatility, returns, and trading volume observed in traditional financial markets may not hold at the hourly frequency in cryptocurrency markets. Although the volatility process displays strong persistence, no significant negative association is found between returns and volatility, indicating the absence of a leverage effect. Moreover, there is no clear evidence of seasonalities or fat tails in the volatility distribution. These findings imply that cryptocurrency markets are heavily influenced by algorithmic trading, and that price dynamics may not fully reflect the risk-averse behavior often attributed to human investors.
