  In Gatheral et al. 2018, first posted in 2014, volatility is characterized by
fractional behavior with a Hurst exponent $H < 0.5$, challenging traditional
views of volatility dynamics. Gatheral et al. demonstrated this using realized
volatility measurements. Our study extends this analysis by employing
range-based proxies to confirm their findings across a broader dataset and
non-standard assets. Notably, we address the concern that rough volatility
might be an artifact of microstructure noise in high-frequency return data. Our
results reveal that log-volatility, estimated via range-based methods, behaves
akin to fractional Brownian motion with an even lower $H$, below $0.1$. We also
affirm the efficacy of the rough fractional stochastic volatility model (RFSV),
finding that its predictive capability surpasses that of AR, HAR, and GARCH
models in most scenarios. This work substantiates the intrinsic nature of rough
volatility, independent of the microstructure noise often present in
high-frequency financial data.
