A GMM approach to estimate the roughness of stochastic volatility
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摘要
We propose a GMM estimator for log‑normal stochastic volatility driven by fractional Brownian motion, with an analytical bias correction to account for measurement error in realized variance; the estimator is consistent and asymptotically normal and, applied to many index datasets, yields very small Hurst exponents (around 0.05), confirming rough volatility empirically [page::0][page::2][page::25].
