DMS Statistics and Data Science Seminar

Speaker: Carsten Chong (Columbia University)

Title: Statistical inference for rough volatility: Central limit theorems

Abstract: In recent years, there has been substantive empirical evidence that stochastic volatility is rough. In other words, the local behavior of stochastic volatility is much more irregular than semimartingales and resembles that of a fractional Brownian motion with Hurst parameter H<0.5. In this paper, we derive a consistent and asymptotically mixed normal estimator of H based on high-frequency price observations. In contrast to previous works, we work in a semiparametric setting and do not assume any a priori relationship between volatility estimators and true volatility. Furthermore, our estimator attains a rate of convergence that is known to be optimal in a minimax sense in parametric rough volatility models.

Short Bio: Dr. Chong is currently an assistant professor at Columbia University and will join HKUST this summer. Before this, Dr. Chong did a Ph.D. at the Technical University of Munich. His research interests are primarily focused on statistical inference problems for stochastic processes, with an emphasis on high-frequency techniques and applications to financial econometrics. He is also interested in the area of stochastic partial differential equations, in particular, in stochastic PDEs driven by Levy noises, which, in contrast to Gaussian noises, typically have discontinuous and/or heavy-tailed components.