# Statistical Inference for non-Markovian Stochastic Volatility Models

## Statistical Inference for non-Markovian Stochastic Volatility Models

Long memory stochastic volatility (LMSV) models have been used to explain the persistence of volatility in the market, while rough stochastic volatility (RSV) models have been shown to reproduce statistical properties of low frequency financial data. In these two classes of models, the volatility process is often described by a fractional Ornstein-Uhlenbeck process with Hurst index H, where H>1/2 for LMSV models and H<1/2 for RSV models. The goal of this talk is to propose a general methodology for the estimation of the parameters of the above models, the filtering of the volatility process, and the calibration of the Hurst index, H, which will then be applied to the option pricing on the S&P 500 index.

Alexandra Chronopoulou is an Assistant Professor at the University of Illinois at Urbana-Champaign in the department of Industrial & Enterprise Systems Engineering (ISE). Before joining ISE, she has spent a year at the City University of New York, at City College in the department of Mathematics, and two years at the University of California, Santa Barbara. She did a post-doc at INRIA Nancy Research Center, in the BIGS (Biology, Genetics and Statistics) team. She obtained her Ph.D. from the department of Statistics at Purdue University.

Her primary research interests include (i) financial engineering (option pricing, high-frequency finance, stochastic volatility models, stochastic volatility models with long memory, Monte Carlo and particle filtering methods and financial time series), (ii) stochastic modeling (simulation methods, modeling of stochastic systems with long memory and selfsimilar processes), and (iii) statistics (estimation of the Hurst parameter, statistical inference for fractional stochastic differential equations, statistical inference for multiscale diffusions, change-point detection, statistical inference for discrete-time choice models).