Fractional Stochastic Volatility Models: Statistical Inference & Hedging (Alexandra Chronopoulou, UIUC)

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. In this talk, we focus on the long-range dependent case and propose a methodology for the estimation of the leverage effect (that is the correlation between the stock’s volatility and the stock returns), based on the discrete quadratic covariation of the processes. We also study the sensitivity of the option price with respect to the strike and determine when the option is underhedged, overhedged or perfectly hedged.

When 4:00 pm to 5:00 pm on Thursday, October 26, 2017
Location 111 Cummington Mall, Room 148