We develop a discrete-time stochastic volatility option pricing model, which exploits the informationcontained in high-frequency data. The Realized Volatility (RV) is used as a proxy of the unobservablelog-returns volatility. We model its dynamics by a simple but effective long-memory process:...

We develop infinitesimally robust statistical procedures for general diffusion pro-cesses. We first prove existence and uniqueness of the times series influence functionof conditionally unbiased M{estimators for ergodic and stationary diffusions, underweak conditions on the (martingale)...

We develop infinitesimally robust statistical procedures for general diffusion processes. We first prove existence and uniqueness of the times series influence function of conditionally unbiased M-estimators for ergodic and stationary diffusions, under weak conditions on the (martingale)...

We define rank-based estimators (R-estimators) for semiparametric time series models in whichthe conditional location and scale depend on a Euclidean parameter, while the innovation density isan infinite-dimensional nuisance. Applications include linear and nonlinear models, featuring...

We develop a discrete-time stochastic volatility option pricing model exploiting the information contained in the Realized Volatility (RV), which is used as a proxy of the unobservable log-return volatility. We model the RV dynamics by a simple and effective long-memory process, whose parameters...