Mathias Trabs, Universität Hamburg
Parameter estimation for stochastic PDEs based on discreet obeservations in time and space
Motivated by random phenomena in natural science as well as by mathematical finance, stochastic partial differential equations (SPDEs) have been intensively studied during the last fifty years with a main focus on theoretical analytic and probabilistic aspects. Thanks to the exploding number of available data and the fast progress in information technology, SPDE models become nowadays increasingly popular for practitioners, for instance, to model neuronal systems or interest rate fluctuations to give only two examples. Consequently, statistical methods are required to calibrate this class of complex models. We study the parameter estimation for parabolic, linear, second order SPDEs observing a mild solution on a discrete grid in time and space. Focusing first on volatility estimation and assuming a high-frequency regime in time, we provide an explicit and easy to implement method of moments estimator based on squared time increments of the process. If the observation frequency in time is finer than in space, the estimator is consistent and admits a central limit theorem. This is established moreover for the estimation of the integrated volatility in a semi-parametric framework. In a second step, we consider not only time increments of the solution field but also space increments as well as space-time increments. This allows for the construction of estimators which are robust with respect to the sampling regime, i.e., they are also applicable if the observation grid in space is finer than in time. Finally, we discuss the estimation of the parameters in the differential operator which determines the SPDE. This talk is based on joint works with Markus Bibinger and Florian Hildebrandt.