A simple asymptotically optimal filter over an infinite horizon

A filtering problem over an infinite horizon for a continuous time signal and discrete time observation in the presence of non-Gaussian white noise is considered. Conditions are presented, under which a nonlinear Kalman type filter with limiter is asymptotically optimal in the mean square sense for long time intervals given provided the sampling frequency is sufficiently high.

Ornstein–Uhlenbeck type processes with non-normal distribution

We analyse a class of diffusion models that (i) allows an explicit expression for the likelihood function of discrete time observation, (ii) allows the possibility of heavy-tailed observations, and (iii) allows an analysis of the tails of the increments. The class simply consists of transformed Ornstein–Uhlenbeck processes and is of relevance for heavy-tailed time series. We also treat the question of the existence of an equivalent martingale measure for the class of models considered.

Ornstein–Uhlenbeck type processes with non-normal distribution

We analyse a class of diffusion models that (i) allows an explicit expression for the likelihood function of discrete time observation, (ii) allows the possibility of heavy-tailed observations, and (iii) allows an analysis of the tails of the increments. The class simply consists of transformed Ornstein–Uhlenbeck processes and is of relevance for heavy-tailed time series. We also treat the question of the existence of an equivalent martingale measure for the class of models considered.