In this paper we are concerned with Bayesian inference in the presence of untractable
likelihood functions. By untractable we mean that the likelihood function of our model of …

After a quick review of superpositions of OU (supOU) processes, integrated supOU
processes and the supOU stochastic volatility model we estimate these processes by using …

This paper describes a Bayesian nonparametric approach to volatility estimation. Volatility is
assumed to follow a superposition of an infinite number of Ornstein–Uhlenbeck processes …

A modification of one of the most popular stochastic model in describing financial indexes
dynamics is introduced. For describing a nonlinear behavior of volatility, a threshold noise …

We study properties of the (generalized) Dickman distribution with two parameters and the
stationary solution of the Ornstein-Uhlenbeck stochastic differential equation driven by a …

A discrete stochastic volatility model is considered; the model is driven by two stable-
Paretian processes, one for the observations and the other for the scale parameter. Due to …

Barndorff-Nielsen and Shephard (2001) proposed a class of stochastic volatility models in
which the volatility follows the Ornstein–Uhlenbeck process driven by a positive Levy …

This paper reviews a class of multifractal models obtained via products of exponential
Ornstein–Uhlenbeck processes driven by Lévy motion. Given a self-decomposable …

A curse of dimensionality arises when using the Continuum-GMM procedure to estimate
large dimensional models. Two solutions are proposed, both of which convert the high …

We consider estimation of stochastic volatility models which are driven by a heavy-tailed
innovation distribution. Exploiting the simple structure of the characteristic function of …