ABSTRACT. In this paper, we study the detailed distributional properties of integrated non-Gaussian Ornstein–Uhlenbeck (intOU) processes. Both exact and approximate results are given. We emphasize the study of the tail behaviour of the intOU process. Our results have many potential applications in financial economics, as OU processes are used as models of instantaneous variance in stochastic volatility (SV) models. In this case, an intOU process can be regarded as a model of integrated variance. Hence, the tail behaviour of the intOU process will determine the tail behaviour of returns generated by SV models
none4noWe analyze the problem of the analytical characterization of the probability distribution of ...
In this thesis we consider a stochastic volatility model based on non-Gaussian Ornstein-Uhlenbeck pr...
We study Ornstein-Uhlenbeck stochastic processes driven by Lévy processes, and extend them to more g...
In this paper, we study the detailed distributional properties of integrated non-Gaussian Ornstein–U...
In this paper, we study the detailed distributional properties of integrated non-Gaussian Ornstein–U...
In this paper, we study the detailed distributional properties of integrated non-Gaussian Ornstein-U...
Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important ...
Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important ...
Non-Gaussian processes of Ornstein-Uhlenbeck type, or OU processes for short, offer the possibility ...
Continuous non-Gaussian stationary processes of the OU-type are becoming increasingly popular given ...
Continuous non-Gaussian stationary processes of the OU-type are becoming increasingly popular given ...
Continuous non-Gaussian stationary processes of the OU-type are becoming increasingly popular given ...
Continuous-time stochastic volatility models are becoming an increasingly popular way to describe mo...
Non-Gaussian Ornstein-Uhlenbeck processes allow to model several distributional features of assets’ ...
Continuous-time stochastic volatility models are becoming an increasingly popular way to describe mo...
none4noWe analyze the problem of the analytical characterization of the probability distribution of ...
In this thesis we consider a stochastic volatility model based on non-Gaussian Ornstein-Uhlenbeck pr...
We study Ornstein-Uhlenbeck stochastic processes driven by Lévy processes, and extend them to more g...
In this paper, we study the detailed distributional properties of integrated non-Gaussian Ornstein–U...
In this paper, we study the detailed distributional properties of integrated non-Gaussian Ornstein–U...
In this paper, we study the detailed distributional properties of integrated non-Gaussian Ornstein-U...
Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important ...
Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important ...
Non-Gaussian processes of Ornstein-Uhlenbeck type, or OU processes for short, offer the possibility ...
Continuous non-Gaussian stationary processes of the OU-type are becoming increasingly popular given ...
Continuous non-Gaussian stationary processes of the OU-type are becoming increasingly popular given ...
Continuous non-Gaussian stationary processes of the OU-type are becoming increasingly popular given ...
Continuous-time stochastic volatility models are becoming an increasingly popular way to describe mo...
Non-Gaussian Ornstein-Uhlenbeck processes allow to model several distributional features of assets’ ...
Continuous-time stochastic volatility models are becoming an increasingly popular way to describe mo...
none4noWe analyze the problem of the analytical characterization of the probability distribution of ...
In this thesis we consider a stochastic volatility model based on non-Gaussian Ornstein-Uhlenbeck pr...
We study Ornstein-Uhlenbeck stochastic processes driven by Lévy processes, and extend them to more g...