Learn More
This paper provides methods for carrying out likelihood based inference for diffusion driven models, for example discretely observed multivariate diffusions, continuous time stochas-tic volatility models and counting process models. The diffusions can potentially be non-stationary. Although our methods are sampling based, making use of Markov chain Monte(More)
In this paper we provide a unified methodology in order to conduct likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility models, characterized by both a leverage effect and jumps in returns. Given the non-linear/non-Gaussian state-space form, approximating the likelihood for the parameters is(More)
In this paper we develop likelihood based inferential methods for a novel class of (potentially non-stationary) diffusion driven state space models. Examples of models in this class are continuous time stochastic volatility models and counting process models. Although our methods are sampling based, making use of Markov chain Monte Carlo methods to sample(More)
This paper provides methods for carrying out likelihood based inference on non-linear observed and partially observed non-linear diffusions. The diffusions can potentially be non-stationary. The methods are based on innovative Markov chain Monte Carlo methods combined with an augmentation strategy. We study the performance of the methods as the degree of(More)
In this paper, we p r o vide a method for modelling stationary time series. We allow t h e family of marginal densities for the observations to be speciied. Our approach is to construct the model with a speciied marginal family and build the dependence structure around it. We show that the resulting time series is linear with a simple autocorrelation(More)
In this paper, we provide a new method for modelling stationary time series, concentrating on volatility models. Knowledge about the marginalfamily for the observables is usually quite speciic, for example, exchange rate returns are thought to follow a Student-t. Our approach is to construct the model with a speciied marginal family and build the dependence(More)