This paper introduces a new non-parametric approach to the modeling of circular data, based on the use of Bernstein polynomial densities which generalizes the standard Bernstein polynomial model to account for the specific characteristics of circular data. It is shown that the trigonometric...

This paper introduces a new approach to Bayesian nonparametric inference for densities on the hypercube, based on the use of a multivariate Bernstein polynomial prior. Posterior convergence rates under the proposed prior are obtained. Furthermore, a novel sampling scheme, based on the use of...

We present a non-parametric approach for the estimation of the bivariate distribution of two circular variables and the modelling of the joint distribution of a circular and a linear variable. We combine nonparametric estimates of the marginal densities of the circular and linear components with...

We use an asymmetric dynamic conditional correlation (ADCC) GJR-GARCH model to estimate the time-varying volatilities of financial returns. The ADCC-GJR-GARCH model takes into consideration the asymmetries in individual assets volatilities, as well as in the correlations. The errors are modeled...

Financial time series analysis deals with the understanding of data collected on financial markets. Several parametric distribution models have been entertained for describing, estimating and predicting the dynamics of financial time series. Alternatively, this article considers a Bayesian...