We propose a dynamic factor model for the analysis of multivariate time series count data. Our model allows for idiosyncratic as well as common serially correlated latent factors in order to account for potentially complex dynamic interdependence between series of counts. The model is estimated...

Multivariate random sums appear in many scientific fields, most notably in actuarial science, where they model both the number of claims and their sizes. Unfortunately, they pose severe inferential problems. For example, their density function is analytically intractable, in the general case,...

We utilise several asset pricing models that allow for discontinuities in the returns and volatility time series in order toobtain estimates of Value-at-Risk (VaR). The first class of model that we use mixes a continuous diffusion processwith discrete jumps at random points in time (Poisson Jump...

We develop a new class of distributions, namely, the odd power generalized Weibull-G powerseries (OPGW-GPS) class of distributions. We present some special classes of the proposeddistribution. Structural properties, have also been derived. We conducted a simulation studyto evaluate the...

In this paper, a new Poisson area-biased Ailamujia distribution has been formulated to analyse count data. It was created by combining two distributions: the Poisson and area-biased Ailamujia distributions, using the compounding technique. Several distributional properties of the formulated...