  We take a new look at the problem of disentangling the volatility and jumps
processes of daily stock returns. We first provide a computational framework
for the univariate stochastic volatility model with Poisson-driven jumps that
offers a competitive inference alternative to the existing tools. This
methodology is then extended to a large set of stocks for which we assume that
their unobserved jump intensities co-evolve in time through a dynamic factor
model. To evaluate the proposed modelling approach we conduct out-of-sample
forecasts and we compare the posterior predictive distributions obtained from
the different models. We provide evidence that joint modelling of jumps
improves the predictive ability of the stochastic volatility models.
