Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian prediction of jumps in large panels of time series data   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.

27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg