# Bayesian prediction of jumps in large panels of time series data

## TL;DR

This paper introduces a Bayesian framework for modeling and predicting jumps in large panels of stock return data, improving forecast accuracy by jointly modeling volatility and jumps.

## Contribution

It develops a novel computational approach for univariate stochastic volatility with Poisson jumps and extends it to large panels with dynamic jump intensities.

## Key findings

- Joint modeling of jumps enhances predictive performance.
- The proposed method outperforms existing tools in out-of-sample forecasts.
- Dynamic factor models capture co-evolving jump intensities.

## Abstract

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.