UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Bayesian prediction of jumps in large panels of time series data

Dellaportas, P; Alexopoulos, A; Papaspiliopoulos, O; (2022) Bayesian prediction of jumps in large panels of time series data. Bayesian Analysis 10.1214/21-BA1268. (In press). Green open access

[thumbnail of Dellaportas_21-BA1268.pdf]
Preview
Text
Dellaportas_21-BA1268.pdf - Published version

Download (978kB) | Preview

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.

Type: Article
Title: Bayesian prediction of jumps in large panels of time series data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1214/21-BA1268
Publisher version: http://dx.doi.org/10.1214/21-BA1268
Language: English
Additional information: This is an open access article under the CC BY 4.0 license Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)
Keywords: dynamic factor model, forecasting stock returns, Markov chain Monte Carlo, sequential Monte Carlo, stochastic volatility with jumps
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10125556
Downloads since deposit
14Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item