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June 2022 Bayesian Prediction of Jumps in Large Panels of Time Series Data
Angelos Alexopoulos, Petros Dellaportas, Omiros Papaspiliopoulos
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Bayesian Anal. 17(2): 651-683 (June 2022). DOI: 10.1214/21-BA1268


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.

Funding Statement

The authors gratefully acknowledge the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: ARISTEIA-LIKEJUMPS-436, and the Alan Turing Institute for EPSRC grant EP/N510129/1.


The authors are grateful for the comments and feedback from the anonymous associate editor and referees, which significantly helped to improve this work.


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Angelos Alexopoulos. Petros Dellaportas. Omiros Papaspiliopoulos. "Bayesian Prediction of Jumps in Large Panels of Time Series Data." Bayesian Anal. 17 (2) 651 - 683, June 2022.


Published: June 2022
First available in Project Euclid: 23 April 2021

Digital Object Identifier: 10.1214/21-BA1268

Keywords: dynamic factor model , forecasting stock returns , Markov chain Monte Carlo , sequential Monte Carlo , stochastic volatility with jumps


Vol.17 • No. 2 • June 2022
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