We take a new look at the problem of disentangling the volatility and jumps processes of daily stock returns. We first provide a computational frame-work 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 com-pare 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.

Bayesian prediction of jumps in large panels of time series data

Papaspiliopoulos, Omiros
2022

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 frame-work 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 com-pare 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.
2022
2022
Alexopoulos, Angelos; Dellaportas, Petros; Papaspiliopoulos, Omiros
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4061787
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