Bayesian prediction of jumps in large panels of time series data

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.

49 pages, 27 figures, 4 tables

References in corpus (8)

• A General Framework for the Parametrization of Hierarchical Models
• Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models
• Testing for common arrivals of jumps for discretely observed multidimensional processes
• Slice sampling covariance hyperparameters of latent Gaussian models
• Sparse Bayesian time-varying covariance estimation in many dimensions
• Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models
• Sparse Bayesian vector autoregressions in huge dimensions
• Scalable inference for a full multivariate stochastic volatility model