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Bayesian prediction of jumps in large panels of time series data

Angelos Alexopoulos, Petros Dellaportas and Omiros Papaspiliopoulos

Papers from arXiv.org

Abstract: We take a new look at the problem of disentangling the volatility and jumps processes in a panel of stock daily returns. We first provide an efficient computational framework that deals with the stochastic volatility model with Poisson-driven jumps in a univariate scenario that offers a competitive inference alternative to the existing implementation tools. This methodology is then extended to a large set of stocks in which it is assumed that the unobserved jump intensities of each stock co-evolve in time through a dynamic factor model. A carefully designed sequential Monte Carlo algorithm provides out-of-sample empirical evidence that our suggested model outperforms, with respect to predictive Bayes factors, models that do not exploit the panel structure of stocks.

New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2019-03
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