A Flexible Predictive Density Combination for Large Financial Data Sets in Regular and Crisis Periods

Roberto Casarin, Stefano Grassi, Francesco Ravazzolo and Herman van Dijk
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Stefano Grassi: University of Rome Tor Vergata
Francesco Ravazzolo: BI Norwegian Business School

No 22-053/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination weights to relatively small subsets. Given the representation of the probability model in extended nonlinear state-space form, efficient simulation-based Bayesian inference is proposed using parallel dynamic clustering as well as nonlinear filtering, implemented on graphics processing units. The approach is applied to combine predictive densities based on a large number of individual US stock returns of daily observations over a period that includes the Covid-19 crisis period. Evidence on dynamic cluster composition, weight patterns and model set incompleteness gives valuable signals for improved modelling. This enables higher predictive accuracy and better assessment of uncertainty and risk for investment fund management.

Keywords: Density Combination; Large Set of Predictive Densities; Dynamic Factor Models; Nonlinear state-space; Bayesian Inference (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 E37 (search for similar items in EconPapers)
Date: 2022-08-09
New Economics Papers: this item is included in nep-ecm
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Journal Article: A flexible predictive density combination for large financial data sets in regular and crisis periods (2023)
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