Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/263973 
Year of Publication: 
2022
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2022-053/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
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.
Subjects: 
Density Combination
Large Set of Predictive Densities
Dynamic Factor Models
Nonlinear state-space
Bayesian Inference
JEL: 
C11
C15
C53
E37
Document Type: 
Working Paper

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