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

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Tinbergen Institute Discussion Paper ; No. TI 2022-053/III

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Bayesian Analysis: General ; Statistical Simulation Methods: General ; Forecasting Models; Simulation Methods ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
• Thema: Density Combination ; Large Set of Predictive Densities ; Dynamic Factor Models ; Nonlinear state-space ; Bayesian Inference

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Casarin, Roberto ; Grassi, Stefano ; Ravazzolo, Francesco ; van Dijk, Herman K.
• Ereignis: Veröffentlichung
• (wer): Tinbergen Institute
• (wo): Amsterdam and Rotterdam
• (wann): 2022

Weitere Informationen

• Handle: http://hdl.handle.net/10419/263973
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ