Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/195463
Authors: 
Toczydlowska, Dorota
Peters, Gareth W.
Year of Publication: 
2018
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 6 [Year:] 2018 [Issue:] 3 [Pages:] 1-45
Abstract: 
A novel class of dimension reduction methods is combined with a stochastic multi-factor panel regression-based state-space model in order to model the dynamics of yield curves whilst incorporating regression factors. This is achieved via Probabilistic Principal Component Analysis (PPCA) in which new statistically-robust variants are derived also treating missing data. We embed the rank reduced feature extractions into a stochastic representation for state-space models for yield curve dynamics and compare the results to classical multi-factor dynamic Nelson-Siegel state-space models. This leads to important new representations of yield curve models that can be practically important for addressing questions of financial stress testing and monetary policy interventions, which can incorporate efficiently financial big data. We illustrate our results on various financial and macroeconomic datasets from the Euro Zone and international market.
Subjects: 
feature extraction
yield curve modelling
panel regression
heavy tail distribution
multivariate state-space models
robust dimensionality reduction
Expectations-Maximisation algorithm
macroeconomic and financial datasets
JEL: 
C32
C38
C51
C53
C55
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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