Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics
Dorota Toczydlowska () and
Gareth W. Peters ()
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Dorota Toczydlowska: Department of Statistical Science, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
Gareth W. Peters: Department of Statistical Science, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
Econometrics, 2018, vol. 6, issue 3, 1-45
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.
Keywords: feature extraction; yield curve modelling; panel regression; heavy tail distribution; multivariate state-space models; robust dimensionality reduction; Expectations-Maximisation algorithm; macroeconomic and financial datasets (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:6:y:2018:i:3:p:34-:d:158660
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