Cookies?
Library Header Image
LSE Research Online LSE Library Services

Financial big data solutions for state space panel regression in interest rate dynamics

Toczydlowska, Dorota and Peters, Gareth W. (2018) Financial big data solutions for state space panel regression in interest rate dynamics. Econometrics, 6 (3).

Full text not available from this repository.

Identification Number: 10.3390/econometrics6030034

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.

Item Type: Article
Additional Information: © 2018 The Authors
Divisions: Systemic Risk Centre
Subjects: H Social Sciences > HG Finance
JEL classification: C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C32 - Time-Series Models
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Other Model Applications
Date Deposited: 26 Apr 2019 09:18
Last Modified: 26 Apr 2019 09:18
URI: http://eprints.lse.ac.uk/id/eprint/100498

Actions (login required)

View Item View Item