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Dynamic semi-parametric factor model for functional expectiles

Authors
  • Burdejová, Petra1
  • Härdle, Wolfgang K.1, 2
  • 1 Humboldt-Universität zu Berlin, Berlin, Germany , Berlin (Germany)
  • 2 Singapore Management University, Sim Kee Boon Institute for Financial Economics, Singapore, Singapore , Singapore (Singapore)
Type
Published Article
Journal
Computational Statistics
Publisher
Springer Berlin Heidelberg
Publication Date
Apr 03, 2019
Volume
34
Issue
2
Pages
489–502
Identifiers
DOI: 10.1007/s00180-019-00883-1
Source
Springer Nature
Keywords
License
Yellow

Abstract

High-frequency data can provide us with a quantity of information for forecasting and help to calculate and prevent the future risk based on extremes. This tail behaviour is very often driven by exogenous components and may be modelled conditionally on other variables. However, many of these phenomena are observed over time, exhibiting non-trivial dynamics and dependencies. We propose a functional dynamic factor model to study the dynamics of expectile curves. The complexity of the model and the number of dependent variables are reduced by lasso penalization. The functional factors serve as a low-dimensional representation of the conditional tail event, while the time-variation is captured by factor loadings. We illustrate the model with an application to climatology, where daily data over years on temperature, rainfalls or strength of wind are available.

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