Dynamic semi-parametric factor model for functional expectiles

Arbeitspapier

High-frequency data can provide us with a quantity of informa- tion for forecasting, help to calculate and prevent the future risk based on extremes. This tail behaviour is very often driven by ex- ogenous components and may be modelled conditional on other vari- ables. However, many of these phenomena are observed over time, exhibiting non-trivial dynamics and dependencies. We propose a func- tional 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2017-027

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models ; Large Data Sets: Modeling and Analysis ; Optimization Techniques; Programming Models; Dynamic Analysis ; Climate; Natural Disasters and Their Management; Global Warming
• Subject: factor model ; functional data ; expectiles ; extremes

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Burdejová, Petra ; Härdle, Wolfgang Karl
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/191791
• Last update: 10.03.2025, 11:43 AM CET