Inhomogeneous dependency modelling with time varying copulae

Arbeitspapier

Measuring dependence in a multivariate time series is tantamount to modelling its dynamic structure in space and time. In the context of a multivariate normally distributed time series, the evolution of the covariance (or correlation) matrix over time describes this dynamic. A wide variety of applications, though, requires a modelling framework different from the multivariate normal. In risk management the non-normal behaviour of most financial time series calls for nonlinear (i.e. non-gaussian) dependency. The correct modelling of non-gaussian dependencies is therefore a key issue in the analysis of multivariate time series. In this paper we use copulae functions with adaptively estimated time varying parameters for modelling the distribution of returns, free from the usual normality assumptions. Further, we apply copulae to estimation of Value-at-Risk (VaR) of a portfolio and show its better performance over the RiskMetrics approach, a widely used methodology for VaR estimation.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2006-075

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General
• Subject: Value-at-Risk ; time varying copula ; adaptive estimation ; nonparametric estimation

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Giacomini, Enzo ; Härdle, Wolfgang Karl ; Ignatieva, Ekaterina ; Spokoiny, Vladimir
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2006

Further information

• Handle: http://hdl.handle.net/10419/25158
• Last update: 10.03.2025, 11:43 AM CET