Multivariate tail copula: modeling and estimation

Arbeitspapier

In general, risk of an extreme outcome in financial markets can be expressed as a function of the tail copula of a high-dimensional vector after standardizing marginals. Hence it is of importance to model and estimate tail copulas. Even for moderate dimension, nonparametrically estimating a tail copula is very inefficient and fitting a parametric model to tail copulas is not robust. In this paper we propose a semi-parametric model for tail copulas via an elliptical copula. Based on this model assumption, we propose a novel estimator for the tail copula, which proves favourable compared to the empirical tail copula, both theoretically and empirically

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Discussion Paper ; No. 468

Classification and Topics

• Subject: Asymptotic normality ; Dependence modeling ; Elliptical copula ; Elliptical distribution ; Multivariate modeling ; Regular variation ; Tail copula

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Klüppelberg, Claudia ; Kuhn, Gabriel ; Peng, Liang
• Event: Veröffentlichung
• (who): Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
• (where): München
• (when): 2006

Further information

• DOI: doi:10.5282/ubm/epub.1836
• Handle: http://hdl.handle.net/10419/31079
• URN: urn:nbn:de:bvb:19-epub-1836-8