Nonparametric Estimation of Extremal Dependence

There is an increasing interest to understand the dependence structure of a random vector not only in the center of its distribution but also in the tails. Extreme-value theory tackles the problem of modelling the joint tail of a multivariate distribution by modelling the marginal distributions and the dependence structure separately. For esti- mating dependence at high levels, the stable tail dependence function and the spectral measure are particularly convenient. These objects also lie at the basis of nonpara- metric techniques for modelling the dependence among extremes in the max-domain of attraction setting. In case of asymptotic independence, this setting is inadequate, and more refined tail dependence coefficients exist, serving, among others, to discrim- inate between asymptotic dependence and independence. Throughout, the methods are illustrated on financial data.

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Document type	Contribution à ouvrage collectif (Book Chapter) – Chapitre
Publication date	2016
Language	Anglais
Host document	Dipak K. Dey, Jun Yan ; "Extreme Value Modeling and Risk Analysis: Methods and Applications"- p. 353-369 (ISBN : 9781498701297)
Collection	CRC Press
Peer reviewed	yes
Publisher	Taylor & Francis Group
Publication status	Publié
Affiliation	UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles
Links	http://hdl.handle.net/2078.1/180222[Handle]

See also
	[boreal:154832]	Nonparametric estimation of extremal dependence