Nonparametric estimation of extremal dependence

Anna Kiriliouk, Johan Segers, Michał Warchoł

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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 estimating dependence at high levels, the stable tail dependence function and the spectral measure are particularly convenient. These objects also lie at the basis of nonparametric techniques for modelling the dependence among extremes in the max domain of attraction setting. In case of asymptotic independence, this setting is in adequate, and more refined tail dependence coefficients exist, serving, among others, to discriminate between asymptotic dependence and independence. Throughout, the methods are illustrated on financial data.

langue originaleAnglais
titreExtreme Value Modeling and Risk Analysis
Sous-titreMethods and Applications
EditeurCRC Press
Nombre de pages23
ISBN (Electronique)9781498701310
ISBN (imprimé)9781498701297
Etat de la publicationPublié - 6 janv. 2016
Modification externeOui

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Kiriliouk, A., Segers, J., & Warchoł, M. (2016). Nonparametric estimation of extremal dependence. Dans Extreme Value Modeling and Risk Analysis: Methods and Applications (p. 353-375). CRC Press.