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ISBADP2014_44_segers_Nonparametricestimationofextremaldependence.pdf
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Abstract
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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Kiriliouk, A., Segers, J., & Warchol, M. (2014). Nonparametric estimation of extremal dependence (ISBA Discussion Paper 2014/44). https://hdl.handle.net/2078.5/268223