Regression type models for extremal dependence

We propose a vector generalized additive modelling framework for taking into account the eect of covariates on angular density functions in a multivariate extreme value context. The proposed methods are tailored for settings where
the dependence between extreme values may change according to covariates. We devise a maximum penalized loglikelihood estimator, discuss details of the estimation procedure, and derive its consistency and asymptotic normality. The simulation study suggests that the proposed methods perform well in a wealth of simulation scenarios by accurately recovering the true covariate-adjusted angular density. Our empirical analysis reveals relevant dynamics of the dependence between extreme air temperatures in two alpine resorts during the winter season.