Modelling extremal events: for insurance and finance
Modelling extremal events: for insurance and finance
Regression with response distributions of Pareto-type
Computational Statistics & Data Analysis
Functional nonparametric estimation of conditional extreme quantiles
Journal of Multivariate Analysis
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In financial practice, it is important to understand the dependence structure between the returns of individual assets and the market index. This is particularly true under extreme situations. Theoretically, this amounts to regressing the dependence relationship against a set of pre-specified predictive variables. To this end, we propose here a novel method called tail dependence regression. It assumes a tail dependence index model between individual assets and market index. Subsequently, such a tail dependence index is modeled as a linear combination of the predictors through a monotonic transformation. An approximate maximum likelihood method is then developed to estimate the unknown regression coefficients. The resulting estimator's asymptotic properties are investigated theoretically. Numerical studies including both simulated and real datasets are presented for illustration purposes.