B0811
Title: Tail index regression forest
Authors: Luca Trapin - University of Bologna (Italy) [presenting]
Marco Bee - University of Trento (Italy)
Emanuele Taufer - University of Trento (Italy)
Abstract: Regression analysis of the tail index has received increasing attention over the last few years. The availability of large and complex datasets has stimulated the development of tail index regression techniques that could capture complex relationships between a large number of predictors and a dependent variable of interest. However, available methods are either flexible and asymptotically justified but do not scale well with the dimension of the predictor space or well-suited in high-dimension but lack asymptotic results. A novel regression forest approach is presented that fills this gap. The asymptotic normality of the tail index regression forest estimator is established under mild assumptions on the tail behaviour of the dependent variable. An extensive simulation study and an application to the conditional distribution of ROE for a large cross-section of U.S. companies confirm that the approach outperforms existing parametric and non-parametric tail index regression methods.
