A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tail Data

TLDR: This work draws from Extreme Value Theory the tools to build a hybrid unimodal density having a parameter controlling the heaviness of the upper tail, a Gaussian whose upper tail has been replaced by a generalized Pareto tail.

Abstract: We propose an estimator for the conditional density p(Y |X) that can adapt for asymmetric heavy tails which might depend on X. Such estimators have important applications in nance and insurance. We draw from Extreme Value Theory the tools to build a hybrid unimodal density having a parameter controlling the heaviness of the upper tail. This hybrid is a Gaussian whose upper tail has been replaced by a generalized Pareto tail. We use this hybrid in a multi-modal mixture in order to obtain a nonparametric density estimator that can easily adapt for heavy tailed data. To obtain a conditional density estimator, the parameters of the mixture estimator can be seen as functions of X and these functions learned. We show experimentally that this approach better models the conditional density in terms of likelihood than compared competing algorithms : conditional mixture models with other types of components and multivariate nonparametric models.