Title: Deep quantile regression
Authors: Ilias Chronopoulos - University of Essex (United Kingdom) [presenting]
Aristeidis Raftapostolos - University of Strathclyde (United Kingdom)
George Kapetanios - Kings College, University of London (United Kingdom)
Abstract: A deep quantile estimator is proposed using neural networks and their universal approximation property to examine a non-linear association between the conditional quantiles of a dependent variable and predictors. The proposed methodology is versatile and allows both the use of different penalty functions and high dimensional covariates. We present a Monte Carlo exercise where we examine the finite sample properties of the proposed estimator and show that our approach delivers good finite sample performance. We use the deep quantile estimator to forecast Value at Risk and find significant gains over linear quantile regression alternatives, supported by various testing schemes. We also contribute to the interpretability of neural networks output by making comparisons between the commonly used SHAP values and an alternative method based on partial derivatives.