D-vine copula based quantile regression

TL;DR

This paper introduces a flexible, fast, and accurate semiparametric quantile regression method based on D-vine copulas, effectively handling high-dimensional data and common issues like quantile crossing, with applications in finance.

Contribution

The paper presents a novel D-vine copula-based quantile regression approach that automatically manages variable selection, quantile crossing, and interactions, outperforming existing methods in accuracy and computational efficiency.

Findings

Improved accuracy over traditional quantile regression methods.

Reduced computational time in high-dimensional settings.

Effective application to financial risk modeling and stress testing.

Abstract

Quantile regression, that is the prediction of conditional quantiles, has steadily gained importance in statistical modeling and financial applications. The authors introduce a new semiparametric quantile regression method based on sequentially fitting a likelihood optimal D-vine copula to given data resulting in highly flexible models with easily extractable conditional quantiles. As a subclass of regular vine copulas, D-vines enable the modeling of multivariate copulas in terms of bivariate building blocks, a so-called pair-copula construction (PCC). The proposed algorithm works fast and accurate even in high dimensions and incorporates an automatic variable selection by maximizing the conditional log-likelihood. Further, typical issues of quantile regression such as quantile crossing or transformations, interactions and collinearity of variables are automatically taken care of. In a simulation study the improved accuracy and saved computational time of the approach in comparison with established quantile regression methods is highlighted. An extensive financial application to international credit default swap (CDS) data including stress testing and Value-at-Risk (VaR) prediction demonstrates the usefulness of the proposed method.