Generalized Additive Models for Pair-Copula Constructions

TL;DR

This paper extends pair-copula constructions with generalized additive models to incorporate covariate effects, enabling flexible modeling of dependence structures in financial data.

Contribution

It introduces a novel method for integrating covariates into pair-copula models using generalized additive models, with a sequential estimation approach.

Findings

Time-varying dependence between exchange rates captured

Method validated through simulation studies

Applied to real foreign exchange data

Abstract

Pair-copula constructions are flexible dependence models that use bivariate copulas as building blocks. In this paper, we use generalized additive models to extend them by allowing covariates effects. Borrowing ideas from a traditionally univariate context, we let each pair-copula parameter depend directly on the covariates in a parametric, semiparametric or nonparametric way. We propose a sequential estimation method that we study by simulation, and apply it to investigate the time-varying dependence structure between the intraday returns on four major foreign exchange rates. An R package, a script reproducing the results in this article, and additional simulation results are provided as supplementary material.