COPAR - Multivariate time series modeling using the COPula AutoRegressive model

TL;DR

This paper introduces COPAR, a copula-based multivariate time series model that captures non-linear and asymmetric dependencies, outperforming traditional linear models in finance and economics applications.

Contribution

The paper presents a novel vine copula-based autoregressive model for multivariate time series, enabling flexible dependence modeling beyond linear correlations.

Findings

Effective modeling of non-linear dependencies

Application to macroeconomic, energy, and finance data

Improved accuracy over traditional linear models

Abstract

Analysis of multivariate time series is a common problem in areas like finance and economics. The classical tool for this purpose are vector autoregressive models. These however are limited to the modeling of linear and symmetric dependence. We propose a novel copula-based model which allows for non-linear and asymmetric modeling of serial as well as between-series dependencies. The model exploits the flexibility of vine copulas which are built up by bivariate copulas only. We describe statistical inference techniques for the new model and demonstrate its usefulness in three relevant applications: We analyze time series of macroeconomic indicators, of electricity load demands and of bond portfolio returns.