Detecting regime switches in the dependence structure of high dimensional financial data

TL;DR

This paper introduces Markov switching regular vine copulas to detect regime changes in high-dimensional financial data, capturing asymmetric and tail dependencies, with methods validated through simulations and real data analysis.

Contribution

It develops fast inference algorithms for regime-switching vine copulas, enabling better modeling of dependence in financial crises.

Findings

Regime switches are present in financial dependence structures.

Models can accurately describe inhomogeneity during crises.

Methods are validated with simulations and real data.

Abstract

Misperceptions about extreme dependencies between different financial assets have been an im- portant element of the recent financial crisis. This paper studies inhomogeneity in dependence structures using Markov switching regular vine copulas. These account for asymmetric depen- dencies and tail dependencies in high dimensional data. We develop methods for fast maximum likelihood as well as Bayesian inference. Our algorithms are validated in simulations and applied to financial data. We find that regime switches are present in the dependence structure of various data sets and show that regime switching models could provide tools for the accurate description of inhomogeneity during times of crisis.