Interconnected risk contributions: an heavy-tail approach to analyse US financial sectors

TL;DR

This paper models the dynamic tail risk interdependence among US financial sectors using a multivariate heavy-tail approach, revealing sector-specific risk contributions and their evolution over time.

Contribution

It introduces a multivariate Student-t Markov Switching model combined with Multiple-CoVaR measures to analyze sector interdependence and tail risk contributions dynamically.

Findings

Banks are the main risk source for other sectors.

Insurance sector significantly contributes to overall risk.

Risk contributions evolve with financial conditions over time.

Abstract

In this paper we consider a multivariate model-based approach to measure the dynamic evolution of tail risk interdependence among US banks, financial services and insurance sectors. To deeply investigate the risk contribution of insurers we consider separately life and non-life companies. To achieve this goal we apply the multivariate student-t Markov Switching model and the Multiple-CoVaR (CoES) risk measures introduced in Bernardi et. al. (2013b) to account for both the known stylised characteristics of the data and the contemporaneous joint distress events affecting financial sectors. Our empirical investigation finds that banks appear to be the major source of risk for all the remaining sectors, followed by the financial services and the insurance sectors, showing that insurance sector significantly contributes as well to the overall risk. Moreover, we find that the role of each sector in contributing to other sectors distress evolves over time accordingly to the current predominant financial condition, implying different interconnection strength.