Interconnected risk contributions: A heavy-tail approach to analyze U.S. financial sectors

Artikel

This paper investigates the dynamic evolution of tail risk interdependence among U.S. banks, financial services and insurance sectors. Life and non-life insurers have been considered separately to account for their different characteristics. The tail risk interdependence measurement framework relies on the multivariate Student-t Markov switching (MS) model and the multiple-conditional value-at-risk (CoVaR) (conditional expected shortfall (CoES)) risk measures introduced in Bernardi et al. (2013), accounting for both the stylized facts of financial data and the contemporaneous multiple joint distress events. The Shapley value methodology is then applied to compose the puzzle of individual risk attributions, providing a synthetic measure of tail interdependence. Our empirical investigation finds that banks appear to contribute more to the tail risk evolution of all of the remaining sectors, followed by the financial services and the insurance sectors, showing that the insurance sector significantly contributes as well to the overall risk. We also find that the role of each sector in contributing to other sectors' distress evolves over time according to the current predominant financial condition, implying different interdependence strength.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 8 ; Year: 2015 ; Issue: 2 ; Pages: 198-226 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: Markov switching ; tail risk interdependence ; risk measures

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Bernardi, Mauro ; Petrella, Lea
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2015

Further information

• DOI: doi:10.3390/jrfm8020198
• Handle: http://hdl.handle.net/10419/178560
• Last update: 10.03.2025, 11:44 AM CET