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J. Risk Financial Manag. 2015, 8(2), 198-226; doi:10.3390/jrfm8020198

Interconnected Risk Contributions: A Heavy-Tail Approach to Analyze U.S. Financial Sectors

1
Department of Statistical Sciences, University of Padua, Via C. Battisti, 241/243, 35121 Padua, Italy
2
MEMOTEF Department, Sapienza University of Rome, Via del Castro Laurenziano, 9,00161 Rome, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Sheri Markose and Lars Stentoft
Received: 8 July 2014 / Revised: 27 February 2015 / Accepted: 17 March 2015 / Published: 7 April 2015
(This article belongs to the Special Issue Financial Risk Modeling and Forecasting)
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Abstract

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. View Full-Text
Keywords: Markov switching; tail risk interdependence; risk measures Markov switching; tail risk interdependence; risk measures
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bernardi, M.; Petrella, L. Interconnected Risk Contributions: A Heavy-Tail Approach to Analyze U.S. Financial Sectors. J. Risk Financial Manag. 2015, 8, 198-226.

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