Interconnected Risk Contributions: A Heavy-Tail Approach to Analyze U.S. Financial SectorsReport as inadecuate




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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





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Academic Editors: Sheri Markose and Lars Stentoft

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





Author: Mauro Bernardi 1 and Lea Petrella 2,*

Source: http://mdpi.com/



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