Extreme risk spillover network: application to financial institutions

Authors:
Top Cited Papers
Publisher website
Google Scholar
Abstract
Using the CAViaR tool to estimate the value-at-risk (VaR) and the Granger causality risk test to quantify extreme risk spillovers, we propose an extreme risk spillover network for analysing the interconnectedness across financial institutions. We construct extreme risk spillover networks at 1% and 5% risk levels (which we denote 1% and 5% VaR networks) based on the daily returns of 84 publicly listed financial institutions from four sectors—banks, diversified financials, insurance and real estate—during the period 2006–2015. We find that extreme risk spillover networks have a time-lag effect. Both the static and dynamic networks show that on average the real estate and bank sectors are net senders of extreme risk spillovers and the insurance and diversified financials sectors are net recipients, which coheres with the evidence from the recent global financial crisis. The networks during the 2008–2009 financial crisis and the European sovereign debt crisis exhibited distinctive topological features that differed from those in tranquil periods. Our approach supplies new information on the interconnectedness across financial agents that will prove valuable not only to investors and hedge fund managers, but also to regulators and policy-makers.
Funding Information
  • China Scholarship Council (201506135022)
  • Specialized Research Fund for the Doctoral Program of Higher Education (20130161110031)
  • National Natural Science Foundation of China (71201054)
  • National Science Foundation (PHY-1505000, CMMI-1125290, CHE-1213217)
  • National Natural Science Foundation of China (71501066)
  • National Natural Science Foundation of China (71521061)
  • National Natural Science Foundation of China (71373072)
  • U.S. Department of Energy (DE-AC07-05Id14517)

This publication has 44 references indexed in Scilit: