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Measuring systemic financial risk and analyzing influential factors: an extreme value approach

Measuring systemic financial risk and analyzing influential factors: an extreme value approach Purpose – The purpose of this paper is to measure a single financial institution's contribution to systemic risk by using extremal quantile regression and analyzing the influential factors of systemic risk. Design/methodology/approach – Extreme value theory is applied when measuring the systemic risk of financial institutions. Extremal quantile regression, where extreme value distribution is assumed for the tail, is used to measure the extreme risk and analyze the changes in and dependencies of risk. Furthermore, influential factors of systemic risk are analyzed using panel regression. Findings – The key findings of the paper are that value at risk and contribution to systemic risk are very different when measuring the risk of a financial institution; banks’ contributions to systemic risk are much higher; and size and leverage ratio are two significant and important factors influencing an institution's systemic risk. Practical implications – Characterizing variables of financial institutions such as size, leverage ratio and market beta should be considered together when regulating and constraining financial institutions. Originality/value – To take extreme risk into account, this paper measures systemic financial risk using extremal quantile regression for the first time. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png China Finance Review International Emerald Publishing

Measuring systemic financial risk and analyzing influential factors: an extreme value approach

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2044-1398
DOI
10.1108/CFRI-07-2013-0095
Publisher site
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Abstract

Purpose – The purpose of this paper is to measure a single financial institution's contribution to systemic risk by using extremal quantile regression and analyzing the influential factors of systemic risk. Design/methodology/approach – Extreme value theory is applied when measuring the systemic risk of financial institutions. Extremal quantile regression, where extreme value distribution is assumed for the tail, is used to measure the extreme risk and analyze the changes in and dependencies of risk. Furthermore, influential factors of systemic risk are analyzed using panel regression. Findings – The key findings of the paper are that value at risk and contribution to systemic risk are very different when measuring the risk of a financial institution; banks’ contributions to systemic risk are much higher; and size and leverage ratio are two significant and important factors influencing an institution's systemic risk. Practical implications – Characterizing variables of financial institutions such as size, leverage ratio and market beta should be considered together when regulating and constraining financial institutions. Originality/value – To take extreme risk into account, this paper measures systemic financial risk using extremal quantile regression for the first time.

Journal

China Finance Review International – Emerald Publishing

Published: Nov 11, 2014

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