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

Author

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  • Yan Wang
  • Shoudong Chen
  • Xiu Zhang

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.

Suggested Citation

  • Yan Wang & Shoudong Chen & Xiu Zhang, 2014. "Measuring systemic financial risk and analyzing influential factors: an extreme value approach," China Finance Review International, Emerald Group Publishing Limited, vol. 4(4), pages 385-398, November.
    • Handle: RePEc:eme:cfripp:v:4:y:2014:i:4:p:385-398
    DOI: 10.1108/CFRI-07-2013-0095
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    Citations

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    Cited by:

    1. Huang, Wei-Qiang & Wang, Dan, 2018. "A return spillover network perspective analysis of Chinese financial institutions’ systemic importance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 405-421.
    2. Qin, Xiao & Zhou, Chunyang, 2019. "Financial structure and determinants of systemic risk contribution," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    3. Hsieh, Ming-Hua & Lee, Yi-Hsi & Shyu, So-De & Chiu, Yu-Fen, 2019. "Estimating multifactor portfolio credit risk: A variance reduction approach," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    4. ZHANG, Ping & WANG, Yiru & ZHAO, Min & YANG, Tzu-Yi, 2021. "Measuring Systemic Risk Of China'S Listed Banks," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 25(3), pages 6-28, September.
    5. Chaudhry, Sajid M. & Ahmed, Rizwan & Huynh, Toan Luu Duc & Benjasak, Chonlakan, 2022. "Tail risk and systemic risk of finance and technology (FinTech) firms," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    6. Douglas da Rosa München & Herbert Kimura, 2020. "Regulatory Banking Leverage: what do you know?," Working Papers Series 540, Central Bank of Brazil, Research Department.
    7. Li, Xindan & Yu, Honghai & Fang, Libing & Xiong, Cheng, 2019. "Do firm-level factors play forward-looking role for financial systemic risk: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    8. Huang, Wei-Qiang & Wang, Dan, 2018. "Systemic importance analysis of chinese financial institutions based on volatility spillover network," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 19-30.

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