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Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach

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  • Xisong Jin
  • Francisco Nadal De Simone

Abstract

This study proposes a novel framework which combines marginal probabilities of default estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology of Segoviano, and the generalized dynamic factor model (GDFM) supplemented by a dynamic t-copula. The framework models banks? default dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures banking systemic credit risk in three forms: (1) credit risk common to all banks; (2) credit risk in the banking system conditional on distress on a specific bank or combinations of banks and; (3) the buildup of banking system vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the banking sector short-term and conditional forward default measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. Finally, the framework produces robust outof-sample forecasts of the banking systemic credit risk measures. This paper advances the agenda of making macroprudential policy operational.

Suggested Citation

  • Xisong Jin & Francisco Nadal De Simone, 2013. "Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach," BCL working papers 82, Central Bank of Luxembourg.
  • Handle: RePEc:bcl:bclwop:bclwp082
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    2. Jin, Xisong & Nadal De Simone, Francisco, 2020. "Monetary policy and systemic risk-taking in the Euro area investment fund industry: A structural factor-augmented vector autoregression analysis," Journal of Financial Stability, Elsevier, vol. 49(C).
    3. Guerra, Solange Maria & Silva, Thiago Christiano & Tabak, Benjamin Miranda & de Souza Penaloza, Rodrigo Andrés & de Castro Miranda, Rodrigo César, 2016. "Systemic risk measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 329-342.
    4. Yves Mersch, 2013. "Default of Systemically Important Financial Intermediaries: Short-Term Stability vs. Incentive Compatability," Chapters, in: Andreas Dombret & Otto Lucius (ed.), Stability of the Financial System, chapter 11, Edward Elgar Publishing.
    5. Jin, Xisong & Nadal De Simone, Francisco, 2014. "A framework for tracking changes in the intensity of investment funds' systemic risk," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 343-368.
    6. Allen, Linda & Tang, Yi, 2016. "What’s the contingency? A proposal for bank contingent capital triggered by systemic risk," Journal of Financial Stability, Elsevier, vol. 26(C), pages 1-14.
    7. Collins, Sean & Gallagher, Emily, 2016. "Assessing the credit risk of money market funds during the eurozone crisis," Journal of Financial Stability, Elsevier, vol. 25(C), pages 150-165.
    8. Nadal De Simone, Francisco, 2021. "Measuring the deadly embrace: Systemic and sovereign risks," Research in International Business and Finance, Elsevier, vol. 56(C).
    9. Kabundi, Alain & De Simone, Francisco Nadal, 2020. "Monetary policy and systemic risk-taking in the euro area banking sector," Economic Modelling, Elsevier, vol. 91(C), pages 736-758.
    10. Bochmann, Paul & Hiebert, Paul & Schüler, Yves S. & Segoviano, Miguel, 2022. "Latent fragility: conditioning banks’ joint probability of default on the financial cycle," Working Paper Series 2698, European Central Bank.
    11. Silva, Walmir & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2017. "An analysis of the literature on systemic financial risk: A survey," Journal of Financial Stability, Elsevier, vol. 28(C), pages 91-114.
    12. Borio, Claudio & Drehmann, Mathias & Tsatsaronis, Kostas, 2014. "Stress-testing macro stress testing: Does it live up to expectations?," Journal of Financial Stability, Elsevier, vol. 12(C), pages 3-15.
    13. Xisong Jin & Francisco Nadal De Simone, 2017. "Systemic Financial Sector and Sovereign Risks," BCL working papers 109, Central Bank of Luxembourg.
    14. Arismendi-Zambrano, Juan & Belitsky, Vladimir & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2022. "The implications of dependence, tail dependence, and bounds’ measures for counterparty credit risk pricing," Journal of Financial Stability, Elsevier, vol. 58(C).
    15. Badea Irina - Raluca, 2015. "Hrm - Well-Being At Work Relation. A Case Study," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 4, pages 146-154, August.
    16. Nguyen, Linh Hoang & Lambe, Brendan John, 2021. "International tail risk connectedness: Network and determinants," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    17. Reboredo, Juan C. & Ugolini, Andrea, 2015. "A vine-copula conditional value-at-risk approach to systemic sovereign debt risk for the financial sector," The North American Journal of Economics and Finance, Elsevier, vol. 32(C), pages 98-123.
    18. Xisong Jin & Francisco Nadal De Simone, 2016. "Tracking Changes in the Intensity of Financial Sector's Systemic Risk," BCL working papers 102, Central Bank of Luxembourg.
    19. Necmi Kemal Avkiran & Lin Mi, 2017. "The Rising Systemic Importance of Chinese Banks: Should the World Be Concerned?," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 50(4), pages 427-440, December.
    20. Xisong Jin & Francisco Nadal De Simone, 2015. "Investment funds? vulnerabilities: A tail-risk dynamic CIMDO approach," BCL working papers 95, Central Bank of Luxembourg.
    21. Gastón Andrés Giordana & Ingmar Schumacher, 2017. "An Empirical Study on the Impact of Basel III Standards on Banks’ Default Risk: The Case of Luxembourg," JRFM, MDPI, vol. 10(2), pages 1-21, April.
    22. Xisong Jin, 2018. "How much does book value data tell us about systemic risk and its interactions with the macroeconomy? A Luxembourg empirical evaluation," BCL working papers 118, Central Bank of Luxembourg.

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    More about this item

    Keywords

    financial stability; procyclicality; macroprudential policy; credit risk; early warning indicators; default probability; non-linearities; generalized dynamic factor model; dynamic copulas; GARCH;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G1 - Financial Economics - - General Financial Markets

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