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Tracking Changes in the Intensity of Financial Sector's Systemic Risk

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  • Xisong Jin
  • Francisco Nadal De Simone

Abstract

This study provides the first available estimates of systemic risk in the financial sector comprising the banking and investment fund industries during 2009Q4­2015Q4. Systemic risk is measured in three forms: as risk common to the financial sector; as contagion within the financial sector and; as the build­up of financial sector's vulnerabilities over time, which may unravel in a disorderly manner. The methodology models the financial sector components' default dependence statistically and captures the time­varying non-linearities and feedback effects typical of financial markets. In addition, the study estimates the common components of the financial sector's default measures and by identifying the macro-financial variables most closely associated with them, it provides useful input into the formulation of macro­prudential policy. The main results suggest that: (1) interdependence in the financial sector decreased in the first three years of the sample, but rose again later coinciding with ECB's references to increased search for yield in the financial sector. (2) Investment funds are a more important source of contagion to banks than the other way round, and this is more the case for European banking groups than for Luxembourg banks. (3) For tracking the growth of vulnerabilities over time, it is better to monitor the most vulnerable part of the financial sector because the common components of systemic risk measures tend to lead these measures.

Suggested Citation

  • 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.
    • Handle: RePEc:bcl:bclwop:bclwp102
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    References listed on IDEAS

    as
    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    4. Sylvain Benoit & Jean-Edouard Colliard & Christophe Hurlin & Christophe Pérignon, 2017. "Where the Risks Lie: A Survey on Systemic Risk," Review of Finance, European Finance Association, vol. 21(1), pages 109-152.
    5. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2009. "A Robust Criterion for Determining the Number of Factors in Approximate Factor Models," Working Papers ECARES 2009_023, ULB -- Universite Libre de Bruxelles.
    6. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    7. Huang, Xin & Zhou, Hao & Zhu, Haibin, 2012. "Assessing the systemic risk of a heterogeneous portfolio of banks during the recent financial crisis," Journal of Financial Stability, Elsevier, vol. 8(3), pages 193-205.
    8. Gabriele Galati & Richhild Moessner, 2013. "Macroprudential Policy – A Literature Review," Journal of Economic Surveys, Wiley Blackwell, vol. 27(5), pages 846-878, December.
    9. Marcos Souto & Benjamin M. Tabak & Francisco Vazquez, 2009. "Linking Financial and Macroeconomic Factors to Credit Risk Indicators of Brazilian Banks," Working Papers Series 189, Central Bank of Brazil, Research Department.
    10. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    11. Baur, Dirk G., 2013. "The structure and degree of dependence: A quantile regression approach," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 786-798.
    12. International Monetary Fund, 2006. "Indonesia: Selected Issues," IMF Staff Country Reports 2006/318, International Monetary Fund.
    13. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    14. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    15. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    16. Segoviano, Miguel A., 2006. "Consistent information multivariate density optimizing methodology," LSE Research Online Documents on Economics 24511, London School of Economics and Political Science, LSE Library.
    17. Monica Billio & Mila Getmansky & Andrew W. Lo & Loriana Pelizzon, 2010. "Econometric Measures of Systemic Risk in the Finance and Insurance Sectors," NBER Working Papers 16223, National Bureau of Economic Research, Inc.
    18. Xisong Jin & Francisco Nadal De Simone, 2015. "Investment funds? vulnerabilities: A tail-risk dynamic CIMDO approach," BCL working papers 95, Central Bank of Luxembourg.
    19. Philip Lowe & Claudio Borio, 2002. "Asset prices, financial and monetary stability: exploring the nexus," BIS Working Papers 114, Bank for International Settlements.
    20. Bernd Schwaab, 2012. "Conditional probabilities and contagion measures for euro area sovereign default risk," Research Bulletin, European Central Bank, vol. 17, pages 6-11.
    21. Xisong Jin & Francisco Nadal De Simone, 2012. "An Early-warning and Dynamic Forecasting Framework of Default Probabilities for the Macroprudential Policy Indicators Arsenal," BCL working papers 75, Central Bank of Luxembourg.
    22. Jin, Xisong & Nadal De Simone, Francisco de A., 2014. "Banking systemic vulnerabilities: A tail-risk dynamic CIMDO approach," Journal of Financial Stability, Elsevier, vol. 14(C), pages 81-101.
    23. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    24. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
    25. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    26. Nicole M. Boyson & Christof W. Stahel & René M. Stulz, 2010. "Hedge Fund Contagion and Liquidity Shocks," Journal of Finance, American Finance Association, vol. 65(5), pages 1789-1816, October.
    27. Mathias Drehmann & Nikola Tarashev, 2011. "Systemic importance: some simple indicators," BIS Quarterly Review, Bank for International Settlements, March.
    28. Tibor F. Liska, 2007. "The Liska model," Society and Economy, Akadémiai Kiadó, Hungary, vol. 29(3), pages 363-381, December.
    29. Bouye, Eric & Durlleman, Valdo & Nikeghbali, Ashkan & Riboulet, Gaël & Roncalli, Thierry, 2000. "Copulas for finance," MPRA Paper 37359, University Library of Munich, Germany.
    30. Miguel Segoviano, 2006. "Consistent Information Multivariate Density Optimizing Methodology," FMG Discussion Papers dp557, Financial Markets Group.
    31. Xisong Jin & Francisco Nadal de Simone, 2011. "Market- and Book-Based Models of Probability of Default for Developing Macroprudential Policy Tools," BCL working papers 65, Central Bank of Luxembourg.
    32. Jeffrey A. Frankel & George Saravelos, 2010. "Are Leading Indicators of Financial Crises Useful for Assessing Country Vulnerability? Evidence from the 2008-09 Global Crisis," NBER Working Papers 16047, National Bureau of Economic Research, Inc.
    33. Siem Jan Koopman & Andre Lucas & Bernd Schwaab, 2010. "Macro, Industry and Frailty Effects in Defaults: The 2008 Credit Crisis in Perspective," Tinbergen Institute Discussion Papers 10-004/2, Tinbergen Institute, revised 24 Aug 2010.
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    Cited by:

    1. 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).
    2. Emmanuel Farhi & Jean Tirole, 2021. "Shadow Banking and the Four Pillars of Traditional Financial Intermediation [Securitization without Risk Transfer]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(6), pages 2622-2653.
    3. International Monetary Fund, 2017. "Luxembourg: Financial Sector Assessment Program: Technical Note-Risk Analysis," IMF Staff Country Reports 2017/261, International Monetary Fund.

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    More about this item

    Keywords

    financial stability? macro-prudential policy? banking sector; investment funds; default probability? non-linearities? generalized dynamic factor model? dynamic copulas;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • F3 - International Economics - - International Finance
    • G1 - Financial Economics - - General Financial Markets

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