IDEAS home Printed from https://ideas.repec.org/p/bcl/bclwop/bclwp102.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.bcl.lu/en/publications/Working-papers/102/BCLWP102.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Bernd Schwaab, 2012. "Conditional probabilities and contagion measures for euro area sovereign default risk," Research Bulletin, European Central Bank, vol. 17, pages 6-11.
    5. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    6. Gabriele Galati & Richhild Moessner, 2013. "Macroprudential Policy – A Literature Review," Journal of Economic Surveys, Wiley Blackwell, vol. 27(5), pages 846-878, December.
    7. 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.
    8. 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.
    9. Monica Billio & Mila Getmansky & Andrew W. Lo & Loriana Pelizzon, 2010. "Econometric Measures of Systemic Risk in the Finance and Insurance Sectors," NBER Chapters, in: Market Institutions and Financial Market Risk, National Bureau of Economic Research, Inc.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Mathias Drehmann & Nikola Tarashev, 2011. "Systemic importance: some simple indicators," BIS Quarterly Review, Bank for International Settlements, March.
    18. Bouye, Eric & Durlleman, Valdo & Nikeghbali, Ashkan & Riboulet, Gaël & Roncalli, Thierry, 2000. "Copulas for finance," MPRA Paper 37359, University Library of Munich, Germany.
    19. Miguel Segoviano, 2006. "Consistent Information Multivariate Density Optimizing Methodology," FMG Discussion Papers dp557, Financial Markets Group.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. Philip Lowe & Claudio Borio, 2002. "Asset prices, financial and monetary stability: exploring the nexus," BIS Working Papers 114, Bank for International Settlements.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Farhi, Emmanuel & Tirole, Jean, 2017. "Shadow Banking and the Four Pillars of Traditional Financial Intermediation," CEPR Discussion Papers 12373, C.E.P.R. Discussion Papers.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xisong Jin & Francisco Nadal De Simone, 2017. "Systemic Financial Sector and Sovereign Risks," BCL working papers 109, Central Bank of Luxembourg.
    2. 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.
    3. 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.
    4. 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).
    5. Xisong Jin & Francisco Nadal De Simone, 2015. "Investment funds? vulnerabilities: A tail-risk dynamic CIMDO approach," BCL working papers 95, Central Bank of Luxembourg.
    6. 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.
    7. 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.
    8. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.
    9. Marc Hallin & Roman Liska, 2008. "Dynamic Factors in the Presence of Block Structure," Economics Working Papers ECO2008/22, European University Institute.
    10. Aramonte, Sirio & Giudice Rodriguez, Marius del & Wu, Jason, 2013. "Dynamic factor Value-at-Risk for large heteroskedastic portfolios," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4299-4309.
    11. 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.
    12. Huang, Huichou & MacDonald, Ronald & Zhao, Yang, 2012. "Global Currency Misalignments, Crash Sensitivity, and Downside Insurance Costs," MPRA Paper 53745, University Library of Munich, Germany, revised 18 Nov 2013.
    13. Matteo Luciani, 2015. "Monetary Policy and the Housing Market: A Structural Factor Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 199-218, March.
    14. Luciani, Matteo, 2014. "Forecasting with approximate dynamic factor models: The role of non-pervasive shocks," International Journal of Forecasting, Elsevier, vol. 30(1), pages 20-29.
    15. Hallin, Marc & Liska, Roman, 2011. "Dynamic factors in the presence of blocks," Journal of Econometrics, Elsevier, vol. 163(1), pages 29-41, July.
    16. Claudio Borio, 2011. "Rediscovering the Macroeconomic Roots of Financial Stability Policy: Journey, Challenges, and a Way Forward," Annual Review of Financial Economics, Annual Reviews, vol. 3(1), pages 87-117, December.
    17. 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.
    18. Mario Forni & Luca Gambetti & Luca Sala, 2014. "No News in Business Cycles," Economic Journal, Royal Economic Society, vol. 124(581), pages 1168-1191, December.
    19. Ruiz, Esther & Poncela, Pilar, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bcl:bclwop:bclwp102. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://www.bcl.lu/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (email available below). General contact details of provider: https://www.bcl.lu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.