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Systemic Real and Financial Risks; Measurement, Forecasting, and Stress Testing

Author

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  • Marcella Lucchetta
  • Gianni De Nicolo

Abstract

This paper formulates a novel modeling framework that delivers: (a) forecasts of indicators of systemic real risk and systemic financial risk based on density forecasts of indicators of real activity and financial health; (b) stress-tests as measures of the dynamics of responses of systemic risk indicators to structural shocks identified by standard macroeconomic and banking theory. Using a large number of quarterly time series of the G-7 economies in 1980Q1-2010Q2, we show that the model exhibits significant out-of sample forecasting power for tail real and financial risk realizations, and that stress testing provides useful early warnings on the build-up of real and financial vulnerabilities.

Suggested Citation

  • Marcella Lucchetta & Gianni De Nicolo, 2012. "Systemic Real and Financial Risks; Measurement, Forecasting, and Stress Testing," IMF Working Papers 12/58, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:12/58
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    References listed on IDEAS

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    1. Andrew T. Foerster & Pierre-Daniel G. Sarte & Mark W. Watson, 2011. "Sectoral versus Aggregate Shocks: A Structural Factor Analysis of Industrial Production," Journal of Political Economy, University of Chicago Press, vol. 119(1), pages 1-38.
    2. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    3. Harald Uhlig & Pooyan Amir Ahmadi, 2012. "Measuring The Dynamic Effects Of Monetary Policy Shocks: A Bayesian Favar Approach With Sign Restriction," 2012 Meeting Papers 1060, Society for Economic Dynamics.
    4. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    5. Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 208-216, April.
    6. Joseph G. Haubrich & Andrew W. Lo, 2013. "Quantifying Systemic Risk," NBER Books, National Bureau of Economic Research, Inc, number haub10-1.
    7. repec:spo:wpecon:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    8. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Eugenio Cerutti & Stijn Claessens & Patrick McGuire, 2012. "Systemic Risks in Global Banking: What Available Data Can Tell Us and What More Data Are Needed?," NBER Chapters,in: Risk Topography: Systemic Risk and Macro Modeling, pages 235-260 National Bureau of Economic Research, Inc.
    2. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    3. repec:wsi:gcrxxx:v:03:y:2013:i:01:n:s2010493613500013 is not listed on IDEAS
    4. Dieter Gramlich & Mikhail V. Oet & Stephen J. Ong, 2013. "Policy in adaptive financial markets—the use of systemic risk early warning tools," Working Paper 1309, Federal Reserve Bank of Cleveland.
    5. Robert Engle & Eric Jondeau & Michael Rockinger, 2015. "Systemic Risk in Europe," Review of Finance, European Finance Association, vol. 19(1), pages 145-190.
    6. Buncic, Daniel & Melecky, Martin, 2013. "Macroprudential stress testing of credit risk: A practical approach for policy makers," Journal of Financial Stability, Elsevier, vol. 9(3), pages 347-370.
    7. Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Financial conditions and density forecasts for US output and inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 24, pages 66-78, March.
    8. 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.
    9. Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial indicators and density forecasts for US output and inflation," Temi di discussione (Economic working papers) 977, Bank of Italy, Economic Research and International Relations Area.
    10. Covas, Francisco B. & Rump, Ben & Zakrajšek, Egon, 2014. "Stress-testing US bank holding companies: A dynamic panel quantile regression approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 691-713.
    11. repec:kap:annfin:v:14:y:2018:i:1:d:10.1007_s10436-017-0310-3 is not listed on IDEAS
    12. Aida Caldera Sánchez & Oliver Röhn, 2016. "How do policies influence GDP tail risks?," OECD Economics Department Working Papers 1339, OECD Publishing.
    13. Xisong Jin & Francisco Nadal De Simone, 2015. "Investment funds? vulnerabilities: A tail-risk dynamic CIMDO approach," BCL working papers 95, Central Bank of Luxembourg.
    14. 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.

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