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Systemic Risks and the Macroeconomy

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  • Marcella Lucchetta
  • Gianni De Nicoló

Abstract

This paper presents a modeling framework that delivers joint forecasts of indicators of systemic real risk and systemic financial risk, as well as stress-tests of these indicators as impulse responses to structural shocks identified by standard macroeconomic and banking theory. This framework is implemented using large sets of quarterly time series of indicators of financial and real activity for the G-7 economies for the 1980Q1-2009Q3 period. We obtain two main results. First, there is evidence of out-of sample forecasting power for tail risk realizations of real activity for several countries, suggesting the usefulness of the model as a risk monitoring tool. Second, in all countries aggregate demand shocks are the main drivers of the real cycle, and bank credit demand shocks are the main drivers of the bank lending cycle. These results challenge the common wisdom that constraints in the aggregate supply of credit have been a key driver of the sharp downturn in real activity experienced by the G-7 economies in 2008Q4- 2009Q1.

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Bibliographic Info

Paper provided by International Monetary Fund in its series IMF Working Papers with number 10/29.

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Length: 41
Date of creation: 01 Feb 2010
Date of revision:
Handle: RePEc:imf:imfwpa:10/29

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Keywords: Banking sector; Economic forecasting; Economic indicators; Economic models; External shocks; Financial risk; Group of seven; Prices; bank credit; bank lending; correlation; time series; banking models; statistics; descriptive statistics; equation; probability; equations; banking theory; covariance; explanatory power; standard deviations; basic descriptive statistics; bank stock; bank liabilities; banking crises; independent variables; bank loan; bank for international settlements; prediction; factor analysis; banking systems;

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  1. 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.
  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. Ben Bernanke & Mark Gertler & Simon Gilchrist, 1998. "The Financial Accelerator in a Quantitative Business Cycle Framework," NBER Working Papers 6455, National Bureau of Economic Research, Inc.
  4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  5. Andrew T. Foerster & Pierre-Daniel G. Sarte & Mark W. Watson, 2008. "Sectoral vs. aggregate shocks : a structural factor analysis of industrial production," Working Paper 08-07, Federal Reserve Bank of Richmond.
  6. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  7. Elena Loukoianova & Gianni De Nicoló & John H. Boyd, 2009. "Banking Crises and Crisis Dating," IMF Working Papers 09/141, International Monetary Fund.
  8. Xin Huang & Hao Zhou & Haibin Zhu, 2009. "A Framework for Assessing the Systemic Risk of Major Financial Institutions," BIS Working Papers 281, Bank for International Settlements.
  9. Ethan Cohen-Cole & Burcu Duygan-Bump & José Fillat & Judit Montoriol-Garriga, 2008. "Looking behind the aggregates: a reply to “Facts and Myths about the Financial Crisis of 2008”," Risk and Policy Analysis Unit Working Paper QAU08-5, Federal Reserve Bank of Boston.
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