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A new index of financial conditions

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  • Gary Koop

    ()
    (Department of Economics, University of Strathclyde)

  • Dimitris Korobilis

    ()
    (Department of Economics, University of Glasgow)

Abstract

We use factor augmented vector autoregressive models with time-varying coefficients to construct a financial conditions index. The time-variation in the parameters allows for the weights attached to each .financial variable in the index to evolve over time. Furthermore, we develop methods for dynamic model averaging or selection which allow the financial variables entering into the FCI to change over time. We discuss why such extensions of the existing literature are important and show them to be so in an empirical application involving a wide range of .financial variables.

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File URL: http://www.strath.ac.uk/media/departments/economics/researchdiscussionpapers/2013/13-07FINAL.pdf
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Bibliographic Info

Paper provided by University of Strathclyde Business School, Department of Economics in its series Working Papers with number 1307.

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Length: 24 pages
Date of creation: Jun 2013
Date of revision:
Publication status: Published
Handle: RePEc:str:wpaper:1307

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Keywords: financial stress; dynamic model averaging; forecasting;

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References

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  1. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  2. Gary Koop & Dimitris Korobilis, 2011. "Forecasting Inflation Using Dynamic Model Averaging," Working Papers 1119, University of Strathclyde Business School, Department of Economics.
  3. Matheson, Troy D., 2012. "Financial conditions indexes for the United States and euro area," Economics Letters, Elsevier, vol. 115(3), pages 441-446.
  4. Fabio C. Bagliano & Claudio Morana, 2010. "The Great Recession: US dynamics and spillovers to the world economy," Working papers 17, Former Department of Economics and Public Finance "G. Prato", University of Torino.
  5. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2007. "A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering," CEPR Discussion Papers 6043, C.E.P.R. Discussion Papers.
  6. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
  7. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," NBER Working Papers 10220, National Bureau of Economic Research, Inc.
  8. Felices, Guillermo & Wieladek, Tomasz, 2012. "Are emerging market indicators of vulnerability to financial crises decoupling from global factors?," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 321-331.
  9. Dimitris Korobilis, 2013. "Assessing the Transmission of Monetary Policy Using Time-varying Parameter Dynamic Factor Models-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 157-179, 04.
  10. Castelnuovo, Efrem, 2013. "Monetary policy shocks and financial conditions: A Monte Carlo experiment," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 282-303.
  11. Eickmeier, Sandra & Lemke, Wolfgang & Marcellino, Massimiliano, 2011. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time-Varying FAVAR," CEPR Discussion Papers 8341, C.E.P.R. Discussion Papers.
  12. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
  13. Kaufmann, Sylvia & Schumacher, Christian, 2012. "Finding relevant variables in sparse Bayesian factor models: Economic applications and simulation results," Discussion Papers 29/2012, Deutsche Bundesbank, Research Centre.
  14. Marco Del Negro & Christopher Otrok, 2008. "Dynamic factor models with time-varying parameters: measuring changes in international business cycles," Staff Reports 326, Federal Reserve Bank of New York.
  15. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
  16. Meligkotsidou, Loukia & Vrontos, Ioannis D., 2008. "Detecting structural breaks and identifying risk factors in hedge fund returns: A Bayesian approach," Journal of Banking & Finance, Elsevier, vol. 32(11), pages 2471-2481, November.
  17. Stephan Danninger & Irina Tytell & Ravi Balakrishnan & Selim Elekdag, 2009. "The Transmission of Financial Stress From Advanced to Emerging Economies," IMF Working Papers 09/133, International Monetary Fund.
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Cited by:
  1. Kirsten Thompson & Renee van Eyden & Rangan Gupta, 2013. "Identifying a financial conditions index for South Africa," Working Papers 201333, University of Pretoria, Department of Economics.
  2. Mehmet Balcilar & Kirsten Thompson & Rangan Gupta & Renee van Eyden, 2014. "Testing the Asymmetric Effects of Financial Conditions in South Africa: A Nonlinear Vector Autoregression Approach," Working Papers 201414, University of Pretoria, Department of Economics.

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