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A New Index of Financial Conditions

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  • Gary, Koop
  • Dimitris, Korobilis

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://repo.sire.ac.uk/handle/10943/475
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Bibliographic Info

Paper provided by Scottish Institute for Research in Economics (SIRE) in its series SIRE Discussion Papers with number 2013-48.

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Date of creation: 2013
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Handle: RePEc:edn:sirdps:475

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Keywords: financial stress; dynamic model averaging; forecasting;

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References

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  1. Catherine Doz & Lucrezia Reichlin, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Post-Print peer-00844811, HAL.
  2. Fabio Bagliano & Claudio Morana, 2010. "The Great Recession: US dynamics and spillovers to the world economy," CeRP Working Papers 103, Center for Research on Pensions and Welfare Policies, Turin (Italy).
  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. 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.
  5. Ben Bernanke & Jean Boivin & Piotr S. Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, MIT Press, vol. 120(1), pages 387-422, January.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Koop, Gary & Korobilis, Dimitris, 2012. "Large Time-Varying Parameter VARs," SIRE Discussion Papers 2012-14, Scottish Institute for Research in Economics (SIRE).
  11. 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.
  12. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, 08.
  13. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
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Cited by:
  1. 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.
  2. 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.

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