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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 coe¢ cients to construct a nancial conditions index. The time-variation in the parameters allows for the weights attached to each nancial variable in the index to evolve over time. Furthermore, we develop methods for dynamic model averaging or selection which allow the nancial 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 nancial variables.

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Bibliographic Info

Paper provided by Business School - Economics, University of Glasgow in its series Working Papers with number 2013_06.

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Handle: RePEc:gla:glaewp:2013_06

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Keywords: financial stress; dynamic model averaging; forecasting;

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  1. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
  2. 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.
  3. 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.
  4. 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.
  5. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2006. "A Two-step estimator for large approximate dynamic factor models based on Kalman filtering," THEMA Working Papers 2006-23, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  6. 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.
  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. Koop, Gary & Korobilis, Dimitris, 2010. "Forecasting Inflation Using Dynamic Model Averaging," SIRE Discussion Papers 2010-113, Scottish Institute for Research in Economics (SIRE).
  9. 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).
  10. Troy Matheson, 2011. "Financial Conditions Indexes for the United States and Euro Area," IMF Working Papers 11/93, International Monetary Fund.
  11. 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.
  12. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  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. 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.
  15. 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.
  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. 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.
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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 2014-468, Department of Research, Ipag Business School.
  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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