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A New Index of Financial Conditions

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  • Koop, Gary
  • Korobilis, Dimitris

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.

Suggested Citation

  • Koop, Gary & Korobilis, Dimitris, 2013. "A New Index of Financial Conditions," MPRA Paper 45463, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:45463
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    References listed on IDEAS

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    1. Kaufmann, Sylvia & Schumacher, Christian, 2012. "Finding relevant variables in sparse Bayesian factor models: Economic applications and simulation results," Discussion Papers 29/2012, Deutsche Bundesbank.
    2. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September.
    3. Bates, Brandon J. & Plagborg-Møller, Mikkel & Stock, James H. & Watson, Mark W., 2013. "Consistent factor estimation in dynamic factor models with structural instability," Journal of Econometrics, Elsevier, vol. 177(2), pages 289-304.
    4. Breitung, Jörg & Eickmeier, Sandra, 2011. "Testing for structural breaks in dynamic factor models," Journal of Econometrics, Elsevier, vol. 163(1), pages 71-84, July.
    5. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "Classical time-varying FAVAR models - Estimation, forecasting and structural analysis," CEPR Discussion Papers 8321, C.E.P.R. Discussion Papers.
    6. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Working Papers 334, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
    8. 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, April.
    9. 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.
    10. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, January.
    11. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    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, August.
    13. Angela Abbate & Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2016. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 573-601, June.
    14. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    15. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    16. Kimberly Beaton & René Lalonde & Corinne Luu, 2009. "A Financial Conditions Index for the United States," Discussion Papers 09-11, Bank of Canada.
    17. Bagliano, Fabio C. & Morana, Claudio, 2012. "The Great Recession: US dynamics and spillovers to the world economy," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 1-13.
    18. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    19. William English & Kostas Tsatsaronis & Edda Zoli, 2005. "Assessing the predictive power of measures of financial conditions for macroeconomic variables," BIS Papers chapters, in: Bank for International Settlements (ed.), Investigating the relationship between the financial and real economy, volume 22, pages 228-52, Bank for International Settlements.
    20. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    21. Esteban Gómez & Andrés Murcia & Nancy Zamundio, 2011. "Financial Conditions Index: Early and Leading Indicator for Colombia," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 29(66), pages 174-220, December.
    22. Garratt, Anthony & Koop, Gary & Mise, Emi & Vahey, Shaun P., 2009. "Real-Time Prediction With U.K. Monetary Aggregates in the Presence of Model Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 480-491.
    23. 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.
    24. Helmut Lütkepohl, 2005. "New Introduction to Multiple Time Series Analysis," Springer Books, Springer, number 978-3-540-27752-1, December.
    25. Scott Brave & R. Andrew Butters, 2011. "Monitoring financial stability: a financial conditions index approach," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 35(Q I), pages 22-43.
    26. 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.
    27. 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.
    28. Bénédicte Vidaillet & V. d'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    29. Matheson, Troy D., 2012. "Financial conditions indexes for the United States and euro area," Economics Letters, Elsevier, vol. 115(3), pages 441-446.
    30. Mr. Stephan Danninger & Ms. Irina Tytell & Mr. Ravi Balakrishnan & Mr. Selim A Elekdag, 2009. "The Transmission of Financial Stress from Advanced to Emerging Economies," IMF Working Papers 2009/133, International Monetary Fund.
    31. Jan Hatzius & Peter Hooper & Frederic S. Mishkin & Kermit L. Schoenholtz & Mark W. Watson, 2010. "Financial Conditions Indexes: A Fresh Look after the Financial Crisis," NBER Working Papers 16150, National Bureau of Economic Research, Inc.
    32. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    33. repec:hal:journl:peer-00844811 is not listed on IDEAS
    34. 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.
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    More about this item

    Keywords

    financial stress; dynamic model averaging; forecasting;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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