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Measuring financial cycles with a model-based filter: Empirical evidence for the United States and the euro area

Author

Listed:
  • Gabriele Galati
  • Irma Hindrayanto
  • Siem Jan Koopman
  • Marente Vlekke

Abstract

The financial cycle captures systematic patterns in the financial system and is closely related to the concept of procyclicality of systemic risk. This paper investigates the characteristics of financial cycles using a multivariate model-based filter. We extract cycles using an unobserved components time series model and applying state space methods. We estimate financial cycles for the United States, Germany, France, Italy, Spain and the Netherlands, using data from 1970 to 2014. For these countries, we find that the individual financial variables we examine have medium-term cycles which share a few common statistical properties. We therefore refer to these cycles as 'similar'. We find that overall financial cycles are longer than business cycles and have a higher amplitude. Moreover, such behaviour varies over time and across countries. Our results suggest that estimates of the financial cycle can be a useful monitoring tool for policymakers as they may provide a broad indication about when risks to financial stability increase, remain stable, or decrease.

Suggested Citation

  • Gabriele Galati & Irma Hindrayanto & Siem Jan Koopman & Marente Vlekke, 2016. "Measuring financial cycles with a model-based filter: Empirical evidence for the United States and the euro area," DNB Working Papers 495, Netherlands Central Bank, Research Department.
  • Handle: RePEc:dnb:dnbwpp:495
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    References listed on IDEAS

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    Cited by:

    1. repec:mnb:finrev:v:17:y:2018:i:4:p:5-22 is not listed on IDEAS
    2. Schüler, Yves S., 2018. "On the cyclical properties of Hamilton's regression filter," Discussion Papers 03/2018, Deutsche Bundesbank.
    3. Schüler, Yves S. & Hiebert, Paul P. & Peltonen, Tuomas A., 2017. "Coherent financial cycles for G-7 countries: Why extending credit can be an asset," ESRB Working Paper Series 43, European Systemic Risk Board.
    4. Markus Behn & Carsten Detken & Tuomas Peltonen & Willem Schudel, 2017. "Predicting Vulnerabilities in the EU Banking Sector: The Role of Global and Domestic Factors," International Journal of Central Banking, International Journal of Central Banking, vol. 13(4), pages 147-189, December.
    5. Önundur Páll Ragnarsson & Jón Magnús Hannesson & Loftur Hreinsson, 2019. "Financial cycles as early warning indicators - Lessons from the Nordic region," Economics wp80, Department of Economics, Central bank of Iceland.
    6. Guido Bulligan & Lorenzo Burlon & Davide Delle Monache & Andrea Silvestrini, 2019. "Real and financial cycles: estimates using unobserved component models for the Italian economy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 541-569, September.
    7. Jorge E. Galán & Javier Mencía, 2018. "Empirical assessment of alternative structural methods for identifying cyclical systemic risk in Europe," Working Papers 1825, Banco de España;Working Papers Homepage.
    8. Dennis Bonam & Peter van Els & Jan Willem van den End & Leo de Haan & Irma Hindrayanto, 2018. "The natural rate of interest from a monetary and financial perspective," DNB Occasional Studies 1603, Netherlands Central Bank, Research Department.
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    10. Schüler, Yves S., 2018. "Detrending and financial cycle facts across G7 countries: mind a spurious medium term!," Working Paper Series 2138, European Central Bank.
    11. Jorge E. Galán, 2019. "Measuring credit-to-gdp gaps. The hodrick-prescott filter revisited," Occasional Papers 1906, Banco de España;Occasional Papers Homepage.
    12. repec:eee:finsta:v:38:y:2018:i:c:p:72-80 is not listed on IDEAS
    13. Mandler, Martin & Scharnagl, Michael, 2019. "Financial cycles across G7 economies: A view from wavelet analysis," Discussion Papers 22/2019, Deutsche Bundesbank.
    14. Larin, Benjamin, 2016. "A Quantitative Model of Bubble-Driven Business Cycles," Annual Conference 2016 (Augsburg): Demographic Change 145817, Verein für Socialpolitik / German Economic Association.
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    16. Rünstler, Gerhard & Balfoussia, Hiona & Burlon, Lorenzo & Buss, Ginters & Comunale, Mariarosaria & De Backer, Bruno & Dewachter, Hans & Guarda, Paolo & Haavio, Markus & Hindrayanto, Irma & Iskrev, Nik, 2018. "Real and financial cycles in EU countries - Stylised facts and modelling implications," Occasional Paper Series 205, European Central Bank.

    More about this item

    Keywords

    unobserved component models; state space method; maximum likelihood; bandpass filter; short and medium term cycles;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • G01 - Financial Economics - - General - - - Financial Crises

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