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Time–frequency characterization of the U.S. financial cycle

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  • Verona, Fabio

Abstract

Despite an increase in research–motivated by the global financial crisis of 2007–08–empirical studies on the financial cycle are rare compared to those on the business cycle. This paper adds some new evidence to this scarce literature by using a different empirical methodology–wavelet analysis–to extract financial cycles from the data. Our results confirm that the U.S. financial cycle is (much) longer than the business cycle, but we do not find strong evidence supporting the view that the financial cycle has lengthened during the Great Moderation period.

Suggested Citation

  • Verona, Fabio, 2016. "Time–frequency characterization of the U.S. financial cycle," Economics Letters, Elsevier, vol. 144(C), pages 75-79.
  • Handle: RePEc:eee:ecolet:v:144:y:2016:i:c:p:75-79
    DOI: 10.1016/j.econlet.2016.04.024
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    1. Ramsey James B. & Lampart Camille, 1998. "The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(1), pages 1-22, April.
    2. Strohsal, Till & Proaño, Christian R. & Wolters, Jürgen, 2019. "Characterizing the financial cycle: Evidence from a frequency domain analysis," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 568-591.
    3. Claessens, Stijn & Kose, M. Ayhan & Terrones, Marco E., 2012. "How do business and financial cycles interact?," Journal of International Economics, Elsevier, vol. 87(1), pages 178-190.
    4. Marczak, Martyna & Gómez, Víctor, 2015. "Cyclicality of real wages in the USA and Germany: New insights from wavelet analysis," Economic Modelling, Elsevier, vol. 47(C), pages 40-52.
    5. Aguiar-Conraria, LuI´s & Joana Soares, Maria, 2011. "Business cycle synchronization and the Euro: A wavelet analysis," Journal of Macroeconomics, Elsevier, vol. 33(3), pages 477-489, September.
    6. Diego Comin & Mark Gertler, 2006. "Medium-Term Business Cycles," American Economic Review, American Economic Association, vol. 96(3), pages 523-551, June.
    7. Ramsey, James B. & Lampart, Camille, 1998. "Decomposition Of Economic Relationships By Timescale Using Wavelets," Macroeconomic Dynamics, Cambridge University Press, vol. 2(1), pages 49-71, March.
    8. Ko, Jun-Hyung & Lee, Chang-Min, 2015. "International economic policy uncertainty and stock prices: Wavelet approach," Economics Letters, Elsevier, vol. 134(C), pages 118-122.
    9. António Rua, 2012. "Money Growth and Inflation in the Euro Area: A Time-Frequency View," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(6), pages 875-885, December.
    10. Rua, António & Nunes, Luís C., 2009. "International comovement of stock market returns: A wavelet analysis," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 632-639, September.
    11. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2012. "The yield curve and the macro-economy across time and frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1950-1970.
    12. Patrick M. Crowley & David G. Mayes, 2009. "How fused is the euro area core?: An evaluation of growth cycle co-movement and synchronization using wavelet analysis," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2008(1), pages 63-95.
    13. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.
    14. Claudio Borio, 2014. "The financial cycle and macroeconomics: what have we learned and what are the policy implications?," Chapters, in: Ewald Nowotny & Doris Ritzberger-Grünwald & Peter Backé (ed.), Financial Cycles and the Real Economy, chapter 2, pages 10-35, Edward Elgar Publishing.
    15. Caraiani, Petre, 2012. "Stylized facts of business cycles in a transition economy in time and frequency," Economic Modelling, Elsevier, vol. 29(6), pages 2163-2173.
    16. Mathias Drehmann & Claudio Borio & Kostas Tsatsaronis, 2012. "Characterising the financial cycle: don't lose sight of the medium term!," BIS Working Papers 380, Bank for International Settlements.
    17. Borio, Claudio, 2014. "The financial cycle and macroeconomics: What have we learnt?," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 182-198.
    18. Caraiani, Petre, 2012. "Money and output: New evidence based on wavelet coherence," Economics Letters, Elsevier, vol. 116(3), pages 547-550.
    19. Gallegati, Marco & Ramsey, James B., 2013. "Bond vs stock market's Q: Testing for stability across frequencies and over time," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 138-150.
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    1. Aguiar-Conraria, Luis & Martins, Manuel M.F. & Soares, Maria Joana, 2018. "Estimating the Taylor rule in the time-frequency domain," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 122-137.
    2. Hudgins, David & Crowley, Patrick M., 2017. "Modelling a small open economy using a wavelet-based control model," Research Discussion Papers 32/2017, Bank of Finland.
    3. Potjagailo, Galina & Wolters, Maik H., 2019. "Global financial cycles since 1880," Kiel Working Papers 2122, Kiel Institute for the World Economy (IfW).
    4. Strohsal, Till & Proaño, Christian R. & Wolters, Jürgen, 2019. "Characterizing the financial cycle: Evidence from a frequency domain analysis," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 568-591.
    5. Karlo Kauko & Eero Tölö, 2019. "Banking Crisis Prediction with Differenced Relative Credit," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot GmbH, Berlin, vol. 65(4), pages 277-297.
    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. Crowley, Patrick M. & Hudgins, David, 2019. "U.S. Macroeconomic Policy Evaluation in an Open Economy Context using Wavelet Decomposed Optimal Control Methods," Research Discussion Papers 11/2019, Bank of Finland.
    8. Yan, Chuanpeng & Huang, Kevin X.D., 2020. "Financial cycle and business cycle: An empirical analysis based on the data from the U.S," Economic Modelling, Elsevier, vol. 93(C), pages 693-701.
    9. Kapounek, Svatopluk & Kučerová, Zuzana, 2019. "Historical decoupling in the EU: Evidence from time-frequency analysis," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 265-280.
    10. Kunovac, Davor & Mandler, Martin & Scharnagl, Michael, 2018. "Financial cycles in euro area economies: A cross-country perspective," Discussion Papers 04/2018, Deutsche Bundesbank.
    11. Schüler, Yves S. & Hiebert, Paul P. & Peltonen, Tuomas A., 2020. "Financial cycles: Characterisation and real-time measurement," Journal of International Money and Finance, Elsevier, vol. 100(C).
    12. Gallegati, Marco & Giri, Federico & Palestrini, Antonio, 2019. "DSGE model with financial frictions over subsets of business cycle frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 152-163.
    13. Verona, Fabio & Martins, Manuel M.F. & Drumond, Inês, 2017. "Financial shocks, financial stability, and optimal Taylor rules," Journal of Macroeconomics, Elsevier, vol. 54(PB), pages 187-207.
    14. Schüler, Yves S., 2018. "On the cyclical properties of Hamilton's regression filter," Discussion Papers 03/2018, Deutsche Bundesbank.
    15. Schüler, Yves S. & Peltonen, Tuomas A. & Hiebert, Paul, 2017. "Coherent financial cycles for G-7 countries: Why extending credit can be an asset," ESRB Working Paper Series 43, European Systemic Risk Board.
    16. Kauko, Karlo & Tölö, Eero, 2019. "On the long-run calibration of the credit-to-GDP gap as a banking crisis predictor," Research Discussion Papers 6/2019, Bank of Finland.
    17. Voutilainen, Ville, 2017. "Wavelet decomposition of the financial cycle : An early warning system for financial tsunamis," Research Discussion Papers 11/2017, Bank of Finland.
    18. Rachida Hennani & John Theal, 2019. "Characterizing the Luxembourg financial cycle: Alternatives to statistical filters," BCL working papers 133, Central Bank of Luxembourg.
    19. Schüler, Yves S., 2018. "Detrending and financial cycle facts across G7 countries: mind a spurious medium term!," Working Paper Series 2138, European Central Bank.
    20. Hiebert, Paul & Jaccard, Ivan & Schüler, Yves, 2018. "Contrasting financial and business cycles: Stylized facts and candidate explanations," Journal of Financial Stability, Elsevier, vol. 38(C), pages 72-80.
    21. Mandler, Martin & Scharnagl, Michael, 2019. "Financial cycles across G7 economies: A view from wavelet analysis," Discussion Papers 22/2019, Deutsche Bundesbank.

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    More about this item

    Keywords

    Time–frequency estimation; Wavelets; Financial cycle; Business cycle; Credit; Asset prices;
    All these keywords.

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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