Empirical Economics

, Volume 57, Issue 2, pp 385–398 | Cite as

Assessing the cross-country interaction of financial cycles: evidence from a multivariate spectral analysis of the USA and the UK

  • Till Strohsal
  • Christian R. ProañoEmail author
  • Jürgen Wolters


In recent times, a large number of studies has investigated the empirical properties of financial cycles within countries, mainly based on band-pass filter techniques. The contribution of this paper to the literature is twofold. First, in contrast to most existing studies in the financial cycle literature, we perform a multivariate parametric frequency domain analysis which takes the complete (cross-) spectrum into account and not only certain frequencies. Second, we provide evidence on the cross-country interaction of financial cycles. We focus on the USA and UK and use frequency-wise Granger causality analysis as well as structural break tests to obtain three main results. The relation between cycles has recently intensified. There is a significant Granger causality from the US financial cycle to the UK financial cycle, but not the other way around. This relationship is most pronounced for cycles between 8 and 30 years.


Financial cycle Vector autoregressions Indirect spectrum estimation Coherency Granger causality 



We are grateful for comments and suggestions received from Helmut Lütkepohl, Dieter Nautz, Christian Merkl, Lars Winkelmann, Sven Schreiber, and various participants at seminars at the Swiss National Bank and the IAB Nürnberg, as well as at the 2015 IAAE and the 2016 CFE conferences. Financial support from the Deutsche Forschungsgemeinschaft (DFG) through CRC 649 “Economic Risk” and by the Macroeconomic Policy Institute (IMK) in the Hans-Böckler Foundation is gratefully acknowledged.

Supplementary material


  1. Aikman D, Haldane AG, Nelson BD (2015) Curbing the credit cycle. Econ J 125(585):1072–1109. CrossRefGoogle Scholar
  2. Borio C (2014) The financial cycle and macroeconomics: what have we learnt? J Bank Finance 45(395):182–98CrossRefGoogle Scholar
  3. Breitung J, Candelon B (2006) Testing for short- and long-run causality: a frequency-domain approach. J Econom 132(2):363–378. CrossRefGoogle Scholar
  4. Breitung J, Eickmeier S (2014) Analyzing business and financial cycles using multi-level factor models. CAMA Working Paper 43/2014, Australian National University, Centre for Applied Macroeconomic AnalysisGoogle Scholar
  5. Burnside C (1998) Detrending and business cycle facts: a comment. J Monet Econ 41(3):513–532CrossRefGoogle Scholar
  6. Canova F (1998) Detrending and business cycle facts. J Monet Econ 41(3):475–512CrossRefGoogle Scholar
  7. Claessens S, Kose MA, Terrones ME (2011) Financial cycles: what? How? When? In: Clarida R, Giavazzi F (eds) NBER International Seminar on Macroeconomics, vol 7. University of Chicago Press, Chicago, pp 303–344Google Scholar
  8. Claessens S, Kose MA, Terrones ME (2012) How do business and financial cycles interact? J Int Econ 97:178–190CrossRefGoogle Scholar
  9. Drehmann M, Borio C, Tsatsaronis K (2012) Characterizing the final cycle: don’t lose sight of the medium term! BIS Working Paper 380, Bank for International SettlementsGoogle Scholar
  10. ECB: Financial Stability Report. European Central Bank, November 2014 (2014)Google Scholar
  11. Ehrmann M, Fratzscher M, Rigobon R (2011) Stocks, bonds, money markets and exchange rates: measuring international financial transmission. J Appl Econom 26(6):948–974CrossRefGoogle Scholar
  12. Ericsson NR (2013) How biased are US government forecasts of the federal debt? Draft, Board of Governors of the Federal Reserve System, Washington, DCGoogle Scholar
  13. Forbes KJ, Chinn MD (2004) A decomposition of global linkages in financial markets over time. Rev Econ Stat 86(3):705–722CrossRefGoogle Scholar
  14. Geweke JF (1982) Measurement of linear dependence and feedback between multiple time series. J Am Stat Assoc 77(378):304–313. CrossRefGoogle Scholar
  15. Geweke JF (1984) Measures of conditional linear dependence and feedback between time series. J Am Stat Assoc 79(388):907–915. CrossRefGoogle Scholar
  16. Hamilton JD (1994) Time series analysis. Princeton University Press, PrincetonGoogle Scholar
  17. Hendry D (2011) Justifying empirical macro-econometric evidence. In: Journal of Economic Surveys. Online 25th Anniversary ConferenceGoogle Scholar
  18. Johansen S (1995) Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press, OxfordCrossRefGoogle Scholar
  19. Kirchgässner G, Wolters J (1994) Frequency domain analysis of euromarket interest rates. In: Kaehler J, Kugler P (eds) Econometric analysis of financial markets, studies in empirical economics. Physica-Verlag, Heidelberg, pp 89–103. Google Scholar
  20. Kirchgässner G, Wolters J, Hassler U (2013) Introduction to modern time series analysis. Springer, BerlinCrossRefGoogle Scholar
  21. Rey H (2015) Dilemma not trilemma: the global financial cycle and monetary policy independence. Working Paper 21162, National Bureau of Economic Research.
  22. Schüler YS, Hiebert P, Peltonen TA (2015) Characterising financial cycles across Europe: one size does not fit all. Available at SSRN 2539717Google Scholar
  23. Strohsal T, Proaño CR, Wolters J (2015) Characterizing the financial cycle: evidence from a frequency domain analysis. SFB Discussion Paper 2015-21Google Scholar
  24. Wolters J (1980) Stochastic dynamic properties of linear econometric models. In: Beckmann M, Künzi H (eds) Lecture notes in economics and mathematical systems, vol 182. Springer, Berlin, pp 1–154Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Till Strohsal
    • 1
  • Christian R. Proaño
    • 2
    Email author
  • Jürgen Wolters
    • 1
  1. 1.Freie Universität BerlinBerlinGermany
  2. 2.Otto-Friedrich-Universität BambergBambergGermany

Personalised recommendations