The mean reversion/persistence of financial cycles: Empirical evidence for 24 countries worldwide

Authors

DOI:

https://doi.org/10.24136/eq.2023.001

Keywords:

financial cycles, financial connectedness, financial crisis, systemic risk

Abstract

Research background: The globalization trend has inevitably enhanced the connectivity of global financial markets, making the cyclicality of financial activities and the spread of market imbalances have received widespread attention, especially after the global financial crisis.

Purpose of the article: To reduce the negative effects of the contagiousness of the financial cycles, it is necessary to study the persistence of financial cycles and carve out the total connectedness, spillover paths, and sources of risks on a global scale. In addition, understanding the relationship between the financial cycle and economic development is an important way to prevent financial crises.

Methods: This paper adopts the nonlinear smoothing transition autoregressive (STAR) model to extract cyclical and phase characteristics of financial cycles based on 24 countries during 1971Q1?2015Q4, covering developed and developing countries, the Americas, Europe, and Asia regions. In addition, the frequency connectedness approach is used to measure the connectedness of financial cycles and the relationship between the global financial cycle and the global economy.

Findings & value added: The analysis reveals that aggregate financial cycles persist for 13.3 years for smoothed and 8.7 years for unsmoothed on average. The national financial cycles are asynchronous and exhibit more prolonged expansions and faster contractions. The connectedness of financial cycles is highly correlated with systemic crises and contributes to the persistence and harmfulness of shocks. It is mainly driven by short-term components and exhibits more pronounced interconnectedness within regions than across regions. During the financial crisis, the global financial cycle movements precede and are longer than the business fluctuations. Based on the study, some policy implications are presented. This paper emphasizes the impact of systemic crises on the persistence of financial cycles and their connectedness, which contributes to refining research related to the coping mechanisms of financial crises.

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References

Adarov, A. (2022). Financial cycles around the world. International Journal of Finance & Economics, 27(3), 3163?3201. doi: 10.1002/ijfe.2316.

DOI: https://doi.org/10.1002/ijfe.2316
View in Google Scholar

Adarov, A. (2021). Dynamic interactions between financial cycles, business cycles and macroeconomic imbalances: A panel VAR analysis. International Review of Economics & Finance, 74, 434?451. doi: 10.1016/j.iref.2021.03.021.

DOI: https://doi.org/10.1016/j.iref.2021.03.021
View in Google Scholar

Adrian, T., Grinberg, F., Liang, N., Malik, S., & Yu, J. (2022). The term structure of growth-at-risk. American Economic Journal: Macroeconomics, 14, 283?323. doi: 10.1 257/mac.20180428.

DOI: https://doi.org/10.1257/mac.20180428
View in Google Scholar

Akhtaruzzaman, M., Boubaker, S., & Sensoy, A. (2021). Financial contagion during COVID-19 crisis. Finance Research Letters, 38, 101604. doi: 10.1016/j.frl.2020.101 604.

DOI: https://doi.org/10.1016/j.frl.2020.101604
View in Google Scholar

Arndt, H. W., & Hill, H. (1999). Southeast Asia?s economic crisis: Origins, lessons, and the way forward. Institute of Southeast Asian Studies.
View in Google Scholar

Aruoba, S., & Diebold, F. (2009). Real-time measurement of business conditions. Journal of Business and Economic Statistics, 27(4), 417?427. doi: 10.2139/ssrn.127 2128.

DOI: https://doi.org/10.1198/jbes.2009.07205
View in Google Scholar

Barunik, J., & Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16, 271?296. doi: 10.1093/jjfinec/nby001.

DOI: https://doi.org/10.1093/jjfinec/nby001
View in Google Scholar

BenSa?da, A., & Litimi, H. (2021). Financial contagion across G10 stock markets: A study during major crises. International Journal of Finance & Economics, 26(3), 4798?4821. doi: 10.1002/ijfe.2041.

DOI: https://doi.org/10.1002/ijfe.2041
View in Google Scholar

Borio, C. (2011). Rediscovering the macroeconomic roots of financial stability poli-cy: Journey, challenges, and a way forward. Annual Review of Financial Econom-ics, 3(1), 87?117. doi: 10.1146/annurev-financial-102710-144819.

DOI: https://doi.org/10.1146/annurev-financial-102710-144819
View in Google Scholar

Borio, C. (2014). The financial cycle and macroeconomics: What have we learnt? Journal of Banking & Finance, 45, 182?198. doi: 10.1016/j.jbankfin.2013.07.031.

DOI: https://doi.org/10.1016/j.jbankfin.2013.07.031
View in Google Scholar

Borio, C., Disyatat, F. P., & Juselius, M. (2017). Rethinking potential output: Embed-ding information about the financial cycle. Oxford Economic Papers, 69(3), 655?677. doi: 10.1093/oep/gpw063.

DOI: https://doi.org/10.1093/oep/gpw063
View in Google Scholar

Borio, C., Drehmann, M., & Xia, D. (2019). Predicting recessions: financial cycle ver-sus term spread. Retrieved from https://econpapers.repec.org/RePEc:bis:bis wps:818.
View in Google Scholar

Brand?o-Marques, L., Chen, Q., Raddatz, C., Vandenbussche, J., & Xie, P. (2022). The riskiness of credit allocation and financial stability. Journal of Financial Intermediation, 51, 100980. doi: 10.1016/j.jfi.2022.100980.

DOI: https://doi.org/10.1016/j.jfi.2022.100980
View in Google Scholar

Denkowska, A., & Wanat, S. (2020). Dependencies and systemic risk in the Europe-an insurance sector. New evidence-based on Copula-DCC-GARCH model and selected clustering methods. Entrepreneurial Business and Economics Review, 8(4), 7?27. doi: 10.15678/EBER.2020.080401.

DOI: https://doi.org/10.15678/EBER.2020.080401
View in Google Scholar

Dew-Becker, I., & Giglio, S. (2016). Asset pricing in the frequency domain: Theory and empirics. Review of Financial Studies, 29(8), 2029?2068. doi: 10.1093/rfs/hhw 027.

DOI: https://doi.org/10.1093/rfs/hhw027
View in Google Scholar

de Winter, J., Koopman, S. J., & Hindrayanto, I. (2022). Joint decomposition of busi-ness and financial cycles: Evidence from eight advanced economies. Oxford Bulletin of Economics and Statistics, 84(1), 57?79. doi: 10.1111/obes.12459.

DOI: https://doi.org/10.1111/obes.12459
View in Google Scholar

Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive direc-tional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57?66. doi: 10.1016/j.ijforecast.2011.02.006.

DOI: https://doi.org/10.1016/j.ijforecast.2011.02.006
View in Google Scholar

Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompo-sitions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119?134. doi: 10.1016/j.jeconom.2014.04.012.

DOI: https://doi.org/10.1016/j.jeconom.2014.04.012
View in Google Scholar

Dutra, T. M., Dias, J. C., & Teixeira, J. C. A. (2022). Measuring financial cycles: Empir-ical evidence for Germany, United Kingdom and United States of America. In-ternational Review of Economics & Finance, 79, 599?630. doi: 10.1016/j.iref.2022. 02.039.

DOI: https://doi.org/10.1016/j.iref.2022.02.039
View in Google Scholar

Engle, R., & Granger, C. (1987). Cointegration and error-correction: Representation, estimation and testing. Econometrica, 55, 251?276. doi: 10.2307/1913236.

DOI: https://doi.org/10.2307/1913236
View in Google Scholar

Fałdziński, M., Balcerzak, A. P., Meluzín, T., Pietrzak, M. B., & Zinecker, M. (2016). Cointegration of interdependencies among capital markets of chosen Visegrad countries and Germany. In A. Kocourek, M. Vavrousek (Eds.). 34th international conference mathematical methods in economics MME 2016 conference proceedings (pp. 189-194). Liberec: Technical University of Liberec.
View in Google Scholar

Filardo, A., Hubert, P., & Rungcharoenkitkul, P. (2022). Monetary policy reaction function and the financial cycle. Journal of Banking & Finance, 142, 106536. doi: 10.1016/j.jbankfin.2022.106536

DOI: https://doi.org/10.1016/j.jbankfin.2022.106536
View in Google Scholar

Franses, P., & Dijk, D. (2003). Non-linear time series models in empirical finance. Cam-bridge University Press.
View in Google Scholar

Galí, J., Giusti, G., & Noussair, C. N. (2021). Monetary policy and asset price bub-bles: A laboratory experiment. Journal of Economic Dynamics and Control, 130, 104184. doi: 10.1016/j.jedc.2021.104184.

DOI: https://doi.org/10.1016/j.jedc.2021.104184
View in Google Scholar

Gammadigbe, V. (2022). Financial cycles synchronization in WAEMU countries: Implications for macroprudential policy. Finance Research Letters, 46, 102281. doi: 10.1016/j.frl.2021.102281.

DOI: https://doi.org/10.1016/j.frl.2021.102281
View in Google Scholar

Gong, C., & Kim, S. (2018). Regional business cycle synchronization in emerging and developing countries: Regional or global integration? Trade or financial in-tegration? Journal of International Money and Finance, 84, 42?57. doi: 10.1016/j.jim onfin.2018.02.006.

DOI: https://doi.org/10.1016/j.jimonfin.2018.02.006
View in Google Scholar

Gourio, F., Kashyap, A. K., & Sim, J. W. (2018). The trade offs in leaning against the wind. IMF Economic Review, 66(1), 70?115. doi: 10.1057/s41308-017-0043-3.

DOI: https://doi.org/10.1057/s41308-017-0043-3
View in Google Scholar

Huang, C. L. (2020). International stock market co-movements following US finan-cial globalization. International Review of Economics & Finance, 69, 788?814. doi: 10.1016/j.iref.2020.06.009.

DOI: https://doi.org/10.1016/j.iref.2020.06.009
View in Google Scholar

Jing, Z., Liu, Z., Qi, L., & Zhang, X. (2022). Spillover effects of banking systemic risk on firms in China: A financial cycle analysis. International Review of Financial Analysis, 82, 102171. doi: 10.1016/j.irfa.2022.102171.

DOI: https://doi.org/10.1016/j.irfa.2022.102171
View in Google Scholar

Li, X. L., Yan, J., & Wei, X. (2021). Dynamic connectedness among monetary policy cycle, financial cycle and business cycle in China. Economic Analysis and Policy, 69, 640?652. doi: 10.1016/j.eap.2021.01.014.

DOI: https://doi.org/10.1016/j.eap.2021.01.014
View in Google Scholar

Lin, C. F., & Terasvirta, T. (1994). Testing the constancy of regression parameters against continuous structural change. Journal of Econometrics, 62, 211?228. doi: 10.1016/0304-4076(94)90022-1.

DOI: https://doi.org/10.1016/0304-4076(94)90022-1
View in Google Scholar

Luukkonen, R., Saikkonen, P., & Teresavirta, T. (1988). Testing linearity against smooth transition autoregressive models. Biometrika, 75, 491?499. doi: 10.1093/bi omet/75.3.491.

DOI: https://doi.org/10.1093/biomet/75.3.491
View in Google Scholar

Maciejewski, M., & Głodowska, A. (2020). Economic development versus the grow-ing importance of the financial sector: Global insight. International Entrepreneurship Review, 6(3), 77?90. doi: 10.15678/IER.2020.0603.06.

DOI: https://doi.org/10.15678/IER.2020.0603.06
View in Google Scholar

Mazur, M., Dang, M., & Vega, M. (2021). COVID-19 and the march 2020 stock mar-ket crash. Evidence from S&P1500. Finance Research Letters, 38, 101690. doi: 10.1016/j.frl.2020.101690.

DOI: https://doi.org/10.1016/j.frl.2020.101690
View in Google Scholar

Mei, Y., Kun, Z., & Ralescu, A. L. (2020). The dilemma phenomenon, logistics for monetary independence policy and foreign exchange reserves. Soft Computing, 24(9), 6457?6466. doi: 10.1007/s00500-019-04587-y.

DOI: https://doi.org/10.1007/s00500-019-04587-y
View in Google Scholar

Park, C. Y., & Shin, K. (2020). Contagion through national and regional exposures to foreign banks during the global financial crisis. Journal of Financial Stability, 46, 100721. doi: 10.1016/j.jfs.2019.100721.

DOI: https://doi.org/10.1016/j.jfs.2019.100721
View in Google Scholar

Pineda, J., Cortés, L. M., & Perote, J. (2022). Financial contagion drivers during re-cent global crises. Economic Modelling, 117, 106067. doi: 10.1016/j.econmod.2022. 106067.

DOI: https://doi.org/10.1016/j.econmod.2022.106067
View in Google Scholar

Polat, O. (2022). On systemic risk contagion in the euro area: Evidence from fre-quency connectedness and the DY approaches. Borsa Istanbul Review, 22(3), 441?451. doi: 10.1016/j.bir.2021.06.011.

DOI: https://doi.org/10.1016/j.bir.2021.06.011
View in Google Scholar

Qin, Y., Xu, Z. S., Wang, X. X., Skare, M., & Porada Rochoń, M. (2021). Financial cycles in the economy and in economic research: A case study in China. Technological and Economic Development of Economy, 27, 1250?1279. doi: 10.3846/ tede.2021.15439.

DOI: https://doi.org/10.3846/tede.2021.15439
View in Google Scholar

Schnatz, B. (2007). Is reversion to PPP in euro exchange rates non-linear? International Economics and Economic Policy, 4, 281?297. doi: 10.1007/s10368-007-0091-7.

DOI: https://doi.org/10.1007/s10368-007-0091-7
View in Google Scholar

Shen, C. H., Ren, J. Y., Huang, Y. L., Shi, J. G., & Wang, A. Q. (2018). Creating finan-cial cycles in China and interaction with business cycles on the Chinese econo-my. Emerging Markets Finance and Trade, 54(13), 2897?2908. doi: 10.1080/15404 96x.2017.1369402.

DOI: https://doi.org/10.1080/1540496X.2017.1369402
View in Google Scholar

Skare, M., & Porada-Rochon, M. (2020). Multi-channel singular-spectrum analysis of financial cycles in ten developed economies for 1970-2018. Journal of Business Re-search, 112, 567?575. doi: 10.1016/j.jbusres.2019.10.047.

DOI: https://doi.org/10.1016/j.jbusres.2019.10.047
View in Google Scholar

Stekhoven, D. J., & Buehlmann, P. (2012). MissForest-non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112?118. doi: 10.1093/bioin formatics/btr597.

DOI: https://doi.org/10.1093/bioinformatics/btr597
View in Google Scholar

Stiassny, A. (1996). A spectral decomposition for structural VAR models. Empirical Economics, 21, 535?555. doi: 10.1007/BF01180700.

DOI: https://doi.org/10.1007/BF01180700
View in Google Scholar

Strohsal, T., Proa?o, C. R., & Wolters, J. (2019). Characterizing the financial cycle: Evidence from a frequency domain analysis. Journal of Banking & Finance, 106, 568?591. doi: 10.1016/j.jbankfin.2019.06.010.

DOI: https://doi.org/10.1016/j.jbankfin.2019.06.010
View in Google Scholar

Terasvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89(425), 208?218. doi: 10.2307/2291217.

DOI: https://doi.org/10.1080/01621459.1994.10476462
View in Google Scholar

Terasvirta, T., & Anderson, H. (1992). Characterizing nonlinearities in business cycles using smooth transition autoregressive models. Journal of Applied Econometrics, 7, 119?136. doi: 10.1002/jae.3950070509.

DOI: https://doi.org/10.1002/jae.3950070509
View in Google Scholar

Trotta Vianna, M. (2023). Business cycle theories after Keynes: A brief review con-sidering the notions of equilibrium and instability. Structural Change and Economic Dynamics, 64, 134?143. doi: 10.1016/j.strueco.2022.12.004.

DOI: https://doi.org/10.1016/j.strueco.2022.12.004
View in Google Scholar

Ubilava, D. (2022). A comparison of multistep commodity price forecasts using direct and iterated smooth transition autoregressive methods. Agricultural Economics, 53(5), 687?701. doi: 10.1111/agec.12707.

DOI: https://doi.org/10.1111/agec.12707
View in Google Scholar

Umar, Z., Riaz, Y., & Zaremba, A. (2021). Spillover and risk transmission in the components of the term structure of eurozone yield curve. Applied Economics, 53(18), 2141?2157. doi: 10.1080/00036846.2020.1856322.

DOI: https://doi.org/10.1080/00036846.2020.1856322
View in Google Scholar

Wu, F. (2020). Stock market integration in East and Southeast Asia: The role of global factors. International Review of Financial Analysis, 67, 101416. doi: 10.1016/j.irfa. 2019.101416.

DOI: https://doi.org/10.1016/j.irfa.2019.101416
View in Google Scholar

Yan, C., & Huang, K. X. D. (2020). Financial cycle and business cycle: An empirical analysis based on the data from the U.S. Economic Modelling, 93, 693?701. doi: 10.1016/j.econmod.2020.01.018.

DOI: https://doi.org/10.1016/j.econmod.2020.01.018
View in Google Scholar

Zimmerman, E., & Stone, D. (2018). ASEAN think tanks, policy change and eco-nomic cooperation: From the Asian financial crisis to the global financial crisis. Policy and Society, 37(2), 260?275. doi:10.1080/14494035.2017.1397394.

DOI: https://doi.org/10.1080/14494035.2017.1397394
View in Google Scholar

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Published

2023-03-30

How to Cite

Lv, S., Xu, Z., Fan, X., Qin, Y., & Skare, M. . (2023). The mean reversion/persistence of financial cycles: Empirical evidence for 24 countries worldwide. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(1), 11–47. https://doi.org/10.24136/eq.2023.001

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