Hostname: page-component-848d4c4894-x24gv Total loading time: 0 Render date: 2024-05-28T14:00:25.731Z Has data issue: false hasContentIssue false
  • English
  • Français

Macro-financial imbalances and cyclical systemic risk dynamics: understanding the factors driving the financial cycle in the presence of non-linearities

Published online by Cambridge University Press:  25 April 2024

Martin O’Brien  and
Sofia Velasco
Martin O’Brien
Affiliation:
Central Bank of Ireland, Dublin, Ireland
Sofia Velasco*
Affiliation:
European Central Bank, Frankfurt am Main, Germany Queen Mary University of London, London, UK
*
Corresponding author: Sofia Velasco; Email: s.m.velasco@qmul.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper develops a multivariate filter based on an unobserved component model to estimate the financial cycle. Our model features: (1) a dynamic relationship between the financial cycle and key variables; (2) time-varying shock volatility for trend and cycle components. We demonstrate that our approach not only exhibits superior early warning properties for banking crises but also outperforms commonly used indicators in terms of data fit for decomposition exercises, as evidenced by the higher marginal likelihood. We document three important properties of the financial cycle. First, the sensitivity of the financial cycle to changes in real estate valuations increased during the post-90s period. Second, the sensitivity of the cycle to changes in financial conditions displays volatility and country specificities. Finally, our reduced form estimates suggest that the banking crisis of 1988 was preceded by positive contributions from the risk appetite shock, while the primary source of vulnerabilities emanated from the housing market in the run-up to the Global Financial Crisis.

Keywords

Cyclical systemic riskfinancial cyclemultivariate unobserved component modelsstochastic volatility
Type
Articles
Information
Macroeconomic Dynamics , First View , pp. 1 - 20
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anundsen, A. K., Gerdrup, K., Hansen, F. and Kragh-Sørensen, K.. (2016) Bubbles and crises: the role of house prices and credit. Journal of Applied Econometrics 31(7), 1291–1311.CrossRefGoogle Scholar
Arregui, N., Elekdag, S., Gelos, G., Lafarguette, R. and Seneviratne, D.. (2018) Can Countries Manage their Financial Conditions amid Globalization?, Working Paper No. 2018/015, International Monetary Fund.CrossRefGoogle Scholar
Bańbura, M., Giannone, D. and Reichlin, L.. (2010) Large Bayesian vector auto regressions. Journal of Applied Econometrics 25(1), 71–92.CrossRefGoogle Scholar
Bank for International Settlements. (2010). Guidance for national authorities operating the countercyclical capital buffer.Google Scholar
Berger, T., Richter, J. and Wong, B.. (2022) A unified approach for jointly estimating the business and financial cycle, and the role of financial factors. Journal of Economic Dynamics and Control 136, 104315.CrossRefGoogle Scholar
Bernanke, B. S., Boivin, J. and Eliasz, P.. (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach. The Quarterly Journal of Economics 120(1), 387–422.Google Scholar
Beveridge, S. and Nelson, C. R.. (1981) A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle. Journal of Monetary Economics 7(2), 151–174.CrossRefGoogle Scholar
Borio, C. E. V. and Lowe, P. W.. (2004) Securing Sustainable Price Stability: Should Credit Come Back from the Wilderness?, Working Paper No. 157, Bank of International Settlements.Google Scholar
Brunnermeier, M. K. and Sannikov, Y.. (2014) A macroeconomic model with a financial sector. American Economic Review 104(2), 379–421.CrossRefGoogle Scholar
Bussiere, M. and Fratzscher, M.. (2006) Towards a new early warning system of financial crises. Journal of International Money and Finance 25(6), 953–973.CrossRefGoogle Scholar
Camacho, M. and Gadea, M.D.. (2022). Econometric methods for business cycle dating: a practical guide.CrossRefGoogle Scholar
Canova, F. (2011) Methods for Applied Macroeconomic Research. Princeton: Princeton University Press.CrossRefGoogle Scholar
Carriero, A., Clark, T. E., and Marcellino, M. G.. (2016) Large Vector Autoregressions with Stochastic Volatility and Flexible Priors, Working Paper No. 16-17, Federal Reserve Bank of Cleveland.CrossRefGoogle Scholar
Carter, C. K. and Kohn, R.. (1994) On Gibbs sampling for state space models. Biometrika 81(3), 541–553.CrossRefGoogle Scholar
Cecchetti, S. G. (2008) Measuring the macroeconomic risks posed by asset price booms. In Asset Pices and Monetary Policy, pp. 9–43, University of Chicago Press.CrossRefGoogle Scholar
Chan, J. C. C. and Eisenstat, E.. (2015) Marginal likelihood estimation with the Cross-Entropy method. Econometric Reviews 34(3), 256–285.CrossRefGoogle Scholar
Chan, J. C. C. and Eisenstat, E.. (2018) Bayesian model comparison for time-varying parameter vars with stochastic volatility. Journal of applied econometrics 33(4), 509–532.CrossRefGoogle Scholar
Claessens, S., Kose, M. A., Terrones, M. E.. (2012) How do business and financial cycles interact? Journal of International economics 87(1), 178–190.CrossRefGoogle Scholar
Clark, P. K. (1987) The cyclical component of US economic activity. The Quarterly Journal of Economics 102(4), 797–814.CrossRefGoogle Scholar
Clark, T. E. (2011) Real-time density forecasts from Bayesian vector autoregressions with stochastic volatility. Journal of Business & Economic Statistics 29(3), 327–341.CrossRefGoogle Scholar
Cogley, T. and Nason, J. M.. (1995) Effects of the Hodrick-Prescott filter on trend and difference stationary time series implications for business cycle research. Journal of Economic Dynamics and Control 19(1-2), 253–278.CrossRefGoogle Scholar
Cogley, T. and Sargent, T. J.. (2005) Drifts and volatilities: monetary policies and outcomes in the post WWII US. Review of Economic Dynamics 8(2), 262–302.CrossRefGoogle Scholar
Drehmann, M., Borio, C. E. V. and Tsatsaronis, K.. (2012) Characterising the Financial Cycle: Don’t Lose Sight of the Medium Term!, Working Paper No. 380, Bank of International Settlements.Google Scholar
Drehmann, M. and Juselius, J.. (2014) Evaluating early warning indicators of banking crises: satisfying policy requirements. International Journal of Forecasting 30(3), 759–780.CrossRefGoogle Scholar
Dupraz, S., Nakamura, E. and Steinsson, J.. (2019). A plucking model of business cycles, Technical report, National Bureau of Economic Research.CrossRefGoogle Scholar
EU (2013) Directive 2013/36/EU of the European Parliament and of the Council of 26 June 2013 on access to the activity of credit institutions and the prudential supervision of credit institutions and investment firms, amending Directive 2002/87/EC and repealing Directives 2006/48/EC and 2006/49/EC (Capital Requirements Directive IV). Official Journal of the European Union, L 176, 338–436.Google Scholar
Galán, J. E. and Mencia, J.. (2018) Empirical Assessment of Alternative Structural Methods for Identifying Cyclical Systemic Risk in Europe, Working Paper No. 1825, Banco de España.CrossRefGoogle Scholar
Galati, G., Hindrayanto, I., Koopman, S. J. and Vlekke, M.. (2016) Measuring financial cycles in a model-based analysis: empirical evidence for the United States and the euro area. Economics Letters 145, 83–87.CrossRefGoogle Scholar
Ganev, K. (2020) Real-time vs. full-sample performance of one-sided and two-sided HP filters. An application to 27 EU Member States’ GDP Data. Central European Journal of Economic Modelling and Econometrics 12(3), 251–272.Google Scholar
Gersch, W. (1993) Smoothness priors. In New Directions in Time Series Analysis: Part II, pp. 113–146, Springer.CrossRefGoogle Scholar
Geweke, J. and Amisano, G.. (2010) Comparing and evaluating Bayesian predictive distributions of asset returns. International Journal of Forecasting 26(2), 216–230.CrossRefGoogle Scholar
Gilchrist, S. and Zakrajšek, E.. (2012) Credit spreads and business cycle fluctuations. American Economic Review 102(4), 1692–1720.CrossRefGoogle Scholar
Gorton, G. and Ordonez, G.. (2014) Collateral crises. American Economic Review 104(2), 343–378.CrossRefGoogle Scholar
Grant, A. L. and Chan, J. C. C.. (2017) Reconciling output gaps: unobserved components model and Hodrick–Prescott filter. Journal of Economic Dynamics and Control 75, 114–121.CrossRefGoogle Scholar
Guichard, S., Haugh, D. and Turner, D.. (2009) Quantifying the Effect of Financial Conditions in the Euro Area, Japan, United Kingdom and United States, OECD Economics Department Working Papers, No. 677, OECD Publishing.Google Scholar
Hamilton, J. D. (2018) Why you should never use the Hodrick-Prescott filter. Review of Economics and Statistics 100(5), 831–843.CrossRefGoogle Scholar
Hamilton, J. D. and Leff, D.. (2020) Measuring the Credit Gap. San Diego, Mimeo, University of California.Google Scholar
Harding, D. and Pagan, A.. (2002) Dissecting the cycle: a methodological investigation. Journal of Monetary Economics 49(2), 365–381.CrossRefGoogle Scholar
Harvey, A. C. (1985) Trends and cycles in macroeconomic time series. Journal of Business & Economic Statistics 3(3), 216–227.CrossRefGoogle Scholar
Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L. and Watson, M. W.. (2010) Financial Conditions Indexes: A Fresh Look after the Financial Crisis, Working Paper No. 16150, National Bureau of Economic Research.CrossRefGoogle Scholar
Hodrick, R. J. and Prescott, E. C.. (1997) Postwar US business cycles: an empirical investigation. Journal of Money, Credit and Banking 29(1), 1–16.CrossRefGoogle Scholar
Hubrich, K. and Tetlow, R. J.. (2015) Financial stress and economic dynamics: the transmission of crises. Journal of Monetary Economics 70, 100–115.CrossRefGoogle Scholar
Jacquier, E., Polson, N. G. and Rossi, P. E.. (2004) Bayesian analysis of stochastic volatility models with fat-tails and correlated errors. Journal of Econometrics 122(1), 185–212.CrossRefGoogle Scholar
Janes, H., Longton, G. and Pepe, M. S.. (2009) Accommodating covariates in receiver operating characteristic analysis. The Stata Journal 9(1), 17–39.CrossRefGoogle Scholar
Kamber, G., Morley, J. and Wong, B.. (2018) Intuitive and reliable estimates of the output gap from a Beveridge-Nelson filter. Review of Economics and Statistics 100(3), 550–566.CrossRefGoogle Scholar
Kitagawa, G. and Gersch, W.. (1984) A smoothness priors–state space modeling of time series with trend and seasonality. Journal of the American Statistical Association 79(386), 378–389.Google Scholar
Lang, J. H. and Welz, P.. (2018) Semi-structural Credit Gap Estimation, Working Paper Series, No 2194, European Central Bank.CrossRefGoogle Scholar
McConnell, M. M. and Perez-Quiros, G.. (2000) Output fluctuations in the United States: what has changed since the early 1980’s? American Economic Review 90(5), 1464–1476.CrossRefGoogle Scholar
Mehra, Y. P. (2004) The output gap, expected future inflation and inflation dynamics: another look. The B.E. Journal of Macroeconomics, 4(1), 1–19.Google Scholar
Mendoza, E. G. and Terrones, M. E.. (2008) An Anatomy of Credit Booms: Evidence from Macro Aggregates and Micro Data, Working Paper No. 2008/226, International Monetary Fund. CrossRefGoogle Scholar
Mertens, E. (2016) Measuring the level and uncertainty of trend inflation. Review of Economics and Statistics 98(5), 950–967.CrossRefGoogle Scholar
Mitchell, J. and Wallis, K.. (2011) Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness. Journal of Applied Econometrics 26(6), 1023–1040.CrossRefGoogle Scholar
Morley, J. and Piger, J.. (2012) The asymmetric business cycle. Review of Economics and Statistics 94(1), 208–221.CrossRefGoogle Scholar
Mumtaz, H. (2010) Evolving UK Macroeconomic Dynamics: A Time-varying Factor Augmented VAR, Working Paper No. 386, Bank of England.Google Scholar
Nicoletti, G., Wacker, K. M. W. and Lodge, D.. (2014) Measuring Financial Conditions in Major Non-euro Area Economies, Working Paper Series No.1743, European Central Bank.Google Scholar
Primiceri, G. E. (2005) Time varying structural vector autoregressions and monetary policy. The Review of Economic Studies 72(3), 821–852.CrossRefGoogle Scholar
Reinhart, C. M. and Rogoff, K. S.. (2010) Growth in a time of debt. American Economic Review 100(2), 573–578.CrossRefGoogle Scholar
Richter, B., Schularick, M. and Shim, I.. (2019) The costs of macroprudential policy. Journal of International Economics 118, 263–282.CrossRefGoogle Scholar
Rünstler, G. and Vlekke, M.. (2018) Business, housing, and credit cycles. Journal of Applied Econometrics 33(2), 212–226.CrossRefGoogle Scholar
Schüler, Y. S. (2020) On the credit-to-GDP gap and spurious medium-term cycles. Economics Letters 192, 109245.CrossRefGoogle Scholar
Schüler, Y. S., Hiebert, P. P. and Peltonen, T. A.. (2020) Financial cycles: characterisation and real-time measurement. Journal of International Money and Finance 100, 102082.CrossRefGoogle Scholar
Supplementary material: File

O’Brien and Velasco supplementary material

O’Brien and Velasco supplementary material
Download O’Brien and Velasco supplementary material(File)
File 1.4 MB