IDEAS home Printed from https://ideas.repec.org/p/zbw/uhhwps/67.html
   My bibliography  Save this paper

New forecasting methods for an old problem: Predicting 147 years of systemic financial crises

Author

Listed:
  • du Plessis, Emile
  • Fritsche, Ulrich

Abstract

A reflection on the lackluster growth over the decade since the Global Financial Crisis has renewed interest in preventative measures for a long-standing problem. Advances in machine learning algorithms during this period present promising forecasting solutions. In this context, the paper develops new forecasting methods for an old problem by employing 13 machine learning algorithms to study 147 year of systemic financial crises across 17 countries. It entails 12 leading indicators comprising real, banking and external sectors. Four modelling dimensions encompassing a contemporaneous pooled format through an expanding window, transformations with a lag structure and 20-year rolling window as well as individual format are implemented to assess performance through recursive out-of-sample forecasts. Findings suggest fixed capital formation is the most important variable. GDP per capita and consumer inflation have increased in prominence whereas debt-to-GDP, stock market and consumption were dominant at the turn of the 20th century. Through a lag structure, banking sector predictors on average describe 28 percent of the variation in crisis prevalence, real sector 64 percent and external sector 8 percent. A lag structure and rolling window both improve on optimised contemporaneous and individual country formats. Nearly half of all algorithms reach peak performance through a lag structure. As measured through AUC, F1 and Brier scores, top performing machine learning methods consistently produce high accuracy rates, with both random forests and gradient boosting in front with 77 percent correct forecasts. Top models contribute added value above 20 percentage points in most instances and deals with a high degree of complexity across several countries.

Suggested Citation

  • du Plessis, Emile & Fritsche, Ulrich, 2022. "New forecasting methods for an old problem: Predicting 147 years of systemic financial crises," WiSo-HH Working Paper Series 67, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
  • Handle: RePEc:zbw:uhhwps:67
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/264550/1/1816447846.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2018. "An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions?," Discussion Papers 48/2018, Deutsche Bundesbank.
    2. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    3. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
    4. Carmen M. Reinhart & Kenneth S. Rogoff, 2014. "This Time is Different: A Panoramic View of Eight Centuries of Financial Crises," Annals of Economics and Finance, Society for AEF, vol. 15(2), pages 215-268, November.
    5. Andrew Berg & Eduardo Borensztein & Catherine Pattillo, 2005. "Assessing Early Warning Systems: How Have They Worked in Practice?," IMF Staff Papers, Palgrave Macmillan, vol. 52(3), pages 1-5.
    6. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    7. Funke, Manuel & Schularick, Moritz & Trebesch, Christoph, 2016. "Going to extremes: Politics after financial crises, 1870–2014," European Economic Review, Elsevier, vol. 88(C), pages 227-260.
    8. Mr. Luc Laeven & Mr. Fabian Valencia, 2018. "Systemic Banking Crises Revisited," IMF Working Papers 2018/206, International Monetary Fund.
    9. Carmen M. Reinhart & Kenneth S. Rogoff, 2009. "Varieties of Crises and Their Dates," Introductory Chapters, in: This Time Is Different: Eight Centuries of Financial Folly, Princeton University Press.
    10. Felix Ward, 2017. "Spotting the Danger Zone: Forecasting Financial Crises With Classification Tree Ensembles and Many Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 359-378, March.
    11. Marcos Chamon & Paolo Manasse & Alessandro Prati, 2007. "Can We Predict the Next Capital Account Crisis?," IMF Staff Papers, Palgrave Macmillan, vol. 54(2), pages 270-305, June.
    12. Luca Benzoni & Olena Chyruk & David Kelley, 2018. "Why Does the Yield-Curve Slope Predict Recessions?," Chicago Fed Letter, Federal Reserve Bank of Chicago.
    13. E. Philip Davis & Dilruba Karim, 2008. "Could Early Warning Systems Have Helped To Predict the Sub-Prime Crisis?," National Institute Economic Review, National Institute of Economic and Social Research, vol. 206(1), pages 35-47, October.
    14. E. Davis & Dilruba Karim & Iana Liadze, 2011. "Should multivariate early warning systems for banking crises pool across regions?," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 147(4), pages 693-716, November.
    15. Alessi, Lucia & Antunes, Antonio & Babecky, Jan & Baltussen, Simon & Behn, Markus & Bonfim, Diana & Bush, Oliver & Detken, Carsten & Frost, Jon & Guimaraes, Rodrigo & Havranek, Tomas & Joy, Mark & Kau, 2015. "Comparing different early warning systems: Results from a horse race competition among members of the Macro-prudential Research Network," MPRA Paper 62194, University Library of Munich, Germany.
    16. Duttagupta, Rupa & Cashin, Paul, 2011. "Anatomy of banking crises in developing and emerging market countries," Journal of International Money and Finance, Elsevier, vol. 30(2), pages 354-376, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    2. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    3. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
    4. du Plessis, Emile, 2022. "Multinomial modeling methods: Predicting four decades of international banking crises," Economic Systems, Elsevier, vol. 46(2).
    5. Markus Holopainen & Peter Sarlin, 2015. "Toward robust early-warning models: A horse race, ensembles and model uncertainty," Papers 1501.04682, arXiv.org, revised Apr 2016.
    6. Mathonnat, Clément & Minea, Alexandru, 2018. "Financial development and the occurrence of banking crises," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 344-354.
    7. Filippopoulou, Chryssanthi & Galariotis, Emilios & Spyrou, Spyros, 2020. "An early warning system for predicting systemic banking crises in the Eurozone: A logit regression approach," Journal of Economic Behavior & Organization, Elsevier, vol. 172(C), pages 344-363.
    8. Alonso-Alvarez, Irma & Molina, Luis, 2023. "How to foresee crises? A new synthetic index of vulnerabilities for emerging economies," Economic Modelling, Elsevier, vol. 125(C).
    9. Kauko, Karlo, 2014. "How to foresee banking crises? A survey of the empirical literature," Economic Systems, Elsevier, vol. 38(3), pages 289-308.
    10. Martin Bruns & Tigran Poghosyan, 2018. "Leading indicators of fiscal distress: evidence from extreme bounds analysis," Applied Economics, Taylor & Francis Journals, vol. 50(13), pages 1454-1478, March.
    11. Casu, Barbara & Clare, Andrew & Saleh, Nashwa, 2011. "Towards a new model for early warning signals for systemic financial fragility and near crises: an application to OECD countries," MPRA Paper 37043, University Library of Munich, Germany.
    12. Casabianca, Elizabeth Jane & Catalano, Michele & Forni, Lorenzo & Giarda, Elena & Passeri, Simone, 2022. "A machine learning approach to rank the determinants of banking crises over time and across countries," Journal of International Money and Finance, Elsevier, vol. 129(C).
    13. CIMPOERU Smaranda, 2016. "European Economies Facing The Global Financial Crisis: Are Emerging Economies More Vulnerable Than Advanced Ones?," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 68(2), pages 69-85, September.
    14. Rakesh Padhan & K. P. Prabheesh, 2019. "Effectiveness Of Early Warning Models: A Critical Review And New Agenda For Future Direction," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 22(4), pages 457-484, December.
    15. Lainà, Patrizio & Nyholm, Juho & Sarlin, Peter, 2015. "Leading indicators of systemic banking crises: Finland in a panel of EU countries," Review of Financial Economics, Elsevier, vol. 24(C), pages 18-35.
    16. Paolo Manasse & Roberto Savona & Marika Vezzoli, 2013. "Rules of Thumb for Banking Crises in Emerging Markets," Working Papers 481, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    17. Huynh, Tran & Uebelmesser, Silke, 2024. "Early warning models for systemic banking crises: Can political indicators improve prediction?," European Journal of Political Economy, Elsevier, vol. 81(C).
    18. Hamdaoui, Mekki, 2016. "Are systemic banking crises in developed and developing countries predictable?," Journal of Multinational Financial Management, Elsevier, vol. 37, pages 114-138.
    19. Patrizio Lainà & Juho Nyholm & Peter Sarlin, 2015. "Leading indicators of systemic banking crises: Finland in a panel of EU countries," Review of Financial Economics, John Wiley & Sons, vol. 24(1), pages 18-35, January.
    20. Hartwig, Benny & Meinerding, Christoph & Schüler, Yves S., 2021. "Identifying indicators of systemic risk," Journal of International Economics, Elsevier, vol. 132(C).

    More about this item

    Keywords

    machine learning; systemic financial crises; leading indicators; forecasting; early warning signal;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:uhhwps:67. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/fwhamde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.