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How to Predict Financial Stress? An Assessment of Markov Switching Models

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  • Thibaut Duprey
  • Benjamin Klaus

Abstract

This paper predicts phases of the financial cycle by using a continuous financial stress measure in a Markov switching framework. The debt service ratio and property market variables signal a transition to a high financial stress regime, while economic sentiment indicators provide signals for a transition to a tranquil state. Whereas the in-sample analysis suggests that these indicators can provide an early warning signal up to several quarters prior to the respective regime change, the out-of-sample findings indicate that most of this performance is owing to the data gathered during the global financial crisis. Comparing the prediction performance with a standard binary early warning model reveals that the Markov switching model is outperforming the vast majority of model specifications for a horizon up to three quarters prior to the onset of financial stress.

Suggested Citation

  • Thibaut Duprey & Benjamin Klaus, 2017. "How to Predict Financial Stress? An Assessment of Markov Switching Models," Staff Working Papers 17-32, Bank of Canada.
  • Handle: RePEc:bca:bocawp:17-32
    DOI: 10.34989/swp-2017-32
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    Cited by:

    1. Sarah Vella, 2025. "Constructing a country-specific indicator for cyclical systemic risk," Economic Change and Restructuring, Springer, vol. 58(3), pages 1-63, June.
    2. Kuang-Liang Chang & Charles Ka Yui Leung, 2022. "How did the asset markets change after the Global Financial Crisis?," Chapters, in: Charles K.Y. Leung (ed.), Handbook of Real Estate and Macroeconomics, chapter 12, pages 312-336, Edward Elgar Publishing.
    3. Hyeongwoo Kim & Wen Shi, 2021. "Forecasting financial vulnerability in the USA: A factor model approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 439-457, April.
    4. Bedayo, Mikel & Estrada, Ángel & Saurina, Jesús, 2020. "Bank capital, lending booms, and busts: Evidence from Spain over the last 150 years," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    5. Tihana Skrinjaric, 2023. "Introducing a composite indicator of cyclical systemic risk in Croatia: possibilities and limitations," Public Sector Economics, Institute of Public Finance, vol. 47(1), pages 1-39.
    6. Somnath Chatterjee & Ching‐Wai (Jeremy) Chiu & Thibaut Duprey & Sinem Hacıoğlu‐Hoke, 2022. "Systemic Financial Stress and Macroeconomic Amplifications in the United Kingdom," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(2), pages 380-400, April.
    7. Yusuf Yıldırım & Anirban Sanyal, 2022. "Evaluating the Effectiveness of Early Warning Indicators: An Application of Receiver Operating Characteristic Curve Approach to Panel Data," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 69(4), pages 557-597, December.
    8. Pfeifer, Lukáš & Hodula, Martin, 2018. "A profit-to-provisioning approach to setting the countercyclical capital buffer: the Czech example," ESRB Working Paper Series 82, European Systemic Risk Board.
    9. Phillip J. Monin, 2019. "The OFR Financial Stress Index," Risks, MDPI, vol. 7(1), pages 1-21, February.
    10. Matteo Aquilina & Douglas Kiarelly Godoy de Araujo & Gaston Gelos & Taejin Park & Fernando Perez-Cruz, 2025. "Harnessing artificial intelligence for monitoring financial markets," BIS Working Papers 1291, Bank for International Settlements.
    11. Pfeifer, Lukáš & Hodula, Martin, 2021. "A profit-to-provisioning approach to setting the countercyclical capital buffer," Economic Systems, Elsevier, vol. 45(1).
    12. Li, Zhicheng & Chen, Xinyun & Xing, Haipeng, 2023. "A multifactor regime-switching model for inter-trade durations in the high-frequency limit order market," Economic Modelling, Elsevier, vol. 118(C).
    13. Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Seismonomics: Listening to the heartbeat of the economy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 288-309, December.
    14. Rakovská, Zuzana, 2021. "Composite survey sentiment as a predictor of future market returns: Evidence for German equity indices," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 473-495.
    15. Nikolaos Papanikolaou, 2020. "Markov-Switching Model of Family Income Quintile Shares," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 48(2), pages 207-222, June.

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    JEL classification:

    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • G01 - Financial Economics - - General - - - Financial Crises
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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