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Predicting Financial Stress Events: A Signal Extraction Approach

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  • Ian Christensen
  • Fuchun Li

Abstract

The objective of this paper is to propose an early warning system that can predict the likelihood of the occurrence of financial stress events within a given period of time. To achieve this goal, the signal extraction approach proposed by Kaminsky, Lizondo and Reinhart (1998) is used to monitor the evolution of a number of economic indicators that tend to exhibit an unusual behaviour in the periods preceding a financial stress event. Based on the individual indicators, we propose three different composite indicators, the summed composite indicator, the extreme composite indicator and the weighted composite indicator. In-sample forecasting results indicate that the three composite indicators are useful tools for predicting financial stress events. The out-of-sample forecasting results suggest that for most countries, including Canada, the weighted composite indicator performs better than the two others across all criteria considered.

Suggested Citation

  • Ian Christensen & Fuchun Li, 2014. "Predicting Financial Stress Events: A Signal Extraction Approach," Staff Working Papers 14-37, Bank of Canada.
  • Handle: RePEc:bca:bocawp:14-37
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    2. Hong Puah, Chin & Kuek, Tai Hock & Arip, M. Affendy, 2017. "Assessing Thailand’s financial vulnerability: An early warning approach," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 13(4), October.
    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. Truong, Chi & Sheen, Jeffrey & Trück, Stefan & Villafuerte, James, 2022. "Early warning systems using dynamic factor models: An application to Asian economies," Journal of Financial Stability, Elsevier, vol. 58(C).
    5. Hyeongwoo Kim & Wen Shi & Hyun Hak Kim, 2020. "Forecasting financial stress indices in Korea: a factor model approach," Empirical Economics, Springer, vol. 59(6), pages 2859-2898, December.
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    8. Layal MansourIshrakieh & Leila Dagher & Sadika El Hariri, 2020. "A financial stress index for a highly dollarized developing country : The case of Lebanon," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 20(2), pages 43-52.
    9. Domonkos Tomáš & Ostrihoň Filip & Šikulová Ivana & Širaňová Mária, 2017. "Analysing the Relevance of the MIP Scoreboard's Indicators," National Institute Economic Review, National Institute of Economic and Social Research, vol. 239(1), pages 32-52, February.
    10. Suh, Sangwon, 2017. "Sudden stops of capital flows to emerging markets: A new prediction approach," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 289-308.
    11. Erhart, Szilard, 2019. "Exposition, climax, denouement: Life-cycle evaluation of the recent financial crisis in the EU by linking the ESRB financial crisis database to the European Commission's Macroeconomic Imbalance Proced," ESRB Working Paper Series 102, European Systemic Risk Board.
    12. Hyeongwoo Kim & Jisoo Son, 2023. "Forecasting Net Charge-Off Rates of Large U.S. Bank Holding Companies using Macroeconomic Latent Factors," Auburn Economics Working Paper Series auwp2023-02, Department of Economics, Auburn University.
    13. Tomáš Domonkos & Filip Ostrihoň & Ivana Šikulová & Maria Širaňová, 2016. "Analyzing macroeconomic imbalances in the EU," EcoMod2016 9660, EcoMod.
    14. Alexander Guarín-López & Ignacio Lozano-Espitia, 2016. "Credit Funding and Banking Fragility: An Empirical Analysis for Emerging Economies," Borradores de Economia 14306, Banco de la Republica.
    15. Mansour Ishrakieh, Layal & Dagher, Leila & El Hariri, Sadika, 2018. "The Institute of Financial Economics Financial Stress Index (IFEFSI) for Lebanon," MPRA Paper 116054, University Library of Munich, Germany.
    16. Hyeongwoo Kim & Kyunghwan Ko, 2017. "Improving Forecast Accuracy of Financial Vulnerability: Partial Least Squares Factor Model Approach," Working Papers 2017-14, Economic Research Institute, Bank of Korea.
    17. Fuchun Li & Hongyu Xiao, 2016. "Early Warning of Financial Stress Events: A Credit-Regime-Switching Approach," Staff Working Papers 16-21, Bank of Canada.
    18. Tai-Hock Kuek & Chin-Hong Puah & M. Affendy Arip, 2019. "Predicting Financial Vulnerability in Malaysia: Evidence From the Signals Approach," Research in World Economy, Research in World Economy, Sciedu Press, vol. 10(3), pages 89-98, December.
    19. María Fernanda Meneses-González & Javier Eliecer Pirateque-Niño & Santiago David Segovia-Baquero, 2019. "Indicadores de alerta temprana para el sector corporativo privado colombiano," Borradores de Economia 1084, Banco de la Republica de Colombia.
    20. Kim, Hyeongwoo & Son, Jisoo, 2023. "What Charge-Off Rates Are Predictable by Macroeconomic Latent Factors?," MPRA Paper 116880, University Library of Munich, Germany.
    21. Allen, Linda & Tang, Yi, 2016. "What’s the contingency? A proposal for bank contingent capital triggered by systemic risk," Journal of Financial Stability, Elsevier, vol. 26(C), pages 1-14.
    22. Tihana Skrinjaric, 2023. "Leading indicators of financial stress in Croatia: a regime switching approach," Public Sector Economics, Institute of Public Finance, vol. 47(2), pages 205-232.
    23. Vašíček, Bořek & Žigraiová, Diana & Hoeberichts, Marco & Vermeulen, Robert & Šmídková, Kateřina & de Haan, Jakob, 2017. "Leading indicators of financial stress: New evidence," Journal of Financial Stability, Elsevier, vol. 28(C), pages 240-257.
    24. Hyeongwoo Kim & Wen Shi, 2015. "Forecasting Financial Market Vulnerability in the U.S.: A Factor Model Approach," Auburn Economics Working Paper Series auwp2015-04, Department of Economics, Auburn University.
    25. Mansour-Ichrakieh, Layal & Zeaiter, Hussein, 2019. "The role of geopolitical risks on the Turkish economy opportunity or threat," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).

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    More about this item

    Keywords

    Econometric and statistical methods; Financial stability;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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