[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/cbk/journl/v11y2022i2p145-164.html
   My bibliography  Save this article

Adaptive Early Warning Systems: An Axiomatic Approach

Author

Listed:
  • Diptes C. P. Bhimjee

    (ISCTE – University Institute of Lisbon, Lisbon, Portugal)

Abstract
The U.S. Subprime Crisis and the subsequent Great Recession have highlighted a renewed interest in the proper design and implementation of Early Warning Systems (E.W.S.), in order to help deter the onset of subsequent extreme financial events, through the implementation of adequate crisis detection mechanisms. The present article describes the Adaptive Early Warning Systems (A.E.W.S.) axiomatic approach, as a natural operational extension to E.W.S. testing. This novel protocol upholds the operational dimension of implementing an efficient holistic crisis detection mechanism, a domain which has been hitherto overlooked by the E.W.S. literature. The paper first describes the major axiomatic principles sustaining the A.E.W.S. protocol, which seek to establish universal principles in support of the said protocol. Second, the article also describes a basic universal template for an A.E.W.S. surveillance platform, which duly describes how multiple testing procedures can be integrated into a single crisis detection framework, while targeting multiple segments of the financial markets (such as the conventional and non-conventional segments of the financial markets). Third, the paper also describes the major advantages and disadvantages associated with the implementation of this novel protocol. It is hoped that the effective implementation of the A.E.W.S. protocol as a novel operational framework in the global macroprudential toolkit might help deter the onset of future extreme financial events, by enabling a greater cohesiveness in E.W.S.-related central banking procedures, as well as promoting a greater international central banking cooperation prior to and during financial distress episodes.

Suggested Citation

  • Diptes C. P. Bhimjee, 2022. "Adaptive Early Warning Systems: An Axiomatic Approach," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 11(2), pages 145-164.
    • Handle: RePEc:cbk:journl:v:11:y:2022:i:2:p:145-164
    as

    Download full text from publisher

    File URL: http://www.cbcg.me/repec/cbk/journl/vol11no2-7.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nenad Milojević & Srdjan Redzepagic, 2021. "Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(3), pages 41-57.
    2. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    3. Markus K. Brunnermeier & Lasse Heje Pedersen, 2009. "Market Liquidity and Funding Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 22(6), pages 2201-2238, June.
    4. Morris Goldstein & Graciela Kaminsky & Carmen Reinhart, 2017. "Methodology and Empirical Results," World Scientific Book Chapters, in: TRADE CURRENCIES AND FINANCE, chapter 11, pages 397-436, World Scientific Publishing Co. Pte. Ltd..
    5. Charles P. Kindleberger & Robert Z. Aliber, 2005. "Manias, Panics and Crashes," Palgrave Macmillan Books, Palgrave Macmillan, edition 0, number 978-0-230-62804-5, October.
    6. Frankel, Jeffrey & Saravelos, George, 2012. "Can leading indicators assess country vulnerability? Evidence from the 2008–09 global financial crisis," Journal of International Economics, Elsevier, vol. 87(2), pages 216-231.
    7. Sarlin, Peter, 2013. "On policymakers’ loss functions and the evaluation of early warning systems," Economics Letters, Elsevier, vol. 119(1), pages 1-7.
    8. Babecký, Jan & Havránek, Tomáš & Matějů, Jakub & Rusnák, Marek & Šmídková, Kateřina & Vašíček, Bořek, 2014. "Banking, debt, and currency crises in developed countries: Stylized facts and early warning indicators," Journal of Financial Stability, Elsevier, vol. 15(C), pages 1-17.
    9. Serena Ng, 2014. "Viewpoint: Boosting Recessions," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 47(1), pages 1-34, February.
    10. Dirk Schoenmaker & Peter Wierts, 2016. "Macroprudential Supervision: From Theory to Policy," National Institute Economic Review, National Institute of Economic and Social Research, vol. 235(1), pages 50-62, February.
    11. Reinhart, Carmen & Goldstein, Morris & Kaminsky, Graciela, 2000. "Assessing financial vulnerability, an early warning system for emerging markets: Introduction," MPRA Paper 13629, University Library of Munich, Germany.
    12. Alessi, Lucia & Detken, Carsten, 2014. "On policymakers’ loss functions and the evaluation of early warning systems: Comment," Economics Letters, Elsevier, vol. 124(3), pages 338-340.
    13. Davis, E. Philip & Karim, Dilruba, 2008. "Comparing early warning systems for banking crises," Journal of Financial Stability, Elsevier, vol. 4(2), pages 89-120, June.
    14. Shikha Singh & Mandira Sarma, 2020. "Financial Structure and Stability: An Empirical Exploration," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(special i), pages 9-32.
    15. Laurence Ball, 2014. "Long-term damage from the Great Recession in OECD countries," European Journal of Economics and Economic Policies: Intervention, Edward Elgar Publishing, vol. 11(2), pages 149-160, September.
    16. Stijn Claessens, 2015. "An Overview of Macroprudential Policy Tools," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 397-422, December.
    17. Liu, Weiling & Moench, Emanuel, 2016. "What predicts US recessions?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1138-1150.
    18. Bhimjee, Diptes C. & Ramos, Sofia B. & Dias, José G., 2016. "Banking industry performance in the wake of the global financial crisis," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 376-387.
    19. Graciela Laura Kaminsky, 1999. "Currency and Banking Crises: The Early Warnings of Distress," IMF Working Papers 1999/178, International Monetary Fund.
    20. Željka Asanović, 2020. "Essay on Finance-Growth Nexus," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(1), pages 97-109.
    21. Dirk Schoenmaker & Peter Wierts, 2016. "Macroprudential Supervision: From Theory to Policy," National Institute Economic Review, National Institute of Economic and Social Research, vol. 235(1), pages 50-62, February.
    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. Marcela Guachamín & Diana Ramírez‐Cifuentes & Olga Delgado, 2020. "An Uncertainty Thermometer to Measure the Macroeconomic‐Financial Risk in South American Countries," Journal of International Development, John Wiley & Sons, Ltd., vol. 32(6), pages 854-890, August.
    2. K. Batu Tunay, 2010. "Banking Crises and Early Warning Systems: A Model Suggestion for Turkish Banking Sector," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 4(1), pages 9-46.
    3. Christofides, Charis & Eicher, Theo S. & Papageorgiou, Chris, 2016. "Did established Early Warning Signals predict the 2008 crises?," European Economic Review, Elsevier, vol. 81(C), pages 103-114.
    4. Sondermann, David & Zorell, Nico, 2019. "A macroeconomic vulnerability model for the euro area," Working Paper Series 2306, European Central Bank.
    5. Babecký, Jan & Havránek, Tomáš & Matějů, Jakub & Rusnák, Marek & Šmídková, Kateřina & Vašíček, Bořek, 2013. "Leading indicators of crisis incidence: Evidence from developed countries," Journal of International Money and Finance, Elsevier, vol. 35(C), pages 1-19.
    6. Basabi Bhattacharya & Tanima Niyogi Sinha Roy, 2012. "Indicators of Banking Fragility in India: An Empirical Test," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 13(2), pages 265-290, September.
    7. Mpho Bosupeng, 2018. "Leading Indicators and Financial Crisis: A Multi-Sectoral Approach Using Signal Extraction," Journal of Empirical Studies, Conscientia Beam, vol. 5(1), pages 20-44.
    8. Stijn Claessens & M. Ayhan Kose, 2013. "Financial Crises: Explanations, Types and Implications," CAMA Working Papers 2013-06, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Cipollini, A. & Kapetanios, G., 2009. "Forecasting financial crises and contagion in Asia using dynamic factor analysis," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 188-200, March.
    10. Mikkel Hermansen & Oliver Röhn, 2017. "Economic resilience: The usefulness of early warning indicators in OECD countries," OECD Journal: Economic Studies, OECD Publishing, vol. 2016(1), pages 9-35.
    11. Mustapha Djennas & Mohamed Benbouziane & Meriem Djennas, 2011. "An Approach of Combining Empirical Mode Decomposition and Neural Network Learning for Currency Crisis Forecasting," Working Papers 627, Economic Research Forum, revised 09 Jan 2011.
    12. Mamdouh Abdelmoula M.Abdelsalam & Hany Abdel-Latif, 2020. "An optimal early warning system for currency crises under model uncertainty," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 20(3), pages 99-107.
    13. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    14. Cruz-Rodríguez Alexis, 2013. "The Relationship between Fiscal Sustainability and Currency Crises in Some Selected Countries," Review of Economic Perspectives, Sciendo, vol. 13(4), pages 176-194, December.
    15. 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.
    16. Hali J. Edison, 2003. "Do indicators of financial crises work? An evaluation of an early warning system," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 8(1), pages 11-53.
    17. Lin, Chin-Shien & Khan, Haider A. & Chang, Ruei-Yuan & Wang, Ying-Chieh, 2008. "A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?," Journal of International Money and Finance, Elsevier, vol. 27(7), pages 1098-1121, November.
    18. Ihejirika, Peters. O, 2020. "Does the Credit-to-GDP Gap Predict Financial Crisis in Nigeria?," International Journal of Social and Administrative Sciences, Asian Economic and Social Society, vol. 5(2), pages 109-126, June.
    19. Correia, Ricardo & Dubiel-Teleszynski, Tomasz & Población, Javier, 2019. "Anticipating individual bank rescues," Economic Modelling, Elsevier, vol. 82(C), pages 345-360.
    20. Andrea Eross & Andrew Urquhart & Simon Wolfe, 2019. "Investigating risk contagion initiated by endogenous liquidity shocks: evidence from the US and eurozone interbank markets," The European Journal of Finance, Taylor & Francis Journals, vol. 25(1), pages 35-53, January.

    More about this item

    Keywords

    Adaptive Early Warning Systems; Forecasting; Financial Crises; Central Banks; Financial Stability; Macroprudential Regulation; Monetary Policy.;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G01 - Financial Economics - - General - - - Financial Crises
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

    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:cbk:journl:v:11:y:2022:i:2:p:145-164. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/cbmgvme.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.