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Optimizing composite early warning indicators

Author

Listed:
  • Beltran, Daniel O.
  • Dalal, Vihar M.
  • Jahan-Parvar, Mohammad R.
  • Paine, Fiona A.

Abstract

Research on predicting financial crises has produced various composite early warning indicators (EWIs) using macroeconomic and financial time-series. Much of the focus has been on identifying the best leading indicators for financial crises (e.g., credit-to-GDP ratios, financial asset prices, etc.). This paper instead focuses on how to optimally extract and combine signals from multiple cyclical indicators. We find that when combining multiple indicators into a composite EWI, jointly optimizing the indicators improves performance relative to optimizing individually and combining their signals. The performance of our jointly optimized EWIs is robust to the key modelling choices inherent in their design including the trend-cycle decomposition method and the preference for false positives over false negatives.

Suggested Citation

  • Beltran, Daniel O. & Dalal, Vihar M. & Jahan-Parvar, Mohammad R. & Paine, Fiona A., 2024. "Optimizing composite early warning indicators," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:ecofin:v:74:y:2024:i:c:s106294082400175x
    DOI: 10.1016/j.najef.2024.102250
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    More about this item

    Keywords

    Business cycle; Credit cycle; Early warning indicators; Equity prices; Financial crisis; Optimization; Trend-cycle decomposition;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E39 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Other
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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