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Optimizing Credit Gaps for Predicting Financial Crises: Modelling Choices and Tradeoffs

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Abstract
Credit gaps are good predictors for financial crises, and banking regulators recommend using them to inform countercyclical capital buffers for banks. Researchers typically create credit gap measures using trend-cycle decomposition methods, which require many modelling choices, such as the method used, and the smoothness of the underlying trend. Other choices hinge on the tradeoffs implicit in how gaps are used as early warning indicators (EWIs) for predicting crises, such as the preference over false positives and false negatives. We evaluate how the performance of credit-gap-based EWIs for predicting crises is influenced by these modelling choices. For the most common trend-cycle decomposition methods used to recover credit gaps, we find that optimally smoothing the trend enhances out-of-sample prediction. We also show that out-of sample performance improves further when we consider a preference for robustness of the credit gap estimates to the arrival of new information, which is important as any EWI should work in real-time. We offer several practical implications.

Suggested Citation

  • Daniel O. Beltran & Mohammad R. Jahan-Parvar & Fiona A. Paine, 2021. "Optimizing Credit Gaps for Predicting Financial Crises: Modelling Choices and Tradeoffs," International Finance Discussion Papers 1307, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgif:1307
    DOI: 10.17016/IFDP.2021.1307
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    Cited by:

    1. Berger, Tino & Richter, Julia & Wong, Benjamin, 2022. "A unified approach for jointly estimating the business and financial cycle, and the role of financial factors," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    2. Harashima, Taiji, 2020. "A Theory of the Credit-to-GDP Gap: Using Credit Gaps to Predict Financial Crises," MPRA Paper 111732, University Library of Munich, Germany.

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    More about this item

    Keywords

    Credit; Credit Gap; Optimization; Predictive Power; Robustness; 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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