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Forecasting Bank Default with the Merton Model: The Case of US Banks

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Computational Science – ICCS 2022 (ICCS 2022)

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Abstract

This paper examines whether the probability of default (Merton 1974) can be applied to banks’ default predictions. Using the case of US banks in the post-crisis period (2010–2014), we estimate several Cox proportional hazard models as well as their out-of-sample performance. As a result, we find that the Merton measure, that is, the probability of default, is not a sufficient statistic for predicting bank default, while, with the 6-month forecasting horizon, it is an extremely significant predictor and its functional form is a useful construct for predicting bank default. Findings suggest that (i) predicting banks’ defaults over a mid- to long-term horizon can be done more effectively by adding the inverse of equity volatility and the value of net income over total assets, and (ii) the role of the capital adequacy ratio is doubtful even in short-run default prediction.

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Notes

  1. 1.

    In particular, Ji et al. [9] employed the time-varying volatility when calculating Merton’s DD and its extension, DC, by sampling the posterior distribution and proposed an early warning indicator using the difference between DD and DC.

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Acknowledgements

This research was supported by the Future-leading Research Initiative at Yonsei University (Grant Number: 2021–22-0306; K.A.).

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Correspondence to Jongwook Jeong or Kwangwon Ahn .

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Jo, K., Choi, G., Jeong, J., Ahn, K. (2022). Forecasting Bank Default with the Merton Model: The Case of US Banks. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13350. Springer, Cham. https://doi.org/10.1007/978-3-031-08751-6_49

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  • DOI: https://doi.org/10.1007/978-3-031-08751-6_49

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