Micro-prudential reverse stress testing as a credit risk management tool

Impact of Bayesian statistics integration

Authors

  • Wassim GHADHAB Univ. Manouba, ESCT, LARIMRAF LR21ES29, Campus universitaire Manouba, 2010, Tunisia
  • Kamel NAOUI Univ. Manouba, ESCT, LARIMRAF LR21ES29, Campus universitaire Manouba, 2010, Tunisia

DOI:

https://doi.org/10.59051/joaf.v15i1.768

Keywords:

reverse stress test, BSVAR, prior distribution, structural approach, scenarios

Abstract

Purpose: This article aims to apply the reverse micro-prudential stress test to credit risk at the Bank for Financing Small and Medium Enterprises (BFPME).

Methodology: It is based on econometric estimations conducted on a quarterly sample of seven (07) variables over the period 2006-2021, incorporating the BSVAR approach of Sims and Zha (1998).

Results: The estimation results demonstrate that the integration of structural Bayesian approach overcomes the limitations of the classical model. The reverse stress test scenario reveals that the credit portfolio of BFPME is expected to turn into NPLs by the end of 2025.

Originality of the article: The article introduces an innovative approach by exploring the integration of Bayesian methods into stress tests, as well as the precise determination of reverse stress test scenarios through prior distribution. It emphasizes the importance for banks to adopt this approach to surpass the limitations of the classical models and add a dimension of decision-makers' reflection.

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Author Biography

Kamel NAOUI, Univ. Manouba, ESCT, LARIMRAF LR21ES29, Campus universitaire Manouba, 2010, Tunisia

Professeur de finance Doyen de l'École Supérieure de Commerce de Tunis (Ecole Supérieure de Commerce de Tunis) Membre du Conseil d'Administration de la BN

References

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Published

2024-06-30

How to Cite

GHADHAB, W., & NAOUI, K. (2024). Micro-prudential reverse stress testing as a credit risk management tool: Impact of Bayesian statistics integration. Journal of Academic Finance, 15(1), 108–122. https://doi.org/10.59051/joaf.v15i1.768