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On Reverse Stress Testing for Worst Case Scenarios: An Application to Credit Risk Modeling of Tunisian Economic Sectors

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  • Amira Dridi

    (High Institute of management)

Abstract

In Tunisia substantial economic and financial vulnerability are mainly caused by the civil unrest and the subsequent regime change after the Tunisian revolution in 2011. In order to quantify this fragility, in this paper, we use reverse stress testing (RST) based on combination of relevant risk factors that lead to the worst case in which the bank becomes unviable and insolvent. Given the financial stress testing scenarios shortcomings (i.e. plausibility, subjectivity), the use of RST methodology is explained by the fact that it identify the probability of realization of such scenarios. We apply this new methodology on Tunisian banks which are the core for financial stability; more specifically, we focus on credit risk RST. We choose the upper bound for value at risk (VaR) in order to identify worst VaR at probability of realization a. Our proposed framework relies on the logistic regression to identify stress probability and the linear regression of financial stress index output gap versus macroeconomic risk factors in order to search for scenarios at 1%, 3% and 5% levels of plausibility. Empirical results show that Tunisian banks have likely to reach the WVaR in 2012 at 5% level. Besides the more extreme the scenarios which are considered the less plausible they become. Our reverse FST is a complement, and never a substitute for risk manager and what matters most is the mindset of those employing it.

Suggested Citation

  • Amira Dridi, 2015. "On Reverse Stress Testing for Worst Case Scenarios: An Application to Credit Risk Modeling of Tunisian Economic Sectors," International Journal of Economic Sciences, International Institute of Social and Economic Sciences, vol. 4(2), pages 40-56, June.
  • Handle: RePEc:sek:jijoes:v:4:y:2015:i:2:p:40-56
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    References listed on IDEAS

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    1. Illing, Mark & Liu, Ying, 2006. "Measuring financial stress in a developed country: An application to Canada," Journal of Financial Stability, Elsevier, vol. 2(3), pages 243-265, October.
    2. Ruodu Wang & Liang Peng & Jingping Yang, 2013. "Bounds for the sum of dependent risks and worst Value-at-Risk with monotone marginal densities," Finance and Stochastics, Springer, vol. 17(2), pages 395-417, April.
    3. Mr. Stephan Danninger & Ms. Irina Tytell & Mr. Ravi Balakrishnan & Mr. Selim A Elekdag, 2009. "The Transmission of Financial Stress from Advanced to Emerging Economies," IMF Working Papers 2009/133, International Monetary Fund.
    4. repec:zbw:bofrdp:2004_018 is not listed on IDEAS
    5. repec:zbw:bofrdp:2005_013 is not listed on IDEAS
    6. Breuer, Thomas & Jandačka, Martin & Mencía, Javier & Summer, Martin, 2012. "A systematic approach to multi-period stress testing of portfolio credit risk," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 332-340.
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    Cited by:

    1. Cristófoli, María Elizabeth & García Fronti, Javier, 2019. "Macroeconomic Reverse Stress Testing: An Early-Warning System for Spanish Banking Regulators. Analysis Based on the 2008 Global Financial Crisis / Prueba de resistencia inversa Macroeconómica: una pru," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, vol. 9(2), pages 181-204, julio-dic.

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    More about this item

    Keywords

    reverse stress testing; worst case scenarios; credit risk; value at risk; generalized Pareto distribution.;
    All these keywords.

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • G01 - Financial Economics - - General - - - Financial Crises
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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