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Loan default correlation using an Archimedean copula approach: A case for recalibration

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  • Fenech, Jean Pierre
  • Vosgha, Hamed
  • Shafik, Salwa

Abstract

Appropriate modelling of loan default correlation capturing the fat tail distributions and non-symmetrical behaviour linked to the sensitivity of the loss correlations is a prerequisite for effective credit risk management, as banks seek to optimally allocate capital. In this study, we provide an insight to the use of copula functions, particularly addressing the key question of why Gaussian copulas caused so much instability during 2007–08. We empirically demonstrate that using an Archimedean copula, particularly the Gumbel, it is more efficient in capturing the top right hand side tail-dependencies, thereby illustrating the impact of fat-tails on non-linear parameters. This finding has significant implications for banks and their capital management requirements, particularly banks employing the Advanced Internal Rate-Based method. This is even more relevant now, with Basel III providing more detailed information as to what constitutes Tier 1, Additional Tier 1 and 2 Capital.

Suggested Citation

  • Fenech, Jean Pierre & Vosgha, Hamed & Shafik, Salwa, 2015. "Loan default correlation using an Archimedean copula approach: A case for recalibration," Economic Modelling, Elsevier, vol. 47(C), pages 340-354.
    • Handle: RePEc:eee:ecmode:v:47:y:2015:i:c:p:340-354
    DOI: 10.1016/j.econmod.2015.03.001
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    References listed on IDEAS

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    Cited by:

    1. Pourkhanali, Armin & Kim, Jong-Min & Tafakori, Laleh & Fard, Farzad Alavi, 2016. "Measuring systemic risk using vine-copula," Economic Modelling, Elsevier, vol. 53(C), pages 63-74.
    2. Liping Wang & Xingnan Zhang & Shufang Wang & Mohamed Khaled Salahou & Yuanhao Fang, 2020. "Analysis and Application of Drought Characteristics Based on Theory of Runs and Copulas in Yunnan, Southwest China," IJERPH, MDPI, vol. 17(13), pages 1-17, June.
    3. Peter Grundke & Kamil Pliszka & Michael Tuchscherer, 2020. "Model and estimation risk in credit risk stress tests," Review of Quantitative Finance and Accounting, Springer, vol. 55(1), pages 163-199, July.
    4. Bax, Karoline & Sahin, Özge & Czado, Claudia & Paterlini, Sandra, 2023. "ESG, risk, and (tail) dependence," International Review of Financial Analysis, Elsevier, vol. 87(C).
    5. Sahab Zandi & Kamesh Korangi & Mar'ia 'Oskarsd'ottir & Christophe Mues & Cristi'an Bravo, 2024. "Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction," Papers 2402.00299, arXiv.org.
    6. Duc Thi Luu, 2022. "Portfolio Correlations in the Bank-Firm Credit Market of Japan," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 529-569, August.
    7. Óskarsdóttir, María & Bravo, Cristián, 2021. "Multilayer network analysis for improved credit risk prediction," Omega, Elsevier, vol. 105(C).

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