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Flexible dependence modeling of operational risk losses and its impact on total capital requirements

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  • Brechmann, Eike
  • Czado, Claudia
  • Paterlini, Sandra

Abstract

Operational risk data, when available, are usually scarce, heavy-tailed and possibly dependent. In this work, we introduce a model that captures such real-world characteristics and explicitly deals with heterogeneous pairwise and tail dependence of losses. By considering flexible families of copulas, we can easily move beyond modeling bivariate dependence among losses and estimate the total risk capital for the seven- and eight-dimensional distributions of event types and business lines. Using real-world data, we then evaluate the impact of realistic dependence modeling on estimating the total regulatory capital, which turns out to be up to 38% smaller than what the standard Basel approach would prescribe.

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  • Brechmann, Eike & Czado, Claudia & Paterlini, Sandra, 2014. "Flexible dependence modeling of operational risk losses and its impact on total capital requirements," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 271-285.
  • Handle: RePEc:eee:jbfina:v:40:y:2014:i:c:p:271-285
    DOI: 10.1016/j.jbankfin.2013.11.040
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    1. Rand Kwong Yew Low, 2018. "Vine copulas: modelling systemic risk and enhancing higher‐moment portfolio optimisation," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 423-463, November.
    2. David E. Allen & Michael McAleer & Abhay K. Singh, 2017. "Risk Measurement and Risk Modelling Using Applications of Vine Copulas," Sustainability, MDPI, vol. 9(10), pages 1-34, September.
    3. Eckert, Christian & Gatzert, Nadine, 2017. "Modeling operational risk incorporating reputation risk: An integrated analysis for financial firms," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 122-137.
    4. Koziol, Philipp & Schell, Carmen & Eckhardt, Meik, 2015. "Credit risk stress testing and copulas: Is the Gaussian copula better than its reputation?," Discussion Papers 46/2015, Deutsche Bundesbank.
    5. Dong-Young Lim, 2021. "A Neural Frequency-Severity Model and Its Application to Insurance Claims," Papers 2106.10770, arXiv.org, revised Feb 2024.
    6. Paolo Giudici & Emanuela Raffinetti, 2021. "Cyber risk ordering with rank-based statistical models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 469-484, September.
    7. Valérie Chavez-Demoulin & Paul Embrechts & Marius Hofert, 2016. "An Extreme Value Approach for Modeling Operational Risk Losses Depending on Covariates," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 735-776, September.
    8. Edward W. Frees & Gee Lee & Lu Yang, 2016. "Multivariate Frequency-Severity Regression Models in Insurance," Risks, MDPI, vol. 4(1), pages 1-36, February.
    9. Kley, Oliver & Klüppelberg, Claudia & Paterlini, Sandra, 2020. "Modelling extremal dependence for operational risk by a bipartite graph," Journal of Banking & Finance, Elsevier, vol. 117(C).
    10. Wanling Huang & André Varella Mollick & Khoa Huu Nguyen, 2017. "Dynamic responses and tail-dependence among commodities, the US real interest rate and the dollar," Empirical Economics, Springer, vol. 53(3), pages 959-997, November.
    11. Ramírez-Cobo, Pepa & Carrizosa, Emilio & Lillo, Rosa E., 2021. "Analysis of an aggregate loss model in a Markov renewal regime," Applied Mathematics and Computation, Elsevier, vol. 396(C).
    12. Mejdoub, Hanène & Ben Arab, Mounira, 2018. "Impact of dependence modeling of non-life insurance risks on capital requirement: D-Vine Copula approach," Research in International Business and Finance, Elsevier, vol. 45(C), pages 208-218.
    13. Lu Yang & Claudia Czado, 2022. "Two‐part D‐vine copula models for longitudinal insurance claim data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1534-1561, December.
    14. Eling, Martin & Jung, Kwangmin, 2020. "Risk aggregation in non-life insurance: Standard models vs. internal models," Insurance: Mathematics and Economics, Elsevier, vol. 95(C), pages 183-198.
    15. Targino, Rodrigo S. & Peters, Gareth W. & Shevchenko, Pavel V., 2015. "Sequential Monte Carlo Samplers for capital allocation under copula-dependent risk models," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 206-226.
    16. Eling, Martin & Jung, Kwangmin, 2018. "Copula approaches for modeling cross-sectional dependence of data breach losses," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 167-180.
    17. Lu Wei & Jianping Li & Xiaoqian Zhu, 2018. "Operational Loss Data Collection: A Literature Review," Annals of Data Science, Springer, vol. 5(3), pages 313-337, September.
    18. Xu, Chi & Zheng, Chunling & Wang, Donghua & Ji, Jingru & Wang, Nuan, 2019. "Double correlation model for operational risk: Evidence from Chinese commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 327-339.
    19. Li, Wenwei & Hommel, Ulrich & Paterlini, Sandra, 2018. "Network topology and systemic risk: Evidence from the Euro Stoxx market," Finance Research Letters, Elsevier, vol. 27(C), pages 105-112.
    20. Ajjima Jiravichai & Ruth Banomyong, 2022. "A Proposed Methodology for Literature Review on Operational Risk Management in Banks," Risks, MDPI, vol. 10(5), pages 1-18, May.
    21. Suren Pakhchanyan, 2016. "Operational Risk Management in Financial Institutions: A Literature Review," IJFS, MDPI, vol. 4(4), pages 1-21, October.
    22. Huang, Wanling & Mollick, André Varella & Nguyen, Khoa Huu, 2016. "U.S. stock markets and the role of real interest rates," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 231-242.
    23. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.
    24. Stübinger, Johannes & Mangold, Benedikt & Krauss, Christopher, 2016. "Statistical arbitrage with vine copulas," FAU Discussion Papers in Economics 11/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    25. Sukcharoen, Kunlapath & Leatham, David J., 2017. "Hedging downside risk of oil refineries: A vine copula approach," Energy Economics, Elsevier, vol. 66(C), pages 493-507.

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    More about this item

    Keywords

    Operational risk; Risk capital; Dependence modeling; Zero inflation; Student’s t copula; Vine copula;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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