IDEAS home Printed from https://ideas.repec.org/r/cup/astinb/v37y2007i02p265-291_01.html
   My bibliography  Save this item

The Quantitative Modeling of Operational Risk: Between G-and-H and EVT

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Dominique Guegan & Bertrand Hassani & Cédric Naud, 2010. "An efficient threshold choice for operational risk capital computation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00544342, HAL.
  2. Pavel V. Shevchenko, 2010. "Implementing loss distribution approach for operational risk," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(3), pages 277-307, May.
  3. Ghosh, Souvik & Resnick, Sidney, 2010. "A discussion on mean excess plots," Stochastic Processes and their Applications, Elsevier, vol. 120(8), pages 1492-1517, August.
  4. Buch-Kromann, Tine & Guillén, Montserrat & Linton, Oliver & Nielsen, Jens Perch, 2011. "Multivariate density estimation using dimension reducing information and tail flattening transformations," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 99-110, January.
  5. J. D. Opdyke, 2014. "Estimating Operational Risk Capital with Greater Accuracy, Precision, and Robustness," Papers 1406.0389, arXiv.org, revised Nov 2014.
  6. Julia S. Mehlitz & Benjamin R. Auer, 2021. "Time‐varying dynamics of expected shortfall in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 895-925, June.
  7. Balkema, A.A. & Embrechts, P. & Nolde, N., 2010. "Meta densities and the shape of their sample clouds," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1738-1754, August.
  8. Udo Milkau & Jürgen Bott, 2018. "Active Management of Operational Risk in the Regimes of the “Unknown”: What Can Machine Learning or Heuristics Deliver?," Risks, MDPI, vol. 6(2), pages 1-16, April.
  9. Mao, Tiantian & Lv, Wenhua & Hu, Taizhong, 2012. "Second-order expansions of the risk concentration based on CTE," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 449-456.
  10. Francisco Venegas-Martínez & José Francisco Martínez-Sánchez & María Teresa V. Martínez-Palacios, 2016. "An analysis on operational risk in international banking: A Bayesian approach (2007–2011)," Estudios Gerenciales, Universidad Icesi, vol. 32(140), pages 208-220, September.
  11. Enrico Biffis & Erik Chavez, 2014. "Tail Risk in Commercial Property Insurance," Risks, MDPI, vol. 2(4), pages 1-18, September.
  12. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Papers 1502.00882, arXiv.org.
  13. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2017. "Risk quantification in turmoil markets," Risk Management, Palgrave Macmillan, vol. 19(3), pages 202-224, August.
  14. José Francisco Martínez-Sánchez & Francisco Venegas-Martínez, 2013. "Riesgo operacional en la banca trasnacional: un enfoque bayesiano," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(1), pages 31-72, May.
  15. Joan del castillo & Jalila Daoudi & Isabel Serra, 2012. "The full-tails gamma distribution applied to model extreme values," Papers 1211.0130, arXiv.org.
  16. M. Bee & J. Hambuckers & L. Trapin, 2019. "Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1255-1266, August.
  17. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 83-102, June.
  18. de Valk, Cees, 2016. "A large deviations approach to the statistics of extreme events," Other publications TiSEM 117b3ba0-0e40-4277-b25e-d, Tilburg University, School of Economics and Management.
  19. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.
  20. Degen, Matthias & Lambrigger, Dominik D. & Segers, Johan, 2010. "Risk concentration and diversification: Second-order properties," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 541-546, June.
  21. Daniel Hadley & Harry Joe & Natalia Nolde, 2021. "On the Selection of Loss Severity Distributions to Model Operational Risk," Papers 2107.03979, arXiv.org.
  22. Gareth W. Peters & Wilson Ye Chen & Richard H. Gerlach, 2016. "Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-Moments," Risks, MDPI, vol. 4(2), pages 1-41, May.
  23. Marco Bee & Julien Hambuckers & Luca Trapin, 2019. "An improved approach for estimating large losses in insurance analytics and operational risk using the g-and-h distribution," DEM Working Papers 2019/11, Department of Economics and Management.
  24. Xu, Ganggang & Genton, Marc G., 2015. "Efficient maximum approximated likelihood inference for Tukey’s g-and-h distribution," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 78-91.
  25. Dominik D. Lambrigger & Pavel V. Shevchenko & Mario V. Wuthrich, 2009. "The Quantification of Operational Risk using Internal Data, Relevant External Data and Expert Opinions," Papers 0904.1361, arXiv.org.
  26. Tong, Bin & Wu, Chongfeng & Xu, Weidong, 2012. "Risk concentration of aggregated dependent risks: The second-order properties," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 139-149.
  27. Marco Bee, 2022. "The truncated g-and-h distribution: estimation and application to loss modeling," Computational Statistics, Springer, vol. 37(4), pages 1771-1794, September.
  28. Sinemis Zengin & Serhat Yuksel, 2016. "A Comparison of the Views of Internal Controllers/Auditors and Branch/Call Center Personnel of the Banks for Operational Risk: A Case for Turkish Banking Sector," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 5(4), pages 10-29, July.
  29. Embrechts, Paul & Neslehová, Johanna & Wüthrich, Mario V., 2009. "Additivity properties for Value-at-Risk under Archimedean dependence and heavy-tailedness," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 164-169, April.
  30. Gareth W. Peters & Wilson Y. Chen & Richard H. Gerlach, 2016. "Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-moments," Papers 1603.01041, arXiv.org.
  31. Marco Bee & Julien Hambuckers & Flavio Santi & Luca Trapin, 2021. "Testing a parameter restriction on the boundary for the g-and-h distribution: a simulated approach," Computational Statistics, Springer, vol. 36(3), pages 2177-2200, September.
  32. Lu, Zhaoyang, 2011. "Modeling the yearly Value-at-Risk for operational risk in Chinese commercial banks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 604-616.
  33. Pavel V. Shevchenko, 2009. "Implementing Loss Distribution Approach for Operational Risk," Papers 0904.1805, arXiv.org, revised Jul 2009.
  34. Mora Valencia Andrés, 2014. "El uso de la distribución g-h en riesgo operativo," Contaduría y Administración, Accounting and Management, vol. 59(1), pages 123-148, enero-mar.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.