El uso de la distribución g-h en riesgo operativo11El autor agradece las valiosas sugerencias y recomendaciones de los evaluadores anónimos y los comentarios del editor de la revista. Estas contribuciones ayudaron significativamente a mejorar la versión previa de este artículo.

Contaduria y Administracion - Tập 59 - Trang 123-148 - 2014
Andrés Mora Valencia1
1Colegio de Estudios Superiores de Administración

Tài liệu tham khảo

Artzner, 1999, Coherent measures of risk, Mathematical Finance, 203, 10.1111/1467-9965.00068 Beirlant, 2004 Buch-Kroman, 2009, Comparison of tail performance of the Champernowne transformed kernel density estimator, the generalized Pareto distribution and the g-and-h distribution, Astin Bulletin, 139 Carrillo-Menéndez, 2012, Robust quantification of the exposure to operational risk: Bringing economic sense to economic capital, Computers & Operations Research, 39, 792, 10.1016/j.cor.2010.10.001 Chaudhury, 2010, A review of the key issues in operational risk capital modeling, The Journal of Operational Risk, 5, 37, 10.21314/JOP.2010.078 Chávez-Demoulin, 1999 Chávez-Demoulin, 2004, Smooth extremal models in finance and insurance, The Journal of Risk and Insurance, 71, 183, 10.1111/j.0022-4367.2004.00085.x Degen, 2007, The quantitative modeling of operational risk: between g-and-h and EVT, Astin Bulletin, 37, 265, 10.2143/AST.37.2.2024067 Degen, 2008, EVT-based estimation of risk capital and convergence of high quantiles, Advanced in Applied Probability, 40, 696, 10.1239/aap/1222868182 Dell'Aquila, 2006, Extremes and robustness: a contradiction?, Financial Markets Portfolio Management, 103, 10.1007/s11408-006-0002-x Dutta, 2002 Dutta, 2006 Fox, 2002 Haynes, 2005, Bayesian estimation of g-and-k distributions using MCMC, Comput. Stat, 20, 7, 10.1007/BF02736120 Hoaglin, 1985, Summarizing Shape Numerically: The g-and-h Distributions Horbenko, 2011, Robust estimation of operational risk, Journal of Operational Risk, 6, 3, 10.21314/JOP.2011.090 Jiménez, 2006, Una estimación del parameter de la distribution g de Tukey, Revista Colombiana de Estadística, 29, 1 Jiménez, 2011, Using Tukey's g and h family of distributions to calculate value-and-risk and conditional value-and-risk, Journal of Risk, 13, 95, 10.21314/JOR.2011.230 Jobst, 2007, Operational Risk-The Sting is Still in the Tail But the Poison Depends on the Dose, Journal of Operational Risk, 2, 3, 10.21314/JOP.2007.026 Lou, 2009, Computing tails of compound distributions using direct numerical integration, Journal of Computational Finance, 13, 73, 10.21314/JCF.2009.193 Martínez, 1981 Martínez, 1984, Some properties of the Tukey g and h family of distributions, Communications in Statistics: Theory and Methods, 13, 353, 10.1080/03610928408828687 McNeil, 2005 Nešlehová, 2006, Infinite-mean models and the LDA for operational risk, Journal of Operational Risk, 1, 3, 10.21314/JOP.2006.001 Peters, 2006, Bayesian inference, Monte Carlo sampling and operational risk, Journal of Operational Risk, 1, 27, 10.21314/JOP.2006.014 Rayner, 2002, Numerical maximum likelihood estimation for the g-and-k and generalized g-and-h distributions, Statistics and Computing, 57, 10.1023/A:1013120305780 Shevchenko, 2010, Calculation of aggregate loss distributions, The Journal of Operational Risk, 5, 3, 10.21314/JOP.2010.077 Smith, 1987, Estimating tails of probability distributions, Annals of Statistics, 1174, 10.1214/aos/1176350499 Temnov, 2008, A comparison of loss aggregation methods for operational risk, Journal of Operational Risk, 3, 3, 10.21314/JOP.2008.040 Vaz de Melo Mendes, 2006, A Bayesian analysis of clusters of extreme losses, Appl. Stochastic Models Bus. Ind., 155, 10.1002/asmb.625 Venegas, 2006