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dc.contributor.advisorMacías Villalba, Gloria Inés
dc.contributor.authorJaramillo Blanco, Claudia Marcela
dc.coverage.spatialSan Gil (Santander, Colombiaspa
dc.coverage.temporal2014spa
dc.date.accessioned2021-10-11T13:18:23Z
dc.date.available2021-10-11T13:18:23Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/20.500.12749/14608
dc.description.abstractMientras que el acuerdo de Basilea II ha sido aplicado en la mayor parte del mundo, siguen existiendo muchas discrepancias aun en las técnicas avanzadas de modelos de riesgos operacionales que se usan en grandes bancos internacionales. Una de las familias de modelos de distribución de perdidas agregadas, uno de ellos es el LDA, que se enfoca en observar los eventos pasados de las perdidas externas y otro en las técnicas basadas en escenarios que usan opiniones subjetivas de expertos como punto de inicio para determinar el requerimiento de capital regulatorio que se usa para cubrir los riesgos operacionales. El mayor reto metodológico es combinar las dos técnicas de tal manera que cumplan los requerimientos de Basilea II. En este capítulo discutiremos e investigaremos el uso de varias alternativas para modelar una opinión experta que suene de una manera estadística tanto como se permita para posteriormente integrarlo con distribución de perdidas, equipado con datos internos y/o externos, un ejemplo numérico, soporte el análisis y muestre que existen soluciones para difundir la información que surja de ambas fuentes.spa
dc.description.sponsorshipFundación Universitaria San Gil UNISANGILspa
dc.description.tableofcontentsCAPITULO 1 MODELO DE RIESGO OPERACIONAL BASADO EN LA OPINION DE MULTIPLES EXPERTOS ABSTRACTO INTRODUCCION 1.2 PANORAMA GENERAL DE LOS MODELOS AMA • 1.2.1 Enfoque de Distribución de Pérdidas • 1.2.2 AMA Basados en Escenarios • 1.2.3 Integración de LDA y sbAMA 1.3 USANDO OPINIONES DE EXPERTOS PARA MODELAR RIESGO OPERACIONAL: UN CASO PRACTICO DE NEGOCIO 1.4 COMBINACIÓN DE LAS OPINIONES DE LOS EXPERTOS 1.5 MODELO SUPRA-BAYESIANO PARA MODELAJE DE RIESGO OPERACIONAL • 1.5.1 Modelo • 1.5.2 Ilustración del Modelo 1.6 CONCLUSIONES CAPITULO 2 CONSISTENTE MEDICIÓN CUANTITATIVA DEL RIESGO OPERACIONAL ABSTRACTO 2.1 INTRODUCCION 2.2 PRÁCTICAS ACTUALES DE MEDICIÓN DEL RIESGO OPERACIONAL Y ENFOQUES REGULATORIOS. • RECUADRO 2.1 GESTIÓN DEL RIESGO OPERACIONAL (ORM). • RECUADRO 2.2 EVOLUCIÓN DEL MARCO SUFICIENCIA DE CAPITAL POR RIESGO OPERACIONAL AVANZADO. 2.3 RETOS PRINCIPALES DE MEDICIÓN LDA • 2.3.1 Deficiencias de la cuantificación de las Metodologías para las estimaciones de LDA • Efecto del momento de pérdida • EVT y GHD: El enfoque más común para la revisión de LDA • Riesgo Operacional como un proceso dinámico y el papel de las superposiciones Cualitativas • 2.3.2 Deficiencias de LDA que presenta la recogida de datos: Sistemas ORM y Características de los datos • Fuentes y agrupamiento de datos internos y la pérdida de datos externos • Efectos de perdidas frecuentes • Efectos de la frecuencia en la pérdidas esperadas • Efecto de la frecuencia sobre la pérdida de la pérdida inesperada • RECUADRO 2.3 INCONSISTENCIAS DE CAPITAL REGULATORIO DEL NUEVO ACUERDO DE BASILEA • Ajuste de Capital de Riesgo Operacional Estimada bajo AMA • Inicio para presentan el reconocimiento el bajo AMA CAPITULO 5 IDENTIFICAR Y MITIGAR LOS RIESGOS PERCIBIDOS EN LA CADENA DE SERVICIOS DEL BANCO: UN NUEVO ESFUERZO DE FORMALIZACIÓN PARA ABORDAR LA NATURALEZA HETEROGÉNEA DE SERVICIOS BASADOS EN CONOCIMIENTO DE INTANGIBLES. • ABSTRACTO • 5.1 INTRODUCCIÓN • 5.2 BANCOS EN LA ERA POST-SUBPRIME: UN SECTOR IMPORTANTE EN LA CRISIS • 5.3 CONCEPTO DE RIESGO PERCIBIDO: REVISIÓN DE LA LITERATURA • 5.4 SERVICIO DE CADENA DEL BANCO: CADENA DE LOS SERVICIOS Y EVENTOS DE RIESGO • 5.4.1 Proceso de compra del consumidor: diseñando estrategias de inversión • 5.4.2 Montaje de Vehículos de Inversión: La elección Los intermediarios • 5.4.3 Gestión de la incertidumbre • 5.5 SISTEMA DE CONTROL DISEÑADA PARA HACER FRENTE A LA NATURALEZA INTANGIBLE DE RIESGOS DE SERVICIO • 5.6 APLICACIÓN DEL MODELO TEID: EL CASO SOCGEN • 5.7 CONCLUSIONES PARTE 2 CAPITULO 8 IMPORTANTES TÉCNICAS DE MUESTREO PARA LA ESTIMACIÓN DEL GRAN CUANTIL EN EL MÉTODO DE MEDICIÓN AVANZADA. • ABSTRACTO • 8.1 INTRODUCCIÓN • 8.2 PRELIMINARES: POISSON MEZCLAS Y MUESTREO IMPORTANCIA • 8.2.1 Las distribuciones de la pérdida en el Método de Medición Avanzada • 8.2.2 Importancia de muestreo y Cruce entrópico • 8.2.3 Distribuciones de cola pesada para muestreo de importancia • 8.2.4 La elección de la densidad Instrumental • 8.3 CASO DE COLA MODERADAMENTE PESADA: REGISTRO NORMAL DE GRAVEDAD • 8.3.1 Enfoque Mezcla Defensivo • 8.3.2 Enfoque Estándar del cruce entrópico • 8.4 CASO DE COLA PESADA: SEVERIDAD DE PARETO • 8.4.1 Enfoque de defensa mixta • 8.5 RESULTADOS DE LA SIMULACIÓN • 8.5.1 Iniciar normales Severidad • 8.5.2 Severidad de Pareto • 8.6 CONCLUSIONES CAPITULO 10 MODELOS MULTIVARIANTES PARA RIESGO OPERACIONAL: UN ENFOQUE USANDO LA TEORÍA DE VALOR EXTREMO Y MODELOS DE CHOQUE DE POISSON • ABSTRACTO • 10.1 INTRODUCCIÓN • 10.2 ENFOQUE STANDARD LDA • 10.2.1 Modelo de Frecuencia • 10.2.2 Modelo de Severidad • 10.2.3 Agravando por el método de monte Carlo • 10.3 AGREGACIÓN VIA CÓPULA • 10.3.1 Estimación de cópulas con discretos Distribuciones • 10.3.2 Agregación de Procedimiento canónico para el Uso de cópulas • 10.3.3 Modelo de Choque de Poisson • 10.4 ANÁLISIS EMPÍRICO • 10.5 CONCLUSIÓN PARTE 3 CAPITULO 12 ADMINISTRACIÓN Y MITIGACIÓN DEL RIESGO OPERACIONAL • ABSTRACTO • 12.1 INTRODUCCIÓN • 12.2 ALCANCE LIMITADO DE GESTIÓN DEL RIESGO OPERACIONAL BAJO BASILEA II • 12.3 PERSPECTIVA DE GESTIÓN EN LA GESTIÓN DEL RIESGO OPERACIONAL • 12.4 RENDICIÓN DE GESTIÓN DEL RIESGO OPERACIONAL OPERACIONALMENTE MANEJABLE • 12.4.1 Implementación de la administración del riesgo operativo en la vida real para el Medio Ambiente • 12.4.2 Compensación de las posibles carencias de la vida real de las Gestión de riesgo operativo. • 12.4.3 Asignación de responsabilidades para la gestión de riesgo operativo. • 12.5 RECOMENDACIONES Y PERSPECTIVAS CAPITULO 14 SEGUROS DE RIESGO OPERACIONAL COMO GENERADOR DE VALOR NETO • ABSTRACTO • 14.1 INTRODUCCIÓN • 14.2 TRATAMIENTO DE LOS CONCEPTOS DE LA CATEGORÍA DE SEGURO BAJO BASILEA II • 14.3 QUE ABARCA, EN CONCEPTOS DE SEGUROS PARA LA GESTIÓN RIESGO OPERATIVO • 14.3.1 Los avances a la aceptación de los Mercados Eficientes • 14.3.2 Explicación de los Enfoques Bajo La hipótesis de Mercados ineficientes • 14.4 RIESGO, COSTO DE CAPITAL, Y VALOR DEL ACCIONISTA • 14.4.1 Valor de empresa y los costos de capital en los mercados eficientes • 14.4.2 Modelo Crítico • 14.4.3 Derivado del precio realista del costo de capital • 14.4.4 Otras consecuencias de la ineficiencia de los mercados de capitales • 14.5 OPTIMIZACIÓN DEL COSTO TOTAL DE RIESGO • 14.5.1 Evaluación del Costo Total de Riesgo • 14.5.2 Manejo del costo total de Riesgo • 14.5.3 Optimización Costo Total de Riesgo: Un enfoque por fases • 14.6 CONCLUSIONES, RECOMENDACIONES Y PERSPECTIVAS PARA MÁS INVESTIGACIÓN • 15 CONCLUSIONES PERSONALESspa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.titleEl lenguaje del riesgo operativo aplicado a entidades bancarias y cooperativas financieras en Colombia, tomado del libro “Operational risk toward basel III: Best prácticas and issues in modeling, managment and regulation” del autor Greg N. Gregoriouspa
dc.title.translatedThe language of operational risk applied to banks and financial cooperatives in Colombia, taken from the book "Operational risk toward basel III: best practices and issues in modeling, management and regulation" by the author Greg N. Gregoriouspa
dc.degree.nameIngeniero financierospa
dc.publisher.grantorUniversidad Autónoma de Bucaramanga UNABspa
dc.rights.localAbierto (Texto Completo)spa
dc.publisher.facultyFacultad Economía y Negociosspa
dc.publisher.programPregrado Ingeniería Financieraspa
dc.description.degreelevelPregradospa
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.localTrabajo de Gradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.subject.keywordsFinancial engineeringspa
dc.subject.keywordsFinancial analysisspa
dc.subject.keywordsFinancial managenmentspa
dc.subject.keywordsInvestigationspa
dc.subject.keywordsRisk modelsspa
dc.subject.keywordsEconomyspa
dc.subject.keywordsCapital measurementspa
dc.subject.keywordsYieldsspa
dc.subject.keywordsExternal lossesspa
dc.subject.keywordsProbabilitiesspa
dc.subject.keywordsBank operationsspa
dc.subject.keywordsFinancial marketspa
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga - UNABspa
dc.identifier.reponamereponame:Repositorio Institucional UNABspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
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dc.contributor.cvlacMacías Villalba, Gloria Inés [0000290980]spa
dc.contributor.googlescholarMacías Villalba, Gloria Inés [_XmXMLUAAAAJ]spa
dc.contributor.orcidMacías Villalba, Gloria Inés [0000-0001-5897-181X]spa
dc.subject.lembAnálisis financierospa
dc.subject.lembGestión financieraspa
dc.subject.lembIngeniería financieraspa
dc.subject.lembInvestigaciónspa
dc.subject.lembProbabilidadesspa
dc.subject.lembOperaciones bancariasspa
dc.subject.lembMercado financierospa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.description.abstractenglishWhile the Basel II accord has been applied in most parts of the world, many discrepancies remain even in advanced operational risk modeling techniques used by large international banks. One of the families of aggregate loss distribution models, one of them is the LDA, which focuses on observing past events of external losses and another on scenario-based techniques that use subjective opinions of experts as a starting point for determine the regulatory capital requirement that is used to cover operational risks. The biggest methodological challenge is to combine the two techniques in such a way that they meet the requirements of Basel II. In this chapter we will discuss and investigate the use of various alternatives to model an expert opinion that sounds in a statistical way as much as it is allowed to later integrate it with distribution of losses, equipped with internal and / or external data, a numerical example, support the analysis and show that there are solutions to disseminate information from both sources.spa
dc.subject.proposalModelos de riesgosspa
dc.subject.proposalEconomíaspa
dc.subject.proposalMedición de capitalspa
dc.subject.proposalRendimientosspa
dc.subject.proposalPerdidas externasspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TP
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.coverage.campusUNAB Campus Bucaramangaspa
dc.description.learningmodalityModalidad Presencialspa


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