Download or read book Estimaci n de M tricas de Riesgo de Mercado usando Mixturas Gaussianas Estimating Market Risk Metrics Using Gaussian Mixtures written by Jorge Rosales Contreras and published by . This book was released on 2015 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spanish Abstract: Los principales modelos financieros para la estimación del riesgo de mercado suponen que los rendimientosde los activos siguen una distribución Normal o se basan en la distribución empírica. Con frecuencia, elsupuesto de normalidad se da por hecho; sin embargo, resulta poco realista debido a las características deexceso de curtosis y de asimetría observadas en el comportamiento real de los retornos. En este trabajopresentamos evidencia de que las mixturas gaussianas finitas constituyen un medio eficiente para modelar ladistribución de los rendimientos de los activos financieros. Estudiamos el modelo y derivamos expresionespara las métricas usuales de riesgo de mercado. Ilustramos su aplicación calculando métricas de riesgopara una cartera de activos del mercado mexicano, con el modelo propuesto y comparándolas con modelosampliamente usados en el mercado.

Download or read book Finite Gaussian Mixtures and Market Risk Assessment written by Jorge Rosales Contreras and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "El Valor en Riesgo (VaR) es la métrica de riesgo de mercado más popular, al resumir en una cifra la exposición a diversos factores de riesgo. El Déficit Esperado (ES) corrige sus limitaciones y ha ganado terreno. Ambas medidas se pueden estimar bajo distintos modelos."--Resumen h. [iii].

Download or read book Market Risk Measures Using Finite Gaussian Mixtures written by Jorge Rosales Contreras and published by . This book was released on 2015 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Value at Risk (VaR) is the most popular market risk measure as it summarizes in one figure the exposure to different risk factors. It had been around for over a decade when Expected Shortfall (ES) emerged to correct its shortcomings. Both risk measures can be estimated under several models. We explore the application of a parametric model to fit the joint distribution of risk factor returns based on multivariate finite Gaussian Mixtures, derive a closed-form expression for ES under this model and estimate risk measures for a multi-asset portfolio over an extended period. We then compare results versus benchmark models (Historical Simulation and Normal) through back-testing all of them at several confidence levels. Evidence shows that the proposed model is a competitive one for the estimation of VaR and ES.

Download or read book Modeling and Predicting Market Risk with Laplace Gaussian Mixture Distributions written by Markus Haas and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: