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Jump-diffusion based-simulated expected shortfall (SES) method of correcting value-at-risk (VaR) under-prediction tendencies in stressed economic climateMagagula, Sibusiso Vusi 05 1900 (has links)
Value-at-Risk (VaR) model fails to predict financial risk accurately especially during financial crises. This is mainly due to the model’s inability to calibrate new market information and the fact that the risk measure is characterised by poor tail risk quantification. An alternative
approach which comprises of the Expected Shortfall measure and the Lognormal Jump-Diffusion (LJD) model has been developed to address the aforementioned shortcomings of VaR. This model is called the Simulated-Expected-Shortfall (SES) model. The Maximum Likelihood Estimation (MLE) approach is used in determining the parameters of the LJD model since it’s more reliable and authenticable when compared to other nonconventional parameters estimation approaches mentioned in other literature studies. These parameters are then plugged into the LJD model, which is simulated multiple times in generating the new loss dataset used in the developed model. This SES model is statistically
conservative when compared to peers which means it’s more reliable in predicting financial risk especially during a financial crisis. / Statistics / M.Sc. (Statistics)
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Numerical simulation of rarefied gas flows based on the kinetic approach / Simulation numérique de l'écoulement de gaz raréfiés sur la base des équations cinétiques modèlesPolikarpov, Alexey 27 October 2011 (has links)
Ce travail de thèse porte sur le développement de la méthode des vitesses discrètes pour la résolution numérique de équations cinétiques modèles, BGK, S modèle et ES modèle, qui représentent les différentes approximations de l’équation de Boltzmann. / This work is devoted to the development of the numerical resolution of the kinetic model equations such as BGK, S-model, ES-model by the discrete velocity method. The different approximations of the Boltzmann equation are presented
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Jump-diffusion based-simulated expected shortfall (SES) method of correcting value-at-risk (VaR) under-prediction tendencies in stressed economic climateMagagula, Sibusiso Vusi 05 1900 (has links)
Value-at-Risk (VaR) model fails to predict financial risk accurately especially during financial crises. This is mainly due to the model’s inability to calibrate new market information and the fact that the risk measure is characterised by poor tail risk quantification. An alternative
approach which comprises of the Expected Shortfall measure and the Lognormal Jump-Diffusion (LJD) model has been developed to address the aforementioned shortcomings of VaR. This model is called the Simulated-Expected-Shortfall (SES) model. The Maximum Likelihood Estimation (MLE) approach is used in determining the parameters of the LJD model since it’s more reliable and authenticable when compared to other nonconventional parameters estimation approaches mentioned in other literature studies. These parameters are then plugged into the LJD model, which is simulated multiple times in generating the new loss dataset used in the developed model. This SES model is statistically
conservative when compared to peers which means it’s more reliable in predicting financial risk especially during a financial crisis. / Statistics / M.Sc. (Statistics)
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