Calculation of Value-at-Risk and Expected Shortfall under model uncertainty
This thesis studies the concept of calculation of Value-at-Risk and Ex- pected Shortfall when the choice of model is uncertain. The method used for solving the problem is chosen to be Bayesian Model Averaging, using this method will reduce the model risk by taking several models into ac- count. Monte Carlo methods are used to perform the model averaging and the calculation of the risk measurements
