The Quantification of Precision in Market Risk Regulation

Expected Shortfall (ES) has been proposed to replace Value at Risk (VaR) as the new regulatory market risk measures ES is theoretically superior to VaR, however its estimates entail more estimation error. Impetus of critical policy analysis regarding the quantification of relative estimation error in risk measures and robust estimation methods needs to be developed in attempt to alleviate the practical weakness of ES. In order to facilitate this objective, this research proposes a Bootstrap method that is computationally efficient in constructing prediction intervals and provides a robust variant of Filter Historical Simulations.