Estimating Quantile Sensitivity for Financial Models with Correlations and Jumps

Abstract

We apply a generalized likelihood ratio (GLR) derivative estimation method in previous works to estimate quantile sensitivity of financial models with correlations and jumps. Examples illustrate the wide applicability of the GLR method by providing several practical settings where other techniques are difficult to apply, and numerical results demonstrate the effectiveness of the new estimator.