Computing Estimators of a Quantile and Conditional Value-at-Risk

We examine various sorting and selection methods for computing quantile and the conditional value-at-risk, two of the most commonly used risk measures in risk management scenarios. We study the situation where simulation data is already pre-generated, and perform timing experiments on calculating risk measures on the existing datasets. Through numerical experiments, approximate analyses, and existing theoretical results, we find that selection generally outperforms sorting, but which selection strategy runs fastest depends on several factors.