Nonparametric Estimation of Range Value at Risk

Range value at risk (RVaR) is a quantile-based measure with two parameters. As special examples, the (VaR) and expected shortfall (ES), well-known but competing regulatory measures, are both members of RVaR family. The estimation critical issue in financial sector. Several nonparametric estimators described here. We examine these estimators’ accuracy various scenarios using Monte Carlo simulations. Our simulations shed light on how changing p q respect to n affects effectiveness that nonparametric, representing total number samples. Finally, we perform backtesting exercise based Acerbi Szekely’s test.