model introduced in Bell (2014) by allowing both the quantity and strike price to vary. I use the 5% quantile from the … portfolio distribution to measure riskiness and compare different put options. I report a so-called ‘quantile surface’ that … shows the quantile across different combinations of quantity and strike price. I find that it is possible to maximize the …

A method to estimate an extreme quantile that requires no distributional assumptions is presented. The approach is … smoothing parameter. The bandwidth can accommodate to the given quantile level. The procedure is useful for large data sets and … improves quantile estimation compared to other methods in heavy tailed distributions. Implementation is straightforward and R …

when estimating error quantiles. In order to prevent this efficiency loss in quantile estimation we propose a quantile … estimator based on inverting an empirical likelihood weighted distribution estimator. It is found that the new quantile … estimator is uniformly more efficient than the simple empirical quantile and a quantile estimator based on normalized residuals …

linear (GPL). The level of the quantile depends on a generic asymmetry parameter which reflects the possibly distinct costs … scoring rules, incentive-compatible compensation schemes and quantile regression. In the empirical part of the paper, the …

, are less likely to achieve breakthroughs and more likely to invent particularly poor outcomes. Quantile regressions …

different extreme value copulas in the context of risk estimation. To measure the risk we use a quantile. Our results have …

Estimation of a sample quantile's variance requires estimation of the probability density at the quantile. The common … quantile spacing method involves smoothing parameter m. When m, n → ∞ , the corresponding Studentized test statistic … m that contains the Edgeworth expansion term capturing the variance of the quantile spacing. Consequently, the fixed …