Self-Normalized Quantile Empirical Saddlepoint Approximation

TL;DR

The paper introduces SNQESA, a density-free, highly accurate, and computationally efficient method for quantile inference that avoids kernel and resampling issues, suitable for skewed and heavy-tailed distributions.

Contribution

It develops a novel self-normalized saddlepoint approximation for quantiles, providing higher-order tail accuracy and reliable inference without density estimation or bandwidth selection.

Findings

Achieves stable coverage and competitive interval lengths in simulations.

Significantly faster than resampling methods in Monte Carlo experiments.

Extends naturally to two-sample and regression quantile inference.

Abstract

We propose a density-free method for frequentist inference on population quantiles, termed Self-Normalized Quantile Empirical Saddlepoint Approximation (SNQESA). The approach builds a self-normalized pivot from the indicator score for a fixed quantile threshold and then employs a constrained empirical saddlepoint approximation to obtain highly accurate tail probabilities. Inverting these tail areas yields confidence intervals and tests without estimating the unknown density at the target quantile, thereby eliminating bandwidth selection and the boundary issues that affect kernel-based Wald/Hall-Sheather intervals. Under mild local regularity, the resulting procedures attain higher-order tail accuracy and second-order coverage after inversion. Because the pivot is anchored in a bounded Bernoulli reduction, the method remains reliable for skewed and heavy-tailed distributions and for extreme quantiles. Extensive Monte Carlo experiments across light, heavy, and multimodal distributions demonstrate that SNQESA delivers stable coverage and competitive interval lengths in small to moderate samples while being orders of magnitude faster than large-B resampling schemes. An empirical study on Value-at-Risk with rolling windows further highlights the gains in tail performance and computational efficiency. The framework naturally extends to two-sample quantile differences and to regression-type settings, offering a practical, analytically transparent alternative to kernel, bootstrap, and empirical-likelihood methods for distribution-free quantile inference.