Accurate and efficient numerical calculation of stable densities via optimized quadrature and asymptotics

TL;DR

This paper introduces optimized quadrature and asymptotic methods for accurately and efficiently computing stable distribution densities, including cases lacking previous efficient algorithms.

Contribution

It presents novel numerical schemes combining quadrature and asymptotics for stable densities, covering previously challenging parameter regimes.

Findings

Significantly improved computational efficiency for stable density evaluation.

Effective methods for parameter regimes with no prior efficient algorithms.

Numerical examples demonstrating high accuracy and speed.

Abstract

Stable distributions are an important class of infinitely-divisible probability distributions, of which two special cases are the Cauchy distribution and the normal distribution. Aside from a few special cases, the density function for stable distributions has no known analytic form, and is expressible only through the variate's characteristic function or other integral forms. In this paper we present numerical schemes for evaluating the density function for stable distributions, its gradient, and distribution function in various parameter regimes of interest, some of which had no pre-existing efficient method for their computation. The novel evaluation schemes consist of optimized generalized Gaussian quadrature rules for integral representations of the density function, complemented by various asymptotic expansions near various values of the shape and argument parameters. We report several numerical examples illustrating the efficiency of our methods. The resulting code has been made available online.