Saddlepoint-adjusted inversion of characteristic functions

TL;DR

This paper introduces a novel numerical method for accurately retrieving probability density functions from characteristic functions, especially useful for models with intractable densities, ensuring stability and precision in tail evaluations.

Contribution

The paper presents a general, stable, and highly accurate numerical approach for inverting characteristic functions to obtain probability densities, outperforming traditional saddlepoint methods.

Findings

Method accurately evaluates tail probabilities

Demonstrated on jump diffusion financial models

Achieves high stability and precision in likelihood optimization

Abstract

For certain types of statistical models, the characteristic function (Fourier transform) is available in closed form, whereas the probability density function has an intractable form, typically as an infinite sum of probability weighted densities. Important such examples include solutions of stochastic differential equations with jumps, the Tweedie model, and Poisson mixture models. We propose a novel and general numerical method for retrieving the probability density function from the characteristic function, conditioned on the existence of the moment generating function. Unlike methods based on direct application of quadrature to the inverse Fourier transform, the proposed method allows accurate evaluation of the log-probability density function arbitrarily far out in the tail. Moreover, unlike ordinary saddlepoint approximations, the proposed methodology is in principle exact modulus discretization and truncation error of quadrature applied to inversion in a high-density region. Owing to these properties, the proposed method is computationally stable and very accurate under log-likelihood optimisation. The method is illustrated for a normal variance-mean mixture, and in an application of maximum likelihood estimation to a jump diffusion model for financial data.