Point process simulation of generalised inverse Gaussian processes and estimation of the Jaeger integral

TL;DR

This paper introduces novel indirect simulation methods for generalized inverse Gaussian (GIG) Lévy processes, enabling better approximation and bounds for intractable integrals like the Jaeger integral, with applications in finance and engineering.

Contribution

It presents the first shot-noise based simulation approach for GIG processes, including a thinning method and bounds for the Jaeger integral, extending to generalized hyperbolic processes.

Findings

The shot-noise method converges rapidly in most cases.

The approach provides bounds on the Jaeger integral.

Simulation extends to generalized hyperbolic processes.

Abstract

In this paper novel simulation methods are provided for the generalised inverse Gaussian (GIG) L\'{e}vy process. Such processes are intractable for simulation except in certain special edge cases, since the L\'{e}vy density associated with the GIG process is expressed as an integral involving certain Bessel Functions, known as the Jaeger integral in diffusive transport applications. We here show for the first time how to solve the problem indirectly, using generalised shot-noise methods to simulate the underlying point processes and constructing an auxiliary variables approach that avoids any direct calculation of the integrals involved. The resulting augmented bivariate process is still intractable and so we propose a novel thinning method based on upper bounds on the intractable integrand. Moreover our approach leads to lower and upper bounds on the Jaeger integral itself, which may be compared with other approximation methods. The shot noise method involves a truncated infinite series of decreasing random variables, and as such is approximate, although the series are found to be rapidly convergent in most cases. We note that the GIG process is the required Brownian motion subordinator for the generalised hyperbolic (GH) L\'{e}vy process and so our simulation approach will straightforwardly extend also to the simulation of these intractable proceses. Our new methods will find application in forward simulation of processes of GIG and GH type, in financial and engineering data, for example, as well as inference for states and parameters of stochastic processes driven by GIG and GH L\'{e}vy processes.