A recursive representation for decoupling time-state dependent jumps from jump-diffusion processes

TL;DR

This paper introduces a recursive method to decouple jumps from complex multivariate jump-diffusion processes with state-dependent intensities, enabling better analysis and simulation of such stochastic systems.

Contribution

A novel recursive representation that fully decouples jumps from multivariate inhomogeneous stochastic differential equations with state-dependent jump intensities, extending beyond Lévy-driven models.

Findings

Recursive representations converge exponentially fast.

Bounding functions approximate the true solution with increasing accuracy.

Numerical results support theoretical convergence and effectiveness.

Abstract

We establish a recursive representation that fully decouples jumps from a large class of multivariate inhomogeneous stochastic differential equations with jumps of general time-state dependent unbounded intensity, not of L\'evy-driven type that essentially benefits a lot from independent and stationary increments. The recursive representation, along with a few related ones, are derived by making use of a jump time of the underlying dynamics as an information relay point in passing the past on to a previous iteration step to fill in the missing information on the unobserved trajectory ahead. We prove that the proposed recursive representations are convergent exponentially fast in the limit, and can be represented in a similar form to Picard iterates under the probability measure with its jump component suppressed. On the basis of each iterate, we construct upper and lower bounding functions that are also convergent towards the true solution as the iterations proceed. We provide numerical results to justify our theoretical findings.