Option Pricing in Some Non-Lévy Jump Models

Abstract

This paper considers pricing European options in a large class of one-dimensional Markovian jump processes known as subordinate diffusions, which are obtained by time changing a diffusion process with an independent Lévy or additive random clock. These jump processes are non-Lévy in general, and they can be viewed as a natural generalization of many popular Lévy processes used in finance. Subordinate diffusions offer richer jump behavior than Lévy processes and they have found a variety of applications in financial modeling. The pricing problem for these processes presents unique challenges, as existing numerical PIDE schemes fail to be efficient and the applicability of transform methods to many subordinate diffusions is unclear. We develop a novel method based on a finite difference approximation of spatial derivatives and matrix eigendecomposition, and it can deal with diffusions that exhibit various types of boundary behavior. Since financial payoffs are typically not smooth, we apply a smoothing technique and use extrapolation to speed up convergence. We provide convergence and error analysis and perform various numerical experiments to show that the proposed method is fast and accurate. Extension to pricing path-dependent options will be investigated in a followup paper.