Short-time behavior of solutions to L\'evy-driven SDEs

TL;DR

This paper investigates the short-time behavior of solutions to Lévy-driven stochastic differential equations, establishing how the process's initial behavior mirrors that of the driving Lévy process through stochastic calculus techniques.

Contribution

It provides a novel characterization of the short-time behavior of solutions to Lévy-driven SDEs, extending known Lévy process properties to the solutions of these equations.

Findings

Almost sure limits of scaled solutions mirror Lévy process behavior.

Derived explicit LIL-type results for solutions based on Lévy process characteristics.

Established a general framework for analyzing short-time behavior of Lévy-driven SDEs.

Abstract

We consider solutions of L\'evy-driven stochastic differential equations of the form $\mathrm{d} X_t=\sigma(X_{t-})\mathrm{d} L_t$, $X_0=x$ where the function $\sigma$ is twice continuously differentiable and maximal of linear growth and the driving L\'evy process $L=(L_t)_{t\geq0}$ is either vector or matrix-valued. While the almost sure short-time behavior of L\'evy processes is well-known and can be characterized in terms of the characteristic triplet, there is no complete characterization of the behavior of the process $X$. Using methods from stochastic calculus, we derive limiting results for stochastic integrals of the from $\smash{t^{-p}\int_{0+}^t\sigma(X_{t-})\mathrm{d} L_t}$ to show that the behavior of the quantity $t^{-p}(X_t-X_0)$ for $t\downarrow0$ almost surely mirrors the behavior of $t^{-p}L_t$. Generalizing $t^p$ to a suitable function $f:[0,\infty)\rightarrow\mathbb{R}$ then yields a tool to derive explicit LIL-type results for the solution from the behavior of the driving L\'evy process.