Statistical inference for time-changed L\'{e}vy processes via composite characteristic function estimation

TL;DR

This paper develops a semi-parametric estimation method for the parameters of multidimensional time-changed Lévy processes using composite characteristic function estimation, with proven optimal convergence rates and simulation validation.

Contribution

It introduces a consistent estimation approach for Lévy densities in time-changed Lévy processes, establishing optimal convergence rates and addressing the related composite function estimation problem.

Findings

Proposed a consistent estimator for Lévy density.

Derived optimal convergence rates for the estimator.

Validated the method with simulations on NIG Lévy processes.

Abstract

In this article, the problem of semi-parametric inference on the parameters of a multidimensional L\'{e}vy process $L_t$ with independent components based on the low-frequency observations of the corresponding time-changed L\'{e}vy process $L_{\mathcal{T}(t)}$, where $\mathcal{T}$ is a nonnegative, nondecreasing real-valued process independent of $L_t$, is studied. We show that this problem is closely related to the problem of composite function estimation that has recently gotten much attention in statistical literature. Under suitable identifiability conditions, we propose a consistent estimate for the L\'{e}vy density of $L_t$ and derive the uniform as well as the pointwise convergence rates of the estimate proposed. Moreover, we prove that the rates obtained are optimal in a minimax sense over suitable classes of time-changed L\'{e}vy models. Finally, we present a simulation study showing the performance of our estimation algorithm in the case of time-changed Normal Inverse Gaussian (NIG) L\'{e}vy processes.