# Statistical inference for moving-average L\'evy-driven processes: Fourier-based approach

## TL;DR

This paper introduces a Fourier-based semiparametric estimation method for moving-average Lévy-driven processes, establishing optimal convergence rates and advancing statistical inference in continuous-time stochastic models.

## Contribution

It presents a novel Fourier-based estimation approach for Lévy-driven processes with proven optimal convergence rates.

## Key findings

- Estimation method achieves minimax optimal convergence rates.
- Method effectively handles continuous-time moving-average Lévy processes.
- Provides theoretical guarantees for the proposed estimators.

## Abstract

We consider a new method of the semiparametric statistical estimation for the continuous-time moving average L\'evy processes. We derive the convergence rates of the proposed estimators, and show that these rates are optimal in the minimax sense.