Noise Inference For Ergodic L\'evy Driven SDE

TL;DR

This paper develops methods for inferring the characteristics of Le9vy noise in ergodic SDEs from high-frequency data, introducing new inference procedures and simulation tools in YUIMA.

Contribution

It introduces a stochastic expansion for residual functionals, enabling inference on Le9vy noise and presents new simulation and estimation methods in YUIMA for Le9vy-driven SDEs.

Findings

Derived a stochastic expansion for residuals in Le9vy SDEs.

Developed new inference procedures for noise characteristics.

Implemented new simulation and estimation tools in YUIMA.

Abstract

We study inference for the driving L\'evy noise of an ergodic stochastic differential equation (SDE) model, when the process is observed at high-frequency and long time and when the drift and scale coefficients contain finite-dimensional unknown parameters. By making use of the Gaussian quasi-likelihood function for the coefficients, we derive a stochastic expansion for functionals of the unit-time residuals, which clarifies some quantitative effect of plugging-in the estimators of the coefficients, thereby enabling us to take several inference procedures for the driving-noise characteristics into account. We also present new classes and methods available in YUIMA for the simulation and the estimation of a L\'evy SDE model. We highlight the flexibility of these new advances in YUIMA using simulated and real data.