Nonparametric estimation of linear multiplier in SDEs driven by general Gaussian processes

TL;DR

This paper develops a kernel-based nonparametric estimator for the linear multiplier in SDEs driven by general Gaussian processes, analyzing its asymptotic properties.

Contribution

It introduces a new nonparametric estimation method for linear multipliers in Gaussian-driven SDEs and studies its asymptotic behavior.

Findings

Estimator is consistent under certain conditions

Asymptotic normality established

Applicable to a wide class of Gaussian processes

Abstract

We investigate the asymptotic properties of a kernel-type nonparametric estimator of the linear multiplier in models governed by a stochastic differential equation driven by a general Gaussian process.