Drift estimation for rough processes under small noise asymptotic: trajectory fitting method

Abstract: We consider a process $X^\varepsilon$ solution of a stochastic Volterra equation with an unknown parameter $\theta^\star$ in the drift function. The Volterra kernel is singular and given by $K(u)=c u^{\alpha-1/2} \mathbb{1}{u>0}$ with $\alpha \in (0,1/2)$. It is assumed that the diffusion coefficient is proportional to $\varepsilon \to 0$. From an observation of the path $(X^\varepsilon_s){s\in[0,T]}$, we construct a Trajectory Fitting Estimator, which is shown to be consistent and asymptotically normal. We also specify identifiability conditions insuring the $L^p$ convergence of the estimator.