Parametric inference of hidden discrete-time diffusion processes by deconvolution

TL;DR

This paper introduces a new parametric estimation method for hidden discrete-time diffusion models using contrast minimization and deconvolution, demonstrating superior efficiency and stability over classical methods.

Contribution

The paper presents a novel estimation approach that is consistent, asymptotically normal, and more computationally efficient than existing methods for nonlinear stochastic models.

Findings

The proposed estimator outperforms Maximum Likelihood in computation time.

It is more stable and requires no tuning parameters.

Numerical studies confirm its effectiveness in various models.

Abstract

We study a new parametric approach for hidden discrete-time diffusion models. This method is based on contrast minimization and deconvolution and leads to estimate a large class of stochastic models with nonlinear drift and nonlinear diffusion. It can be applied, for example, for ecological and financial state space models. After proving consistency and asymptotic normality of the estimation, leading to asymptotic confidence intervals, we provide a thorough numerical study, which compares many classical methods used in practice (Non Linear Least Square estimator, Monte Carlo Expectation Maxi-mization Likelihood estimator and Bayesian estimators) to estimate stochastic volatility model. We prove that our estimator clearly outperforms the Maximum Likelihood Estimator in term of computing time, but also most of the other methods. We also show that this contrast method is the most stable and also does not need any tuning parameter.