Parameter estimation for a subcritical affine two factor model

TL;DR

This paper investigates the asymptotic behavior of maximum likelihood and least squares estimators for parameters in a subcritical affine two-factor model, establishing their consistency and normality based on continuous observations.

Contribution

It provides the first rigorous proof of strong consistency and asymptotic normality for these estimators in the subcritical affine two-factor model.

Findings

Establishes strong consistency of estimators

Proves asymptotic normality of estimators

Applicable to continuous time observations in ergodic case

Abstract

For an affine two factor model, we study the asymptotic properties of the maximum likelihood and least squares estimators of some appearing parameters in the so-called subcritical (ergodic) case based on continuous time observations. We prove strong consistency and asymptotic normality of the estimators in question.