Asymptotic properties of maximum likelihood estimator for the growth rate for a jump-type CIR process based on continuous time observations

Abstract : We consider a jump-type Cox--Ingersoll--Ross (CIR) process driven by a standard Wiener process and a subordinator, and we study asymptotic properties of the maximum likelihood estimator (MLE) for its growth rate. We distinguish three cases: subcritical, critical and supercritical. In the subcritical case we prove weak consistency and asymptotic normality, and, under an additional moment assumption, strong consistency as well. In the supercritical case, we prove strong consistency and mixed normal (but non-normal) asymptotic behavior, while in the critical case, weak consistency and non-standard asymptotic behavior are described. We specialize our results to so-called basic affine jump-diffusions as well. Concerning the asymptotic behavior of the MLE in the supercritical case, we derive a stochastic representation of the limiting mixed normal distribution, where the almost sure limit of an appropriately scaled jump-type supercritical CIR process comes into play. This is a new phenomenon, compared to the critical case, where a diffusion-type critical CIR process plays a role.
Type de document :
Article dans une revue
Liste complète des métadonnées

https://hal-normandie-univ.archives-ouvertes.fr/hal-02332281
Contributeur : Mohamed Ben Alaya <>
Soumis le : jeudi 24 octobre 2019 - 16:56:39
Dernière modification le : samedi 16 novembre 2019 - 01:44:53

Lien texte intégral

Identifiants

  • HAL Id : hal-02332281, version 1
  • ARXIV : 1609.05865

Citation

Matyas Barczy, Mohamed Ben Alaya, Ahmed Kebaier, Gyula Pap. Asymptotic properties of maximum likelihood estimator for the growth rate for a jump-type CIR process based on continuous time observations. Stochastic Processes and their Applications, Elsevier, 2018. ⟨hal-02332281⟩

Partager

Métriques

Consultations de la notice

5