Parameters estimation of a threshold Chan–Karolyi–Longstaff–Sanders process from continuous and discrete observations
Year: 2025
Type: article
Abstract: We consider a continuous time process that is self‐exciting and ergodic, called the threshold Chan–Karolyi–Longstaff–Sanders (CKLS) process. This process is a generalization of various models in econometrics, such as the Vasicek model, the Cox–Ingersoll–Ross model, and the Black–Scholes model, allowing for the presence of several thresholds which determine changes in the dynamics. We study the asymptotic behavior of maximum‐likelihood and quasi‐maximum‐likelihood estimators of the drift parameters in the case of continuous time and discrete time observations. We show that for high frequency observations and infinite horizon the estimators satisfy the same asymptotic normality property as in the case of continuous time observations. We also discuss diffusion coefficient estimation. Finally, we apply our estimators to simulated and real data to motivate the consideration of (multiple) thresholds.
DOI: 10.1111/sjos.70005
Authors: Sara Mazzonetto, Benoît Nieto
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Institut Élie Cartan de Lorraine, Université de Lorraine, École Polytechnique, Centre de Mathématiques Appliquées de l'École polytechnique, Institut Polytechnique de Paris
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4413230658