Parameters estimation of a Threshold Chan-Karolyi-Longstaff-Sanders process from continuous and discrete observations
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912325776179200 |
|---|---|
| author | Mazzonetto, Sara Nieto, Benoît |
| author_facet | Mazzonetto, Sara Nieto, Benoît |
| contents | We consider a continuous time process that is self-exciting and ergodic, called threshold Chan-Karolyi-Longstaff-Sanders (CKLS) process. This process is a generalization of various models in econometrics, such as Vasicek model, Cox-Ingersoll-Ross, and Black-Scholes, 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 considering (multiple) thresholds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10022 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parameters estimation of a Threshold Chan-Karolyi-Longstaff-Sanders process from continuous and discrete observations Mazzonetto, Sara Nieto, Benoît Statistics Theory Probability primary: 62F12, secondary: 62F03, 62M05 We consider a continuous time process that is self-exciting and ergodic, called threshold Chan-Karolyi-Longstaff-Sanders (CKLS) process. This process is a generalization of various models in econometrics, such as Vasicek model, Cox-Ingersoll-Ross, and Black-Scholes, 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 considering (multiple) thresholds. |
| title | Parameters estimation of a Threshold Chan-Karolyi-Longstaff-Sanders process from continuous and discrete observations |
| topic | Statistics Theory Probability primary: 62F12, secondary: 62F03, 62M05 |
| url | https://arxiv.org/abs/2504.10022 |