Parameter estimation for Chan-Karoli-Longstaff-Saunders model driven by small Lévy noises from discrete observations

2020 ◽  
Vol 10 (4) ◽  
pp. 373
Author(s):  
Chao Wei
Keyword(s):  
Parameter Estimation ◽  
Discrete Observations ◽  
Model Driven

Parameter estimation for reflected Ornstein–Uhlenbeck processes with discrete observations

2014 ◽  
Vol 18 (3) ◽  
pp. 279-291 ◽  
Author(s):  
Yaozhong Hu ◽  
Chihoon Lee ◽  
Myung Hee Lee ◽  
Jian Song
Keyword(s):  
Parameter Estimation ◽  
Discrete Observations

scholarly journals Least Squares Estimator for Vasicek Model Driven by Fractional Levy Processes

2018 ◽  
Vol 14 (2) ◽  
pp. 8013-8024
Author(s):  
Qingbo Wang ◽  
Xiuwei Yin
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Asymptotic Distribution ◽  
Lévy Processes ◽  
Levy Processes ◽  
Estimation Problem ◽  
Least Squares Estimator ◽  
Parameter Estimation Problem ◽  
Vasicek Model ◽  
Model Driven

In this paper, we consider parameter estimation problem for Vasicek model driven by fractional lévy processes defined We construct least squares estimator for drift parameters based on time?continuous observations, the consistency and asymptotic distribution of these estimators are studied in the non?ergodic case. In contrast to the fractional Vasicek model, it can be regarded as a Lévy generalization of fractional Vasicek model.

scholarly journals Estimation for the Discretely Observed Cox–Ingersoll–Ross Model Driven by Small Symmetrical Stable Noises

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 327 ◽  
Author(s):  
Chao Wei
Keyword(s):  
Least Squares ◽  
Rate Of Convergence ◽  
Explicit Formula ◽  
Asymptotic Distribution ◽  
Least Squares Estimation ◽  
Discrete Observations ◽  
Cir Model ◽  
Contrast Function ◽  
Model Driven ◽  
Numerical Calculus

This paper is concerned with the least squares estimation of drift parameters for the Cox–Ingersoll–Ross (CIR) model driven by small symmetrical α-stable noises from discrete observations. The contrast function is introduced to obtain the explicit formula of the estimators and the error of estimation is given. The consistency and the rate of convergence of the estimators are proved. The asymptotic distribution of the estimators is studied as well. Finally, some numerical calculus examples and simulations are given.

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