Asymptotic properties of drift parameter estimators in CKLS diffusion models

Abstract: The Chan–Karolyi–Longstaff–Sanders (CKLS) model is a flexible family of diffusion models widely used in financial mathematics, particularly for modelling interest rates and stochastic volatility. It includes several classical models, such as the Cox–Ingersoll–Ross model, as special cases.

In this talk, we discuss the estimation of drift parameters in CKLS diffusion models based on continuous-time observations. We focus on the construction of explicit estimators using the method of moments and investigate their asymptotic properties. In particular, we address strong consistency, asymptotic normality, existence of moments, and convergence in L^p under suitable assumptions. We also discuss the role of the model parameters in the asymptotic behavior of the estimators and illustrate the theoretical results with numerical simulations.

probabilitystatistics theorydata analysis, statistics and probability

Audience: researchers in the topic

( chat )

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Asymptotic Methods in Statistics

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