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Boyuan Ning

Publications and source records attributed to Boyuan Ning.

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From CKLS Process to CIR-type and OU-type Processes: Using a Twice-differentiable Mapping and Generalized Girsanov's Theorem

We study a twice-differentiable transformation applied to a CKLS-type short-rate model with linear drift and power-type diffusion. The transformation yields a new process whose diffusion component has a square-root structure and whose drift becomes nonlinear. A critical reassessment of earlier studies using similar transformations reveals fundamental errors in model specification and derivations. To address this, we introduce a generalized Girsanov change of measure that adjusts the drift of the transformed process. Under the resulting equivalent measure, the dynamics reduce to the classical Cox-Ingersoll-Ross (CIR) model. Using the Yamada-Watanabe-Engelbert theorem, we establish existence, uniqueness, and positivity of solutions, and show that the combined transformation and measure change is valid only under specific parameter restrictions, including those most relevant for financial applications. The CIR representation allows us to exploit known results on stationary distributions, moments, and boundary behavior. Under an additional coefficient relationship, the process can be further linked to an Ornstein-Uhlenbeck framework, yielding explicit distributional properties under the equivalent measure. Finally, since standard martingale conditions are not applicable, we prove directly that the associated Radon-Nikodym derivative is a true martingale by invoking a recent criterion based on Feller's explosion test and boundary classification.

math.PR

Estimation of the elasticity for CKLS model from high-frequency observations

We investigate parametric estimation of the elasticity parameter in the CKLS diffusion based on high-frequency data. First, we transform the CKLS diffusion to a CIR-type one via a smooth state-space mapping and the general Girsanov change of measure. This transformation enables the applications of existing inference tools for CIR processes while ensuring possibilities of transferring the resulting limit theorems back to the original probability space. However, because Feller's condition fails, many existing high-frequency likelihood-based procedures cannot be applied directly, since their discretization schemes approximate likelihood terms involving the reciprocal of the process by Riemann sums that are no longer well-defined once the paths are allowed to hit zero. Instead, we estimate the drift coefficient of the transformed CIR-type model via a procedure based on its positive Harris recurrence, which is valid in the high-frequency regime. Exploiting the drift-elasticity relationship implied by the CKLS--CIR transformation, with the help of an initial estimation, we obtain an estimator of the CKLS elasticity from the CIR drift estimator in the transformed model. This yields a closed-form estimator of the elasticity parameter with an explicit asymptotic variance. We establish its $p$-consistency, stable convergence in law, and asymptotic normality. Finally, we show that stable convergence in law is invariant under equivalent changes of measure, thereby guaranteeing that the Gaussian limit remains invariant under the original measure.

math.ST