Statistical Surveillance of the Parameters of a One-Factor Cox–Ingersoll–Ross Model

Abstract

In the paper, the tools of statistical process control, and in particular control charts, are applied to sequentially detect a change in the parameters of the Cox–Ingersoll–Ross term-structure model. Different types of multivariate EWMA control charts are introduced and constructed, taking into account the peculiarities of the Cox– Ingersoll–Ross model. The effectiveness of the control charts is analysed on the basis of extensive Monte Carlo simulations for a parameter constellation obtained from a data set on the term-structure in USA. In addition, the paper includes a practical example of a detection of a change in an initially estimated Cox–Ingersoll–Ross model.