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Improved Estimation Strategy in Multi-Factor Vasicek Model

  • S. Ejaz Ahmed
  • Sévérien Nkurunziza
  • Shuangzhe Liu

Abstract

We consider simultaneous estimation of the drift parameters of multivari-ate Ornstein-Uhlebeck process. In this paper, we develop an improved estimation methodology for the drift parameters when homogeneity of several such parameters may hold. However, it is possible that the information regarding the equality of these parameters may not be accurate. In this context, we consider Stein-rule (or shrinkage) estimators to improve upon the performance of the classical maximum likelihood estimator (MLE). The relative dominance picture of the proposed estimators are explored and assessed under an asymptotic distributional quadratic risk criterion. For practical arguments, a simulation study is conducted which illustrates the behavior of the suggested method for small and moderate length of time observation period. More importantly, both analytical and simulation results indicate that estimators based on shrinkage principle not only give an excellent estimation accuracy but outperform the likelihood estimation uniformly.

Keywords

Interest Rate Maximum Likelihood Estimator Wiener Process Term Structure Process Capability Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Physica-Verlag Heidelberg 2009

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsUniversity of WindsorWindsorCanada
  2. 2.Faculty of Information Sciences and EngineeringUniversity of CanberraCanberraAustralia

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