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Small sample properties of ML estimator in Vasicek and CIR models: a simulation experiment

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  • Giuseppina Albano

    (University of Salerno)

  • Michele La Rocca

    (University of Salerno)

  • Cira Perna

    (University of Salerno)

Abstract

In this paper we analyze small sample properties of the ML estimation procedure in Vasicek and CIR models. In particular, we consider short time series, with a length between 20 and 200, typically values observed in the field of survival data. We perform a simulation study in order to investigate which properties of the parameter estimators still remain valid and to evaluate the effect of a bootstrap bias correction method. The results show that the bias of the estimators can be really strong for small samples and the relative bias seems to be worse when the true parameters of the models are near to the nonstationarity case. The bootstrap bias correction is enough efficient in correcting the bias also for very small sample sizes, but the increase in RMSE of the estimator is greater as much as smaller is the bias in the ML estimator. Moreover, the bootstrap correction does not improve the performance of the tests on the parameters.

Suggested Citation

  • Giuseppina Albano & Michele La Rocca & Cira Perna, 2019. "Small sample properties of ML estimator in Vasicek and CIR models: a simulation experiment," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(1), pages 5-19, June.
    • Handle: RePEc:spr:decfin:v:42:y:2019:i:1:d:10.1007_s10203-019-00237-y
    DOI: 10.1007/s10203-019-00237-y
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    References listed on IDEAS

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    Cited by:

    1. Albano, G. & Giorno, V., 2020. "Inferring time non-homogeneous Ornstein Uhlenbeck type stochastic process," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).

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    More about this item

    Keywords

    Linear drift process; Bootstrap resampling; Vasicek and CIR models;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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