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Asymptotic Inference For The Parameters Of A Discrete‐Time Square‐Root Process

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  • Halina Frydman

Abstract

This paper is concerned with asymptotic properties of the maximum likelihood estimators for the discrete‐time square‐root process. This process and its generalizations are employed in financial literature as models for movements of asset prices. the considered process is nonergodic and therefore standard maximum likelihood theory does not apply. the nonstandard asymptotic theory is developed. Strong consistency of the estimators is established, joint asymptotic distribution of the properly normalized estimators is obtained and confidence intervals for the parameters are constructed. the results of the small simulation study are reported.

Suggested Citation

  • Halina Frydman, 1994. "Asymptotic Inference For The Parameters Of A Discrete‐Time Square‐Root Process," Mathematical Finance, Wiley Blackwell, vol. 4(2), pages 169-181, April.
    • Handle: RePEc:bla:mathfi:v:4:y:1994:i:2:p:169-181
    DOI: 10.1111/j.1467-9965.1994.tb00056.x
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    References listed on IDEAS

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    1. Lo, Andrew W., 1988. "Maximum Likelihood Estimation of Generalized Itô Processes with Discretely Sampled Data," Econometric Theory, Cambridge University Press, vol. 4(2), pages 231-247, August.
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    Cited by:

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    2. Christian M. Dahl & Emma M. Iglesias, 2021. "Asymptotic normality of the MLE in the level-effect ARCH model," Statistical Papers, Springer, vol. 62(1), pages 117-135, February.

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