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Test for the existence of finite moments via bootstrap

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  • Wai Leong Ng
  • Chun Yip Yau

Abstract

This paper develops a bootstrap hypothesis test for the existence of finite moments of a random variable, which is nonparametric and applicable to both independent and dependent data. The test is based on a property in bootstrap asymptotic theory, in which the m out of n bootstrap sample mean is asymptotically normal when the variance of the observations is finite. Consistency of the test is established. Monte Carlo simulations are conducted to illustrate the finite sample performance and compare it with alternative methods available in the literature. Applications to financial data are performed for illustration.

Suggested Citation

  • Wai Leong Ng & Chun Yip Yau, 2018. "Test for the existence of finite moments via bootstrap," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 28-48, January.
    • Handle: RePEc:taf:gnstxx:v:30:y:2018:i:1:p:28-48
    DOI: 10.1080/10485252.2017.1402896
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    References listed on IDEAS

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    1. Danielsson, J. & de Haan, L. & Peng, L. & de Vries, C. G., 2001. "Using a Bootstrap Method to Choose the Sample Fraction in Tail Index Estimation," Journal of Multivariate Analysis, Elsevier, vol. 76(2), pages 226-248, February.
    2. Igor Fedotenkov, 2014. "A note on the bootstrap method for testing the existence of finite moments," Statistica, Department of Statistics, University of Bologna, vol. 74(4), pages 447-453.
    3. Giraitis, Liudas & Robinson, Peter M., 2000. "Whittle estimation of ARCH models," LSE Research Online Documents on Economics 2277, London School of Economics and Political Science, LSE Library.
    4. Einmahl, J. H.J. & Dekkers, A. L.M. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Other publications TiSEM 81970cb3-5b7a-4cad-9bf6-2, Tilburg University, School of Economics and Management.
    5. Igor Fedotenkov, 2013. "A bootstrap method to test for the existence of finite moments," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 315-322, June.
    6. Liudas Giraitis & Peter M Robinson, 2000. "Whittle Estimation of ARCH Models," STICERD - Econometrics Paper Series 406, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    7. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    8. Hill, Jonathan B., 2010. "On Tail Index Estimation For Dependent, Heterogeneous Data," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1398-1436, October.
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    Cited by:

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    2. Claudio Giovanni Borroni & Lucio De Capitani, 2022. "Some measures of kurtosis and their inference on large datasets," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 573-607, December.

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