Testing financial time series for autocorrelation: Robust Tests

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Nelson Omar Muriel Torrero http://orcid.org/0000-0002-7760-7826

Resumen

Two modified Portmanteau statistics are studied under dependence assumptions common in financial applications which can be used for testing that heteroskedastic time series are serially uncorrelated without assuming independence or Normality. Their asymptotic distribution is found to be null and their small sample properties are examined via Monte Carlo. The power of the tests is studied under the MA and GARCH-in-mean alternatives. The tests exhibit an appropriate empirical size and are seen to be more powerful than a robust Box-Pierce to the selected alternatives. Real data on daily stock returns and exchange rates is used to illustrate the tests. 

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MURIEL TORRERO, Nelson Omar. Testing financial time series for autocorrelation: Robust Tests. CIENCIA ergo-sum, [S.l.], v. 27, n. 3, sep. 2020. ISSN 2395-8782. Disponible en: <https://cienciaergosum.uaemex.mx/article/view/11758>. Fecha de acceso: 21 oct. 2020 doi: https://doi.org/10.30878/ces.v27n3a6.
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