Autocorrelación en series de tiempo financieras: pruebas robustas

CIENCIA ergo-sum

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Title Autocorrelación en series de tiempo financieras: pruebas robustas
Testing financial time series for autocorrelation: Robust Tests
Creator Muriel Torrero, Nelson Omar
Description Se estudian dos estadísticos de Portmanteau modificados bajo supuestos de dependencia comunes en aplicaciones financieras que pueden utilizarse para comprobar que series de tiempo heterocedásticas son serialmente incorreladas sin suponer independencia o normalidad. Se encuentra que su distribución asintótica es nula y se examinan sus propiedades de muestras pequeñas usando Monte Carlo. El poder de las pruebas se estudia para alternativas MA y GARCH en la media. Las pruebas exhiben un tamaño muestral apropiado y se comprueba que son más poderosas que la prueba robusta de Box-Pierce para alternativas selectas. Ilustramos las pruebas usando datos diarios de retornos financieros y de tipos de cambio.
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. 
Publisher Universidad Autónoma del Estado de México
Date 2020-09-09
Type info:eu-repo/semantics/article
Artículo revisado por pares
Format application/pdf
Source CIENCIA ergo-sum; Vol. 27 Núm. 3 (2020): CIENCIA ergo-sum (noviembre 2020-febrero 2021)
CIENCIA ergo-sum; Vol. 27 Núm. 3 (2020): CIENCIA ergo-sum (noviembre 2020-febrero 2021)
Language spa
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