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Generalized variance ratio tests in the presence of statistical dependence


  • Periklis Kougoulis

    (University of Essex)

  • John C. Nankervis

    (University of Essex)

  • Jerry Coakley

    (University of Essex)


We develop extensions of the variance-ratio statistic for testing the hypothesis a time series is uncorrelated and investigate their finite-sample performance. The tests employ an estimator of the asymptotic covariance matrix of the sample autocorrelations that is consistent under the null for general classes of innovations including EGARCH and non-MDS processes. Monte Carlo experiments show that our tests have better finite-sample size and power properties than the standard variance-ratio tests in experiments using time series generated by EGARCH and non-MDS processes

Suggested Citation

  • Periklis Kougoulis & John C. Nankervis & Jerry Coakley, 2006. "Generalized variance ratio tests in the presence of statistical dependence," Computing in Economics and Finance 2006 180, Society for Computational Economics.
  • Handle: RePEc:sce:scecfa:180

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    References listed on IDEAS

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    More about this item


    Non-MDS process; Monte Carlo;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General


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