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Generalized Variance-Ratio Tests in the Presence of Statistical Dependence

Author

Listed:
  • Neil Kellard
  • Denise Osborn
  • Jerry Coakley
  • John C. Nankervis
  • Periklis Kougoulis
  • Jerry Coakley

Abstract

type="main" xml:id="jtsa12124-abs-0001"> This article extends and generalizes the variance-ratio (VR) statistic by employing an estimator of the asymptotic covariance matrix of the sample autocorrelations. The estimator is consistent under the null for general classes of innovations exhibiting statistical dependence including exponential generalized autoregressive conditional heteroskedasticity and non-martingale difference sequence processes. Monte Carlo experiments show that our generalized test statistics have good finite sample size and superior power properties to other recently developed VR versions. In an application to two major US stock indices, our new generalized VR tests provide stronger rejections of the null than do competing VR tests.

Suggested Citation

  • Neil Kellard & Denise Osborn & Jerry Coakley & John C. Nankervis & Periklis Kougoulis & Jerry Coakley, 2015. "Generalized Variance-Ratio Tests in the Presence of Statistical Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 687-705, September.
    • Handle: RePEc:bla:jtsera:v:36:y:2015:i:5:p:687-705
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    File URL: http://hdl.handle.net/10.1111/jtsa.12124
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    More about this item

    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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