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Robust Power Calculations with Tests for Serial Correlation in Stock Returns

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  • Matthew Richardson
  • Tom Smith

Abstract

This paper provides an asymptotically most powerful test for a particular class of statistics which test the hypothesis of no serial correlation. This class includes many of the statistics employed in the recent finance and macroeconomics literature. Furthermore, with respect to a popular mean reversion alternative model, we show that the asymptotically most powerful test is quite robust to distributional specifications.

Suggested Citation

  • Matthew Richardson & Tom Smith, "undated". "Robust Power Calculations with Tests for Serial Correlation in Stock Returns," Rodney L. White Center for Financial Research Working Papers 12-91, Wharton School Rodney L. White Center for Financial Research.
    • Handle: RePEc:fth:pennfi:12-91
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    Cited by:

    1. Perron, Pierre & Vodounou, Cosme, 2004. "Tests of return predictability: an analysis of their properties based on a continuous time asymptotic framework," Journal of Empirical Finance, Elsevier, vol. 11(2), pages 203-230, March.
    2. Campbell, John Y., 2001. "Why long horizons? A study of power against persistent alternatives," Journal of Empirical Finance, Elsevier, vol. 8(5), pages 459-491, December.
    3. Cosme Vodounou, 1998. "Inférence fondée sur les statistiques des rendements de long terme," CIRANO Working Papers 98s-20, CIRANO.

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