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A New Test for Superior Predictive Ability

Author

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  • Zongwu Cai
  • Jiancheng Jiang
  • Jingshuang Zhang

Abstract

This paper provides a new methodology to test the superior predictive ability (SPA) of technical trading rules relative to the benchmark without potential data snooping bias. Unlike other previous methods, we explicitly approximate the covariance matrix through certain decomposition, which decreases the number of elements needed to be estimated. With the help of covariance matrix, we are able to exploit more information contained in the diagonal and off-diagonal terms and as a result, so that we improve the effectiveness of testing result. Due to the nuisance parameter in composite hypothesis, we choose the generalized likelihood ratio (GLR) test which is of uniform most power, to alleviate such problem and at the same time, to provide a pivotal distribution. Bootstrap procedure is employed in our simulation to obtain the power of the test. The result shows that the GLR test dominates the SPA test proposed by Hansen (2005) in terms of power and our GLR test is sensitive to the inclusion of superior models. Therefore, it increases the power faster than that of SPA test. The result also suggests that the GLR test is less conservative than SPA test.

Suggested Citation

  • Zongwu Cai & Jiancheng Jiang & Jingshuang Zhang, 2013. "A New Test for Superior Predictive Ability," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    • Handle: RePEc:wyi:wpaper:002018
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    References listed on IDEAS

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    1. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, July.
    2. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    3. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    4. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. Peter Hansen, 2003. "Asymptotic Tests of Composite Hypotheses," Working Papers 2003-09, Brown University, Department of Economics.
    7. Hsu, Po-Hsuan & Hsu, Yu-Chin & Kuan, Chung-Ming, 2010. "Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 471-484, June.
    8. Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
    9. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
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    More about this item

    Keywords

    Covariance matrix estimation; Data snooping; Generalized likelihood Ratio test; Reality check; SPA test; Technical trading rules.;
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