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Persistence In Nonlinear Time Series: A Nonparametric Approach

Author

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  • Juan Carlos Escanciano

    (Indiana University Bloomington)

  • Javier Hualde

    (Universidad de Navarra)

Abstract

The purpose of the present paper is to relate two important concepts of time series analysis, namely, nonlinearity and persistence. Traditional measures of persistence are based on correlations or periodograms, which may be inappropriate under nonlinearity and/or non-Gaussianity. This article proves that nonlinear persistence can be characterized by cumulative measures of dependence. The new cumulative measures are nonparametric, simple to estimate and do not require the use of any smoothing user-chosen parameters. In addition, we propose nonparametric estimates of our measures and establish their limiting properties. Finally, we employ our measures to analyze the nonlinear persistence properties of some international stock market indices, where we find an ubiquitous nonlinear persistence in conditional variance that is not accounted for by popular parametric models or by classical linear measures of persistence. This finding has important economic implications in, e.g., asset pricing and hedging. Conditional variance persistence in bull and bear markets is also analyzed and compared.

Suggested Citation

  • Juan Carlos Escanciano & Javier Hualde, 2009. "Persistence In Nonlinear Time Series: A Nonparametric Approach," CAEPR Working Papers 2009-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    • Handle: RePEc:inu:caeprp:2009003
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    File URL: https://caepr.indiana.edu/RePEc/inu/caeprp/caepr2009-003.pdf
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    References listed on IDEAS

    as
    1. Bollerslev, Tim & Engle, Robert F, 1993. "Common Persistence in Conditional Variances," Econometrica, Econometric Society, vol. 61(1), pages 167-186, January.
    2. Herman J. Bierens & Werner Ploberger, 1997. "Asymptotic Theory of Integrated Conditional Moment Tests," Econometrica, Econometric Society, vol. 65(5), pages 1129-1152, September.
    3. Ang, Andrew & Chen, Joseph, 2002. "Asymmetric correlations of equity portfolios," Journal of Financial Economics, Elsevier, vol. 63(3), pages 443-494, March.
    4. Ravi Bansal & Amir Yaron, 2004. "Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles," Journal of Finance, American Finance Association, vol. 59(4), pages 1481-1509, August.
    5. Nze, Patrick Ango & Doukhan, Paul, 2004. "Weak Dependence: Models And Applications To Econometrics," Econometric Theory, Cambridge University Press, vol. 20(6), pages 995-1045, December.
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    Cited by:

    1. Oliver Linton & Yoon Jae Whang & Yu-Min Yen, 2021. "The lower regression function and testing expectation dependence dominance hypotheses," Econometric Reviews, Taylor & Francis Journals, vol. 40(8), pages 709-727, September.

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