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Random Walk or Chaos: A Formal Test on the Lyapunov Exponent

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  • Joon Y. Park

    ()

  • Yoon-Jae Whang

Abstract

A formal test on the Lyapunov exponent is developed to distinguish a random walk model from a chaotic system. The test is based on the Nadaraya-Watson kernel estimate of the Lyapunov exponent. We show that the estimator is consistent: The estimated Lyapunov exponent converges to zero under the random walk hypothesis, while it converges to a positive constant for the chaotic system. The test is thus expected to have discriminatory powers. We derive the asymptotic distribution of the estimator, and make it possible to formally test for the null hypothesis of random walk against chaos. The proposed test statistic is a simple normalization of the estimated Lyapunov exponent. It is shown that the null distribution of the test statistic is given by the range of standard Brownian motion on the unit interval. We confirm through simulation that our test performs reasonably well in finite samples. We also apply out test to some of the standard macro and financial time series. For various stock price indices, the random walk hypothesis is rather strongly rejected in favor of the presence of a chaotic behavior. Contrarily, we find little evidence of chaos for most exchange rates and interest rates.

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Bibliographic Info

Paper provided by Institute of Economic Research, Seoul National University in its series Working Paper Series with number no9.

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Date of creation: Mar 1999
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Handle: RePEc:snu:ioerwp:no9

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Keywords: Lyapunov exponent; chaos; random walk; unit root; kernel regression; Brownian motion; local time; stochastic integrals;

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  1. Peter C.B. Phillips & Tassos Magdalinos, 2004. "Limit Theory for Moderate Deviations from a Unit Root," Cowles Foundation Discussion Papers 1471, Cowles Foundation for Research in Economics, Yale University.
  2. Oliver Linton & Mototsugu Shintani, 2001. "Is There Chaos in the World Economy? A Nonparametric Test Using Consistent Standard Errors," FMG Discussion Papers dp383, Financial Markets Group.
  3. Mototsugu Shintani & Oliver Linton, 2002. "Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos," LSE Research Online Documents on Economics 2093, London School of Economics and Political Science, LSE Library.
  4. Peter C.B. Phillips & Joon Y. Park, 1998. "Nonstationary Density Estimation and Kernel Autoregression," Cowles Foundation Discussion Papers 1181, Cowles Foundation for Research in Economics, Yale University.
  5. Chi-Young Choi & Young-Kyu Moh, 2007. "How useful are tests for unit-root in distinguishing unit-root processes from stationary but non-linear processes?," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 82-112, 03.
  6. Diba, Behzad T & Grossman, Herschel I, 1988. "Explosive Rational Bubbles in Stock Prices?," American Economic Review, American Economic Association, vol. 78(3), pages 520-30, June.
  7. Whang, Yoon-Jae & Linton, Oliver, 1999. "The asymptotic distribution of nonparametric estimates of the Lyapunov exponent for stochastic time series," Journal of Econometrics, Elsevier, vol. 91(1), pages 1-42, July.
  8. Brock, W. A., 1986. "Distinguishing random and deterministic systems: Abridged version," Journal of Economic Theory, Elsevier, vol. 40(1), pages 168-195, October.
  9. Simón Sosvilla-Rivero & Fernando Fernández-Rodriguez & Julián Andrada-Félix, 2005. "Testing chaotic dynamics via Lyapunov exponents," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 911-930.
  10. Rodney Wolff & Qiwei Yao & Howell Tong, 2003. "Statistical Tests for Lyapunov Exponents of Deterministic Systems," School of Economics and Finance Discussion Papers and Working Papers Series 167, School of Economics and Finance, Queensland University of Technology.
  11. Linton, O. & Whang, Yoon-Jae, 2007. "The quantilogram: With an application to evaluating directional predictability," Journal of Econometrics, Elsevier, vol. 141(1), pages 250-282, November.
  12. Peter C.B. Phillips & Joon Y. Park, 1998. "Asymptotics for Nonlinear Transformations of Integrated Time Series," Cowles Foundation Discussion Papers 1182, Cowles Foundation for Research in Economics, Yale University.
  13. repec:cup:etheor:v:11:y:1995:i:3:p:560-96 is not listed on IDEAS
  14. Andrews, Donald W.K., 1995. "Nonparametric Kernel Estimation for Semiparametric Models," Econometric Theory, Cambridge University Press, vol. 11(03), pages 560-586, June.
  15. Dechert, W D & Gencay, R, 1992. "Lyapunov Exponents as a Nonparametric Diagnostic for Stability Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S41-60, Suppl. De.
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Cited by:
  1. repec:att:wimass:9716 is not listed on IDEAS
  2. Mototsugu Shintani & Oliver Linton, 2003. "Is There Chaos in the World Economy? A Nonparametric Test Using Consistent Standard Errors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(1), pages 331-357, February.
  3. Domowitz, Ian & El-Gamal, Mahmoud A., 2001. "A consistent nonparametric test of ergodicity for time series with applications," Journal of Econometrics, Elsevier, vol. 102(2), pages 365-398, June.
  4. Arturo Lorenzo Valdés, 2002. "Pruebas de no linealidad de los rendimientos del mercado mexicano accionario: coeficientes de Lyapunov," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 17(2), pages 305-322.

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