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Detecting Nonlinearity in Time Series: Surrogate and Bootstrap Approaches

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  • Hinich Melvin J
  • Mendes Eduardo M

    (Federal University of Minas Gerais - Brazil)

  • Stone Lewi

Abstract

Detecting nonlinearity in financial time series is a key point when the main interest is to understand the generating process. One of the main tests for testing linearity in time series is the Hinich Bispectrum Nonlinearity Test (HINBIN). Although this test has been succesfully applied to a vast number of time series, further improvement in the size power of the test is possible. A new method that combines the bispectrum and the surrogate method and bootstrap is then presented for detecting nonlinearity, gaussianity and time reversibility. Simulated and real data examples are given to demonstrate the efficacy of the new tests.

Suggested Citation

  • Hinich Melvin J & Mendes Eduardo M & Stone Lewi, 2005. "Detecting Nonlinearity in Time Series: Surrogate and Bootstrap Approaches," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-15, December.
    • Handle: RePEc:bpj:sndecm:v:9:y:2005:i:4:n:3
    DOI: 10.2202/1558-3708.1268
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    References listed on IDEAS

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    1. Hidalgo, Javier, 2003. "An alternative bootstrap to moving blocks for time series regression models," Journal of Econometrics, Elsevier, vol. 117(2), pages 369-399, December.
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    Cited by:

    1. Nesmith Travis D & Jones Barry E, 2008. "Linear Cointegration of Nonlinear Time Series with an Application to Interest Rate Dynamics," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(1), pages 1-18, March.
    2. Belaire-Franch, Jorge, 2004. "Testing for non-linearity in an artificial financial market: a recurrence quantification approach," Journal of Economic Behavior & Organization, Elsevier, vol. 54(4), pages 483-494, August.
    3. Lim, Kian-Ping & Brooks, Robert D. & Hinich, Melvin J., 2008. "Nonlinear serial dependence and the weak-form efficiency of Asian emerging stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(5), pages 527-544, December.
    4. Acatrinei, Marius Cristian & Caraiani, Petre, 2011. "Modeling and Forecasting the Dynamics in Romanian Stock Market Indices Using Threshold Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 42-54, June.
    5. Houston Stokes & Melvin Hinich, 2011. "Detecting and modeling nonlinearity in the gas furnace data," Computational Statistics, Springer, vol. 26(1), pages 77-93, March.
    6. Gustavo Deco & Diego Vidaurre & Morten L. Kringelbach, 2021. "Revisiting the global workspace orchestrating the hierarchical organization of the human brain," Nature Human Behaviour, Nature, vol. 5(4), pages 497-511, April.
    7. Kugiumtzis Dimitris, 2008. "Evaluation of Surrogate and Bootstrap Tests for Nonlinearity in Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(1), pages 1-26, March.

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