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Detecting Nonlinearity in Time Series: Surrogate and Bootstrap Approaches

Listed author(s):
  • Hinich Melvin J

    ()

    (hinich@austin.utexas.edu)

  • Mendes Eduardo M

    ()

    (Federal University of Minas Gerais - Brazil)

  • Stone Lewi

    ()

    (lewi@lanina.tau.ac.il)

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.

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File URL: https://www.degruyter.com/view/j/snde.2005.9.4/snde.2005.9.4.1268/snde.2005.9.4.1268.xml?format=INT
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Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 9 (2005)
Issue (Month): 4 (December)
Pages: 1-15

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Handle: RePEc:bpj:sndecm:v:9:y:2005:i:4:n:3
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  1. Christopher Brooks & Melvin Hinich, 1998. "Episodic nonstationarity in exchange rates," Applied Economics Letters, Taylor & Francis Journals, vol. 5(11), pages 719-722.
  2. Barnett, William A. & Gallant, A. Ronald & Hinich, Melvin J. & Jungeilges, Jochen A. & Kaplan, Daniel T. & Jensen, Mark J., 1997. "A single-blind controlled competition among tests for nonlinearity and chaos," Journal of Econometrics, Elsevier, vol. 82(1), pages 157-192.
  3. Dean Prichard & James Theiler, 1994. "Generating Surrogate Data for Time Series with Several Simultaneously Measured Variables," Working Papers 94-04-023, Santa Fe Institute.
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