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Detecting Nonlinearity in Time Series: Surrogate and Bootstrap Approaches

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Author Info

  • Hinich Melvin J

    ()
    (hinich@austin.utexas.edu)

  • Mendes Eduardo M

    ()
    (Federal University of Minas Gerais - Brazil)

  • Stone Lewi

    ()
    (lewi@lanina.tau.ac.il)

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.

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Bibliographic Info

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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Cited by:
  1. 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.
  2. Barry E. Jones & Travis D. Nesmith, 2006. "Linear cointegration of nonlinear time series with an application to interest rate dynamics," Finance and Economics Discussion Series 2007-03, Board of Governors of the Federal Reserve System (U.S.).
  3. 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.
  4. 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.
  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.

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