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Threshold Random Walks in the U.S. Stock Market

Author

Listed:
  • Zisimos Koustas

    (Department of Economics, Brock University)

  • Jean-Francois Lamarche

    (Department of Economics, Brock University)

  • Apostolos Serletis

    (Department of Economics, University of Calgary)

Abstract

This paper extends the work in Serletis and Shintani (2003) and Elder and Serletis ( 2006) by re-examining the empirical evidence for random walk type behavior in the U.S. stock market. In doing so, it tests the random walk hypothesis by employing unit-root tests that are designed to have more statistical power against nonlinear al- ternatives. The nonlinear feature of our model is re ected by three regimes, one of which is characterized by a unit root process and the random walk hypothesis while the lower and upper regimes are well captured by a stationary autoregressive process with mean reversion and predictability.

Suggested Citation

  • Zisimos Koustas & Jean-Francois Lamarche & Apostolos Serletis, 2006. "Threshold Random Walks in the U.S. Stock Market," Working Papers 0602, Brock University, Department of Economics, revised May 2006.
    • Handle: RePEc:brk:wpaper:0602
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    References listed on IDEAS

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    Cited by:

    1. Hinich, Melvin J. & Serletis, Apostolos, 2008. "Randomly modulated periodicity in the US stock market," Chaos, Solitons & Fractals, Elsevier, vol. 36(3), pages 654-659.

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    More about this item

    Keywords

    Asymmetric time series; Threshold adjustment; Nonlinear autoregression Autoregression;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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