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Testing Shifts in Financial Models with Conditional Heteroskedasticity: An Empirical Distribution Function Approach

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  • Shinn-Juh Lin
  • Jian Yang
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    Abstract

    This paper proposes a class of test procedures for a structural change with an unknown change point. In particular, we consider a general financial time series model with conditional heteroskedasticity. The test statistics are constructed via the empirical distribution approach and aim at detecting a change that may occur beyond the second moment. We derive the asymptotic null distributions of the test statistics and tabulate the critical values. Studies of the local power show that the test statistics have non-trivial local power. Finite sample performances of the proposed tests are studied via Monte Carlo methods. This test procedures are applied to test the change point in the S&P 500 daily index returns.

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    Bibliographic Info

    Paper provided by Quantitative Finance Research Centre, University of Technology, Sydney in its series Research Paper Series with number 30.

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    Date of creation: 01 Dec 1999
    Date of revision:
    Handle: RePEc:uts:rpaper:30

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    Related research

    Keywords: change point; empirical distribution function; sequential empirical process; weak convergence; two-parameter brownian bridge;

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    Cited by:
    1. Lundbergh, Stefan & Terasvirta, Timo, 2002. "Evaluating GARCH models," Journal of Econometrics, Elsevier, vol. 110(2), pages 417-435, October.
    2. Yang, Jian, 2001. "Structural change tests under regression misspecifications," Economics Letters, Elsevier, vol. 70(3), pages 311-317, March.

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