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Evaluating GARCH models

Author

Listed:
  • Lundbergh, Stefan

    () (Dept. of Economic Statistics, Stockholm School of Economics)

  • Teräsvirta, Timo

    () (Dept. of Economic Statistics, Stockholm School of Economics)

Abstract

In this paper a unified framework for testing the adequacy of an estimated GARCH model is presented. Parametric LM or LM type tests of no ARCH in standardized errors, linearity, and parameter constancy are proposed. The asymptotic null distributions of the tests are standard, which makes application easy. Versions of the tests that are robust against nonnormal errors are provided. The finite sample properties of the test statistics are investigated by simulation. The robust tests prove superior to the nonrobust ones when the errors are nonnormal. They also compare favourably in terms of power with misspecification tests previously proposed in the literature.

Suggested Citation

  • Lundbergh, Stefan & Teräsvirta, Timo, 1998. "Evaluating GARCH models," SSE/EFI Working Paper Series in Economics and Finance 292, Stockholm School of Economics, revised 03 Oct 2001.
  • Handle: RePEc:hhs:hastef:0292
    Note: This is the final revised version (October 2001) of the original (December 1998) paper.
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    References listed on IDEAS

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    4. Shinn-Juh Lin & Jian Yang, 1999. "Testing Shifts in Financial Models with Conditional Heteroskedasticity: An Empirical Distribution Function Approach," Research Paper Series 30, Quantitative Finance Research Centre, University of Technology, Sydney.
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    More about this item

    Keywords

    Conditional heteroskedasticity; model misspecification test; nonlinear time series; parameter constancy; smooth transition GARCH.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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