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GARCH model selection criteria

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  • Heather Mitchell
  • Michael Mckenzie

Abstract

The autoregressive conditional heteroscedasticity (ARCH) family of models has grown to encompass a wide range of specifications, each of which is designed to enhance the ability of the model to capture the characteristics of the data. In this paper, the ability of a number of model selection criteria to correctly identify the data generating process in simulated data is established. The results of this study suggest that the Hannan-Quinn and stochastic complexity criteria provide a superior level of performance for ARCH and generalized ARCH (GARCH) processes compared to the more commonly used criteria. Where leverage and/or power effects are present, however, none of the procedures considered perform well. A new LM based test for the presence of nonlinearity and power effects is introduced and tested.

Suggested Citation

  • Heather Mitchell & Michael Mckenzie, 2003. "GARCH model selection criteria," Quantitative Finance, Taylor & Francis Journals, vol. 3(4), pages 262-284.
    • Handle: RePEc:taf:quantf:v:3:y:2003:i:4:p:262-284
    DOI: 10.1088/1469-7688/3/4/303
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    Cited by:

    1. Charles, Amélie & Darné, Olivier, 2014. "Volatility persistence in crude oil markets," Energy Policy, Elsevier, vol. 65(C), pages 729-742.
    2. Klender Cortez & Martha del Pilar Rodríguez-García & Samuel Mongrut, 2020. "Exchange Market Liquidity Prediction with the K-Nearest Neighbor Approach: Crypto vs. Fiat Currencies," Mathematics, MDPI, vol. 9(1), pages 1-15, December.
    3. Michail Karoglou & Panicos Demetriades & Siong Law, 2011. "One date, one break?," Empirical Economics, Springer, vol. 41(1), pages 7-24, August.
    4. Manera, Matteo & Nicolini, Marcella & Vignati, Ilaria, 2016. "Modelling futures price volatility in energy markets: Is there a role for financial speculation?," Energy Economics, Elsevier, vol. 53(C), pages 220-229.

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