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Information criteria for GARCH model selection

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  • Chris Brooks
  • Simon Burke

Abstract

In this paper, a set of appropriately modified information criteria for selection of models from the AR-GARCH class is derived. It is argued that unmodified or naively modified traditional information criteria cannot be used for order determination in the context of conditionally heteroscedastic models. The models selected using the modified criteria are then used to forecast both the conditional mean and the conditional variance of two high frequency exchange rate series. The analysis indicates that although the use of such model selection methods does lead to significantly improved forecasting accuracies for the conditional variance in some instances, these improvements are by no means universal. The use of these criteria to jointly select conditional mean and conditional variance model orders leads to performance degradation for the conditional mean forecasts compared to models which do not allow for the heteroscedasticity.

Suggested Citation

  • Chris Brooks & Simon Burke, 2003. "Information criteria for GARCH model selection," The European Journal of Finance, Taylor & Francis Journals, vol. 9(6), pages 557-580.
  • Handle: RePEc:taf:eurjfi:v:9:y:2003:i:6:p:557-580
    DOI: 10.1080/1351847021000029188
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    References listed on IDEAS

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    1. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
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    5. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    6. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
    7. Bera, Anil K & Higgins, Matthew L, 1997. "ARCH and Bilinearity as Competing Models for Nonlinear Dependence," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 43-50, January.
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    Cited by:

    1. WenShwo Fang & Stephen M. Miller & ChunShen Lee, 2008. "Cross-Country Evidence On Output Growth Volatility: Nonstationary Variance And Garch Models," Scottish Journal of Political Economy, Scottish Economic Society, vol. 55(4), pages 509-541, September.
    2. Bask, Mikael & Widerberg, Anna, 2009. "Market structure and the stability and volatility of electricity prices," Energy Economics, Elsevier, vol. 31(2), pages 278-288, March.
    3. Olusanya E. Olubusoye & OlaOluwa S. Yaya, 2016. "Time series analysis of volatility in the petroleum pricing markets: the persistence, asymmetry and jumps in the returns series," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 40(3), pages 235-262, September.
    4. Frimpong, Joseph Magnus & Oteng-Abayie, Eric Fosu, 2007. "Market Returns and Weak-Form Efficiency: the case of the Ghana Stock Exchange," MPRA Paper 7582, University Library of Munich, Germany, revised 09 Mar 2008.
    5. 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.
    6. Frimpong, Joseph Magnus & Oteng-Abayie, Eric Fosu, 2006. "Modelling and Forecasting Volatility of Returns on the Ghana Stock Exchange Using GARCH Models," MPRA Paper 593, University Library of Munich, Germany, revised 07 Oct 2006.

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