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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.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

Volume (Year): 9 (2003)
Issue (Month): 6 ()
Pages: 557-580

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Handle: RePEc:taf:eurjfi:v:9:y:2003:i:6:p:557-580

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Related research

Keywords: Akaike information criterion; Schwarz information criterion; GARCH; high frequency financial data; exchange rate prediction; volatility forecasting;

References

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  1. Pagan, A.R. & Schwert, G.W., 1989. "Alternative Models For Conditional Stock Volatility," Papers 89-02, Rochester, Business - General.
  2. 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.
  3. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  4. repec:att:wimass:9317 is not listed on IDEAS
  5. West, K.D. & Cho, D., 1993. "The Predictive Ability of Several Models of Exchange Rate Volatility," Working papers 9317r, Wisconsin Madison - Social Systems.
  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. 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.
  8. 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. 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.
  2. Bask, Mikael & Widerberg, Anna, 2008. "Market Structure and the Stability and Volatility of Electricity Prices," Working Papers in Economics 327, University of Gothenburg, Department of Economics.
  3. 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.

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