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Evaluating the Forecasting Performance of GARCH Models Using White’s Reality Check

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  • Souza, Leonardo
  • Veiga, Alvaro
  • Medeiros, Marcelo C.

Abstract

The important issue of forecasting volatilities brings the difficult task of back-testing the forecasting performance. As volatility cannot be observed directly, one has to use an observable proxy for volatility or a utility function to assess the prediction quality. This kind of procedure can easily lead to poor assessment. The goal of this paper is to compare different volatility models and different performance measures using White’s Reality Check. The Reality Check consists of a non-parametric test that checks if any of a number of concurrent methods yields forecasts significantly better than a given benchmark method. For this purpose, a Monte Carlo simulation is carried out with four different processes, one of them a Gaussian white noise and the others following GARCH specifications. Two benchmark methods are used: the naive (predicting the out-of-sample volatility by in-sample variance) and the Riskmetrics method

Suggested Citation

  • Souza, Leonardo & Veiga, Alvaro & Medeiros, Marcelo C., 2005. "Evaluating the Forecasting Performance of GARCH Models Using White’s Reality Check," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 25(1), May.
  • Handle: RePEc:sbe:breart:v:25:y:2005:i:1:a:2671
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    1. Bollerslev, Tim & Ghysels, Eric, 1996. "Periodic Autoregressive Conditional Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-151, April.
    2. Brooks, Chris & Burke, Simon P. & Persand, Gita, 2001. "Benchmarks and the accuracy of GARCH model estimation," International Journal of Forecasting, Elsevier, vol. 17(1), pages 45-56.
    3. 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.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Cifter, Atilla, 2012. "Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 127-142, June.

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