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Assessing the performance of a prediction error criterion model selection algorithm in the context of ARCH models

  • Stavros Degiannakis
  • Evdokia Xekalaki

A number of ARCH models are considered in the framework of evaluating the performance of a method for model selection based on a standardized prediction error criterion (SPEC). According to this method, the ARCH model with the lowest sum of squared standardized forecasting errors is selected for predicting future volatility. A number of statistical criteria, that measure the distance between predicted and inter-day realized volatility, are used to examine the performance of a model to predict future volatility, for forecasting horizons ranging from one day to 100 days ahead. The results reveal that the SPEC model selection procedure has a satisfactory performance in picking that model that generates 'better' volatility predictions. A comparison of the SPEC algorithm with a set of other model evaluation criteria yields similar findings. It appears, therefore, that it can be regarded as a tool in guiding the choice of the appropriate model for predicting future volatility, with applications in evaluating portfolios, managing financial risk and creating speculative strategies with options.

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Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

Volume (Year): 17 (2007)
Issue (Month): 2 ()
Pages: 149-171

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Handle: RePEc:taf:apfiec:v:17:y:2007:i:2:p:149-171
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