Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models
Applied Financial Economics, Vol. 17, pp. 149-171, 2007
23 Pages Posted: 11 Nov 2007 Last revised: 1 Feb 2009
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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