Degiannakis, Stavros and Xekalaki, Evdokia (2007): Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models. Published in: Applied Financial Economics No. 17 (2007): pp. 149171.

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Abstract
Autoregressive conditional heteroscedasticity (ARCH) models have successfully been applied in order to predict asset return volatility. Predicting volatility is of great importance in pricing financial derivatives, selecting portfolios, measuring and managing investment risk more accurately. In this paper, 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 interday realized volatility, are used to examine the performance of a model to predict future volatility, for forecasting horizons ranging from one day to one hundred 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 one’s choice of the appropriate model for predicting future volatility, with applications in evaluating portfolios, managing financial risk and creating speculative strategies with options.
Item Type:  MPRA Paper 

Original Title:  Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models 
English Title:  Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models 
Language:  English 
Keywords:  ARCH Models, Correlated Gamma Ratio Distribution, Model Selection, Predictability, SPEC Algorithm, Volatility Forecasting 
Subjects:  C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C32  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C40  General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods 
Item ID:  96324 
Depositing User:  Dr. STAVROS DEGIANNAKIS 
Date Deposited:  06 Oct 2019 09:50 
Last Modified:  06 Oct 2019 09:50 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/96324 