Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models

Stavros Degiannakis and Evdokia Xekalaki

MPRA Paper from University Library of Munich, Germany

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 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 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.

Keywords: ARCH Models; Correlated Gamma Ratio Distribution; Model Selection; Predictability; SPEC Algorithm; Volatility Forecasting (search for similar items in EconPapers)
JEL-codes: C32 C40 C52 C53 (search for similar items in EconPapers)
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Published in Applied Financial Economics 17 (2007): pp. 149-171

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/96324/1/MPRA_paper_96324.pdf original version (application/pdf)

Related works:
Journal Article: Assessing the performance of a prediction error criterion model selection algorithm in the context of ARCH models (2007)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:96324

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().