Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1485
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dc.contributor.authorPetridis, Konstantinos-
dc.contributor.authorPetridis, Nikolaos E.-
dc.contributor.authorEmrouznejad, Ali-
dc.contributor.authorBen Abdelaziz, Fouad-
dc.date.accessioned2022-10-07T11:07:51Z-
dc.date.available2022-10-07T11:07:51Z-
dc.date.issued2021-07-19-
dc.identifier10.1111/itor.13028en_US
dc.identifier.urihttps://doi.org/10.1111/itor.13028en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/1485-
dc.description.abstractEconomic crisis and uncertainty in global status quo affect stock markets around the world. This fact imposes improvement in the development of volatility models. However, the comparison among volatility models cannot be made based on a single-error measure as a model can perform better in one-error measure and worst in another. In this paper, we propose a two-stage approach for prioritizing volatility models, where in the first stage we develop a novel slack-based data envelopment analysis to rank volatility models. The robustness of the proposed approach has also been investigated using cluster analysis. In the second-stage analysis, it is investigated whether the efficiency scores depend on model characteristics. These attributes concern the time needed in order to estimate the model, the value of Akaike Information Criterion, the number of models’ significant parameters, groups and bias terms, and the error sum of squares (ESS). Further, dummy variables have been introduced to the regression model in order to find whether the employed model includes an in-mean effect, whether the assumed distribution is skewed, and whether the employed model belongs to the generalized autoregressive conditional heteroskedasticity (GARCH) family. The main findings of this research show that the number of models’ statistically significant coefficients, ESS, and in-mean effects tend to increase the efficiency scores, while time elapsed, the number of statistically significant bias terms, and skewed error distributions tend to decrease the efficiency score.en_US
dc.language.isoenen_US
dc.publisherJohn Wiley & Sons Ltden_US
dc.sourceInternational Transactions in Operational Researchen_US
dc.subjectFRASCATI::Social sciences::Economics and Business::Financeen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.subjectFRASCATI::Social sciences::Economics and Business::Business and Managementen_US
dc.subject.otherstatistical distributionsen_US
dc.subject.otherforecastingen_US
dc.subject.othermathematics for computingen_US
dc.subject.otherdata envelopment analysisen_US
dc.subject.otherrankingen_US
dc.subject.otherβ-regressionen_US
dc.subject.otherfinanceen_US
dc.titlePrioritizing of volatility models: a computational analysis using data envelopment analysisen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
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