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https://ruomo.lib.uom.gr/handle/7000/1485
Πλήρης εγγραφή μεταδεδομένων
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Petridis, Konstantinos | - |
dc.contributor.author | Petridis, Nikolaos E. | - |
dc.contributor.author | Emrouznejad, Ali | - |
dc.contributor.author | Ben Abdelaziz, Fouad | - |
dc.date.accessioned | 2022-10-07T11:07:51Z | - |
dc.date.available | 2022-10-07T11:07:51Z | - |
dc.date.issued | 2021-07-19 | - |
dc.identifier | 10.1111/itor.13028 | en_US |
dc.identifier.uri | https://doi.org/10.1111/itor.13028 | en_US |
dc.identifier.uri | https://ruomo.lib.uom.gr/handle/7000/1485 | - |
dc.description.abstract | Economic 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.iso | en | en_US |
dc.publisher | John Wiley & Sons Ltd | en_US |
dc.source | International Transactions in Operational Research | en_US |
dc.subject | FRASCATI::Social sciences::Economics and Business::Finance | en_US |
dc.subject | FRASCATI::Natural sciences::Computer and information sciences | en_US |
dc.subject | FRASCATI::Social sciences::Economics and Business::Business and Management | en_US |
dc.subject.other | statistical distributions | en_US |
dc.subject.other | forecasting | en_US |
dc.subject.other | mathematics for computing | en_US |
dc.subject.other | data envelopment analysis | en_US |
dc.subject.other | ranking | en_US |
dc.subject.other | β-regression | en_US |
dc.subject.other | finance | en_US |
dc.title | Prioritizing of volatility models: a computational analysis using data envelopment analysis | en_US |
dc.type | Article | en_US |
dc.contributor.department | Τμήμα Εφαρμοσμένης Πληροφορικής | en_US |
Εμφανίζεται στις Συλλογές: | Τμήμα Εφαρμοσμένης Πληροφορικής |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
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Volatility_paper.pdf | Prioritizing of volatility models: a computational analysis using data envelopment analysis | 516,58 kB | Adobe PDF | Προβολή/Ανοιγμα |
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