Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1485
Title: Prioritizing of volatility models: a computational analysis using data envelopment analysis
Authors: Petridis, Konstantinos
Petridis, Nikolaos E.
Emrouznejad, Ali
Ben Abdelaziz, Fouad
Type: Article
Subjects: FRASCATI::Social sciences::Economics and Business::Finance
FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Social sciences::Economics and Business::Business and Management
Keywords: statistical distributions
forecasting
mathematics for computing
data envelopment analysis
ranking
β-regression
finance
Issue Date: 19-Jul-2021
Publisher: John Wiley & Sons Ltd
Source: International Transactions in Operational Research
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.
URI: https://doi.org/10.1111/itor.13028
https://ruomo.lib.uom.gr/handle/7000/1485
Other Identifiers: 10.1111/itor.13028
Appears in Collections:Department of Applied Informatics

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