Prioritizing of volatility models: a computational analysis using data envelopment analysis


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.

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Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: This is the peer reviewed version of the following article: Petridis, K., Petridis, N.E., Emrouznejad, A. and Ben Abdelaziz, F. (2021), Prioritizing of volatility models: a computational analysis using data envelopment analysis. Intl. Trans. in Op. Res.., which has been published in final form at  This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Uncontrolled Keywords: finance,forecasting,mathematics for computing; data envelopment analysis,ranking,statistical distributions,β$\beta $-regression,Management of Technology and Innovation,Business and International Management,Computer Science Applications,Strategy and Management,Management Science and Operations Research
Publication ISSN: 1475-3995
Last Modified: 16 Apr 2024 07:33
Date Deposited: 26 Jul 2021 12:24
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Related URLs: https://onlinel ... 1111/itor.13028 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-09
Published Online Date: 2021-07-19
Accepted Date: 2021-04-29
Authors: Petridis, Konstantinos
Petridis, Nikolaos E.
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)
Ben Abdelaziz, Fouad



Version: Accepted Version

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