An illustration of dynamic network DEA in commercial banking including robustness tests
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| Název: | An illustration of dynamic network DEA in commercial banking including robustness tests |
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| Autoři: | Avkiran, Necmi Kemal |
| Zdroj: | Omega. 55:141-150 |
| Informace o vydavateli: | Elsevier BV, 2015. |
| Rok vydání: | 2015 |
| Témata: | 13. Climate action, 8. Economic growth, 1408 Strategy and Management, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 1802 Information Systems and Management, Banks as networks, Inter-temporal bank performance, 1803 Management Science and Operations Research, 02 engineering and technology, Dynamic network DEA, Robustness |
| Popis: | The main motivation of this article is to illustrate dynamic network data envelopment analysis (DN-DEA) in commercial banking with emphasis on testing robustness. To this end, sixteen foreign banks in China are benchmarked against thirty-two domestic banks for the post-2007 period that follows major reforms. When network and dynamic dimensions are brought together, a more comprehensive analysis of the period 2008–2010 is enabled where divisional and between-period interactions are reflected in efficiency estimates. Weighted, variable returns-to-scale, non-oriented dynamic network slacks-based measure is used within the framework of the intermediation approach to bank behavior. A bank network (i.e., a decision-making unit, DMU) is conceptualized as comprised of two divisions or sub-DMUs, namely, interest-bearing operations and non-interest operations linked by number of referrals. Undesirable outputs from sub-DMUs 1 and 2 (non-performing loans, and proportion of fruitless referrals, respectively) are treated as carry-overs that impact the efficiency of the following periods. Under robustness testing, the illustrative application discusses discrimination by efficiency estimates, dimensionality of the performance model, stability of estimates through re-sampling (leave-one-out method), and sensitivity of results to divisional weights and returns-to-scale assumptions. The results based on Chinese commercial banks are illustrative in nature because of simulated data used on two of the variables. |
| Druh dokumentu: | Article |
| Jazyk: | English |
| ISSN: | 0305-0483 |
| DOI: | 10.1016/j.omega.2014.07.002 |
| Přístupová URL adresa: | https://core.ac.uk/display/43355547 https://ideas.repec.org/a/eee/jomega/v55y2015icp141-150.html https://espace.library.uq.edu.au/view/UQ:340889 https://econpapers.repec.org/RePEc:eee:jomega:v:55:y:2015:i:c:p:141-150 https://www.sciencedirect.com/science/article/pii/S0305048314000796 |
| Rights: | Elsevier TDM |
| Přístupové číslo: | edsair.doi.dedup.....e91134b5e9e692e64994b33a929f33fe |
| Databáze: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | The main motivation of this article is to illustrate dynamic network data envelopment analysis (DN-DEA) in commercial banking with emphasis on testing robustness. To this end, sixteen foreign banks in China are benchmarked against thirty-two domestic banks for the post-2007 period that follows major reforms. When network and dynamic dimensions are brought together, a more comprehensive analysis of the period 2008–2010 is enabled where divisional and between-period interactions are reflected in efficiency estimates. Weighted, variable returns-to-scale, non-oriented dynamic network slacks-based measure is used within the framework of the intermediation approach to bank behavior. A bank network (i.e., a decision-making unit, DMU) is conceptualized as comprised of two divisions or sub-DMUs, namely, interest-bearing operations and non-interest operations linked by number of referrals. Undesirable outputs from sub-DMUs 1 and 2 (non-performing loans, and proportion of fruitless referrals, respectively) are treated as carry-overs that impact the efficiency of the following periods. Under robustness testing, the illustrative application discusses discrimination by efficiency estimates, dimensionality of the performance model, stability of estimates through re-sampling (leave-one-out method), and sensitivity of results to divisional weights and returns-to-scale assumptions. The results based on Chinese commercial banks are illustrative in nature because of simulated data used on two of the variables. |
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| ISSN: | 03050483 |
| DOI: | 10.1016/j.omega.2014.07.002 |