Two-stage DEA-Truncated Regression: Application in Banking Efficiency and Financial Development

Filipa Da Silva Fernandes, Charalampos Stasinakis, V. Bardarova

Research output: Contribution to journalArticle

24 Citations (Scopus)
16 Downloads (Pure)

Abstract

This study evaluates the efficiency of peripheral European domestic banks and examines the effects of bank-risk determinants on their performance over 2007–2014. Data Envelopment Analysis is utilised on a Malmquist Productivity Index in order to calculate the bank efficiency scores. Next, a Double Bootstrapped Truncated Regression is applied to obtain bias-corrected scores and examine whether changes in the financial conditions affect differently banks’ efficiency levels. The analysis accounts for the sovereign debt crisis period and for different levels of financial development in the countries under study. Such an application in the respective European banking setting is unique. The proposed method also copes with common misspecification problems observed in regression models based on efficiency scores. The results have important policy implications for the Euro area, as they indicate the existence of a periphery efficiency meta-frontier. Liquidity and credit risk are found to negatively affect banks productivity, whereas capital and profit risk have a positive impact on their performance. The crisis period is found to augment these effects, while bank-risk variables affect more banks' efficiency when lower levels of financial development are observed.

Original languageEnglish
Pages (from-to)284-301
Number of pages18
JournalExpert Systems with Applications
Volume96
Early online date8 Dec 2017
DOIs
Publication statusPublished - 15 Apr 2018

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, [96, (2017)] DOI: 10.1016/j.eswa.2017.12.010

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Data envelopment analysis
  • Truncated regression
  • Bank efficiency
  • Financial development

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