Application of Window Malmquist Index for Examination of Efficiency Change of Czech Commercial Banks

Open access

Abstract

The aim of the paper is to apply the Window Malmquist index approach to examine the efficiency change of Czech commercial banks within the period 2004-2013. We used the Data Envelopment Analysis and theWindow Malmquist index approaches to estimate the efficiency change of Czech commercial banks. The average efficiency computed under the assumption of constant returns to scale was 73% and under the assumption of variable returns to scale the value was 83%. We estimated the average positive efficiency growth of Czech commercial banks during the period 2004-2013. We found that average scale efficiency was 88%, which means that Czech commercial banks were of an inappropriate size, especially the largest banks.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Anayiotos G. Toroyan H. Vamvakidis A. (2010). The efficiency of emerging Europe’s banking sector before and after the recent economic crisis. Financial Theory and Practice 34(3) 247-267.

  • Andries A. M. Cocris V. (2010). A Comparative Analysis of the Efficiency of Romanian Banks. Romanian Journal of Economic Forecasting 4 54-75.

  • Asmild M. Paradi J. C. Aggarwall V. Schaffnit C. (2004). Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry. Journal of Productivity Analysis 21(1) 67-89.

  • Banker R. D. Charnes A. Cooper W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science 30 1078-1092.

  • Baruník J. Soták B. (2010). Influence of Different Ownership Forms on Efficiency of Czech and Slovak Banks: Stochastic Frontier Approach. Politická ekonomie 2010(2) 207-224.

  • Bems R. Sorsa P. (2008). Efficiency of the Slovene Banking Sector in the EU context. Journal for Money and Banking 57 11.

  • Berger A. N. Mester L. J. (1997). Inside the black box: What explains differences in the efficiencies of financial institutions? Journal of Banking and Finance 21 895-947.

  • Caves D. C. Christensen L. R. Dievert W. E. (1982). The economic theory of index number and the measurement of input output and productivity. Econometrica 50 1393-1414.

  • Charnes A. Clark T. Cooper W. W. Golany B. (1985). A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in U. S. Air Forces. In: Thompson R. Thrall R.M. (Eds.). Annals of Operational Research 2 95-112.

  • Charnes A. Cooper W. W. Lewin A. Y. Seiford L. M. (1995). Data Envelopment Analysis: Theory Methodology and Applications. New York: Springer-Verlag.

  • Charnes A. Cooper W. W. Rhodes E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research 2 429-444.

  • Coelli T. J. Prasada Rao D. S. Battese G. E. (1998). An Introduction to Efficiency ad Productivity Analysis. Boston: Kluwer Academic Publishers.

  • Cooper W. W. Seiford L. M. Zhu J. (2011). Handbook on Data Envelopment Analysis. New York: Springer Science + Business Media.

  • Cooper W. Seiford L. M. Tone K. (2007). Data Envelopment Analysis: A Comprehensive Text with Models Applications. New York: Springer Science.

  • Fare R. Grosskopf S. Lindgren B. Roose P. (1992). Productivity change in Swedish analysis Pharmacies 1980-1989: A nonparametric Malmquist approach. Journal of Productivity 3 85-102.

  • Fare R. Grosskopf S. Norris M. Zhang A. (1994). Productivity growth technical progress and efficiency changes in industrial country. American Economic Review 84 66-83.

  • Grigorian D. ManoleV. (2006). Determinants of Commercial Bank Performance in Transition: An Application of Data Envelopment Analysis. Comparative Economic Studies 48(3) 497-522.

  • Gu H. Yue J. (2011). The Relationship between Bank Efficiency and Stock Returns: Evidence from Chinese Listed Banks. World Journal of Social Sciences 1(4) 95-106.

  • Hanclová J. Stanícková M. (2012). Assessment of theVisegrad Countries Performance by Application of the DEA Based Malmquist Productivity Index. In Advances in Economics Risk Management Political and Law Science proceedings of the 1stWSEAS International Conference on Economics Political and Law Science (EPLS ’12). Zlín: Tomas Bata University.

  • Jablonský J. (2012). Data envelopment analysis models with network structure. In: Ramík J. Stavárek D. (Eds.). Proceedings of the 30th International Conference Mathematical Methods in Economics 2012. Karviná: Silesian University School of Business Administration.

  • Jacobs R. Smith P. C. Street A. (2006). Measuring Efficiency in Health Care Analytic Techniques and Health Policy. Cambridge: Cambridge University Press.

  • Lyroudi K. Angelidis D. (2006). Measuring Banking Productivity of the Most Recent European Union Member Countries; A Non-Parametric Approach. Journal of Economics and Business 9(1) 37-57.

  • Malmquist S. (1953). Index numbers and indifference surfaces. Trabajos de Estadistica 4 209-242.

  • Mamatzakis E. Staikouras C. Koutsomanoli-Filippaki A. (2008). Bank efficiency in the new European Union member states: Is there convergence? International Review of Financial Analysis 17(5) 1156-1172.

  • Matoušek R. (2008). Efficiency and scale economies in banking in new EU countries. International Journal of Monetary Economics and Finance 1(3) 235-249.

  • Palecková I. (2015). Efficiency Change in Banking Sectors of Visegrad Countries. In Proceedings of the 7th International Scientific Conference Finance and the Performance of Firms in Science Education and Practice. Zlín: Tomas Bata University.

  • Rezitis A. N. (2010). Agricultural productivity and convergence: Europe and the United States. Applied Economics 42(8) 1029-1044.

  • Rossi S. P. S. Schwaiger M. Winkler G. (2005). Managerial behavior and cost/profit efficiency in the banking sectors of Central and Eastern European countries. Working paper No. 96. Wien: Oesterreichische Nationalbank.

  • Repková I. (2012). Measuring the efficiency in the Czech banking industry: Data Envelopment Analysis and Malmquist index. In: Proceedings of 30th International Conference Mathematical Methods in Economics. Silesian University School of Business Administration Karviná.

  • Repková I. (2013). Estimation of Banking Efficiency in the Czech Republic: Dynamic Data Envelopment Analysis. DANUBE: Law and Economics Review 4(4) 261-275.

  • Repková I. (2014). Efficiency of the Czech banking sector employing the DEA window analysis approach. Procedia Economics and Finance 12 587-596.

  • Seiford L. M. Thrall R. M. (1990). Recent developments in DEA: the mathematical programming approach to frontier analysis. Journal of Econometrics 46 7-38.

  • Stanek R. (2010). Efektivnost ceského bankovního sektoru v letech 2000-2009. In: Konkurenceschopnost a stabilita. Brno: Masaryk University.

  • Stavárek D. (2005). Restrukturalizace bankovních sektoru a efektivnost bank v zemích Visegrádské skupiny. Karviná: Silesian University School of Business Administration.

  • Stavárek D. Poloucek S. (2004). Efficiency and Profitability in the Banking Sector. In: Poloucek S. (Ed.). Reforming the Financial Sector in Central European Countries. Palgrave Macmillan Publishers Hampshire.

  • Stavárek D. Repková I. (2012). Efficiency in the Czech banking industry: A nonparametric approach. Acta Universitatis Agriculturae et Silviculturae Mendeleianae Brunensis 60 357-366.

  • Sueyoshi T. Aoki S. (2001). A use of a nonparametric statistic for DEA frontier shift: the Kruskal and Wallis rank test. Omega 29 1-18.

  • Worthington A. C. (1999). Malmquist Indices of Productivity Change in Australian Financial Services. Journal of International Financial Markets Institutions and Money 9(3) 303-320.

  • Yildirim H. S. Philippatos G. C. (2007). Efficiency of banks: Recent evidence from the transition economies of Europe 1993-2003. The European Journal of Finance 13(2) 123-143.

  • Yue P. (1992). Data Envelopment Analysis and Commercial Bank Performance: A Primer with Applications to Missouri Banks. Federal Reserve Bank of St. Louis Review 74(1) 31-45.

Search
Journal information
Impact Factor


CiteScore 2018: 0.5

SCImago Journal Rank (SJR) 2018: 0.24
Source Normalized Impact per Paper (SNIP) 2018: 0.276

Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 900 267 12
PDF Downloads 766 225 13