the greek banking case

436 Pages·2014·4.04 MB·English

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CARDIFF UNIVERSITY

ESSAYS ON EFFICIENCY
AND PRODUCTIVITY: THE
GREEK BANKING CASE
by Panagiotis Tziogkidis
A Thesis Submitted in Fulfilment of the Requirements for the Degree
of Doctor of Philosophy of Cardiff University
2014
E C O N O M I C S D E P A R T M E N T , C A R D I F F B U S I N E S S S C H O O L
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DECLARATION

This work has not previously been accepted in substance for any degree and
is not concurrently submitted in candidature for any degree.

Signed …………………………………………………………. (candidate)

Date …………………………

STATEMENT 1

This thesis is being submitted in partial fulfillment of the requirements for the degree of
…………………………(insert MCh, Md, MPhil, PhD etc, as appropriate)

Signed …………………………………………………………. (candidate)

Date …………………………

STATEMENT 2

This thesis is the result of my own independent work/investigation, except where otherwise
stated.
Other sources are acknowledged by footnotes giving explicit references.

Signed …………………………………………………………. (candidate)

Date …………………………

STATEMENT 3

I hereby give consent for my thesis, if accepted, to be available for photocopying and for inter-
library loan, and for the title and summary to be made available to outside organisations.

Signed …………………………………………………………. (candidate)

Date …………………………
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Abstract

Bootstrap DEA is a valuable tool for gauging the sensitivity of DEA scores towards
sampling variations, hence allowing for statistical inference. However, it is associated
with generous assumptions while evidence on its performance is limited. This thesis
begins with the evaluation of the performance of bootstrap DEA in small samples
through a variety of Monte Carlo simulations. The results indicate cases under which
bootstrap DEA may underperform and it shown how the violation of the fundamental
assumption of equal bootstrap and DEA biases may affect confidence intervals and
cause the evidenced underperformance. An alternative approach, which utilises the
Pearson system random number generator, seems to perform well towards this
respect. In particular, coverage probabilities converge to the nominal ones for samples
as small as 120 observations and the bootstrap biases are very close to the DEA ones.
In the presence of technological heterogeneity, though, poor performance is observed
in all cases, which is not surprising as even the applicability of simple DEA is
questionable.

Using an illustrative example from the deregulation of the Greek banking sector during
late 80s, potential differences arising from the various approaches are discussed. In
particular, the theoretical explorations are extended to the case of the Global
Malmquist productivity index, which is used to examine the productivity change of
Greek banks during (de)regulation. Some differences are observed on the magnitudes
of the estimated quantities of interest and on the probability masses at the tails of the
relevant bootstrap distributions. Qualitatively, though, the overall conclusions are very
similar; the provision of commercial freedoms enhanced the productivity of
commercial banks whereas the imposition of prudential controls had the opposite
effect. This result is of topical interest as the European Supervisory Mechanism, which
recently assumed duties, will closely supervise “significant institutions” which includes
the 4 biggest Greek banks and their banking subsidiaries.

Keywords: efficiency, productivity, DEA, bootstrap DEA, Global Malmquist index, hypothesis
testing, Monte Carlo simulations, banking, deregulation

JEL Classifications: C12, C14, C15, C61, C67, G21, G28, L25
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Acknowledgements

I would like to thank my supervisors Prof Kent Matthews and Prof Patrick
Minford for their support and guidance throughout the PhD, as well as
the two examiners. Their comments have certainly improved the
structure and the content of the thesis. I would also like to thank the staff
and my colleagues at Cardiff Business School for their useful comments
on my work presented at Cardiff Economics PhD Workshops. Research
funding from the Economic and Social Research Council (ESRC), Cardiff
Business School and the Julian Hodge Institute of Applied
Macroeconomics is gratefully acknowledged.

I have also benefited from comments from various participants at the
International Data Envelopment Analysis Society Conference
(Thessaloniki, 2011), the Quantitative Economics Doctorate Meeting
(Copenhagen, 2011), the Financial Engineering and Banking Society
Conference (Paris, 2013) and the European Workshop on Efficiency and
Productivity Analysis Workshop (Helsinki, 2013). I would particularly like
to thank Prof Leopold Simar and Prof Paul Wilson for their constructive
feedback on my paper presented at the EWEPA conference. Their
comments have significantly improved the quality of my work while their
encouragement for my future research plans is deeply appreciated.
Moreover, Prof Mike Tsionas, Prof Robin Sickles and Prof Kris Kerstens
have also provided useful suggestions for my work. Thanks also go to Dr
Yiannis Kouropalatis who has advised me on presentational and other
peripheral, though important, aspects of my work.

Special thanks also go to Prof Costas Siriopoulos who has been my mentor
since my undergraduate studies at the University of Patras and who has
also provided valuable comments and useful advice during my PhD. Last
but not least, I would like to thank my family for supporting me all these
years.

My warmest gratitude, though, is reserved for my partner, Anna Ziouti,
who has been patient, caring and understanding throughout my PhD life
and to whom I devote this work.
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List of Abbreviations

AEC: Adjusted Efficiency Change
AIC: Akaike information criterion
AMISE: Asymptotic Mean Integrated Square Error
ASE: Athens Stock Exchange
BCV: Biased Cross-Validation
CRS: Constant Returns to Scale
DEA: Data Envelopment Analysis
DGP: Data Generating Process
DMU: Decision Making Unit
DRS: Decreasing Returns to Scale
FDH: Free Disposable Hull
GAS: Greek Accounting Standards
IAS: International Accounting Standards
IRS: Increasing Returns to Scale
ISE: Integrated Square Error
LCV: Likelihood Cross-Validation
LSCV: Least Squares Cross-Validation
M&As: Mergers and Acquisitions
MISE: Mean Integrated Square Error
MPSS: Most Productive Scale Size
OLS: Ordinary Least Squares
RTS: Returns to Scale
SFA: Stochastic Frontier Analysis
SJPI or SJ: Sheather-Jones Plug-In method
SW1998: Simar and Wilson’s (1998) confidence intervals
SW2000: Simar and Wilson’s (2000a) confidence intervals
TFA: Thick Frontier Approach
VRS: Variable Returns to Scale
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Contents

1 Introduction ............................................................................................................... 19
1.1 Purpose of study and preliminary results .......................................................... 21
1.2 Why Greece? ...................................................................................................... 24
1.3 Motivation and contribution .............................................................................. 27
1.4 Structure of the thesis ........................................................................................ 28
2 Small Samples and Bootstrap DEA: a Monte Carlo Analysis ..................................... 30
2.1 Introduction........................................................................................................ 30
2.2 General concepts................................................................................................ 35
2.3 Theoretical foundations ..................................................................................... 39
2.4 Estimation of technical efficiency ...................................................................... 44
2.4.1 Parametric approaches ............................................................................... 44
2.4.2 Non-parametric approaches ....................................................................... 46
2.4.3 Data envelopment analysis ......................................................................... 46
2.4.4 The DEA “estimators” ................................................................................. 51
2.5 General information about the bootstrap ......................................................... 53
2.6 Bootstrapping DEA efficiency scores ................................................................. 56
2.6.1 Bootstrap DEA: a practical consideration ................................................... 57
2.6.2 The Simar and Wilson’s (1998) bootstrap DEA algorithm .......................... 59
2.6.3 Bootstrap DEA: statistical inference and confidence intervals .................. 65
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2.6.4 On smoothing the empirical distribution ................................................... 68
2.6.5 Developments and extensions .................................................................... 74
2.7 Monte Carlo simulations and previous results on bootstrap DEA ..................... 77
2.8 The Monte Carlo experiments ........................................................................... 82
2.8.1 The experiment outline .............................................................................. 82
2.8.2 The data generating process ....................................................................... 86
2.8.3 The economic interpretation of the DGPs .................................................. 90
2.8.4 Defining the fixed DMU .............................................................................. 92
2.8.5 Performing Monte Carlo simulations and associated issues ...................... 99
2.9 Monte Carlo Results: small samples ................................................................ 104
2.9.1 Identifying the population DGP from the data ......................................... 104
2.9.2 Bootstrap and DEA biases ......................................................................... 109
2.9.3 Coverage probabilities .............................................................................. 115
2.9.4 Bootstrap confidence intervals ................................................................. 120
2.9.5 Bootstrap distributions ............................................................................. 126
2.10 Monte Carlo Results: large samples ................................................................. 131
2.11 Conclusions....................................................................................................... 135
3 Testing hypotheses with bootstrap DEA ................................................................. 139
3.1 Introduction...................................................................................................... 139
3.2 Simar and Wilson’s intervals and implied tests ............................................... 142
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3.2.1 Simar and Wilson’s (1998) intervals ......................................................... 143
3.2.2 Simar and Wilson’s (1998) implied tests .................................................. 145
3.2.3 Simar and Wilson’s (2000a) intervals ....................................................... 147
3.2.4 Simar and Wilson’s (2000a) implied tests ................................................ 149
3.3 Considerations and limitations ........................................................................ 150
3.3.1 Dealing with skewness .............................................................................. 151
3.3.2 Same-sample comparisons ....................................................................... 153
3.3.3 Cross-sample comparisons ....................................................................... 156
3.4 Can we “bypass” the issue of unequal biases? ................................................ 157
3.5 On testing returns to scale ............................................................................... 163
3.5.1 Measuring RTS in DEA ............................................................................... 163
3.5.2 Simar and Wilson’s (2002) approach of testing RTS ................................. 164
3.5.3 A proposed approach for testing RTS ....................................................... 168
3.6 Conclusions....................................................................................................... 174
4 A simple alternative to smoothing .......................................................................... 178
4.1 Introduction...................................................................................................... 178
4.2 Why use moments? .......................................................................................... 181
4.3 Method of moments ........................................................................................ 183
4.4 Pearson system random number generator .................................................... 184
4.5 The moments-bootstrap DEA ........................................................................... 188
4.6 Monte Carlo evidence ...................................................................................... 190
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4.6.1 Population, sample and bootstrap moments ........................................... 190
4.6.2 Bootstrap and DEA biases ......................................................................... 193
4.6.3 Coverage probabilities - Small samples .................................................... 196
4.6.4 Confidence intervals ................................................................................. 198
4.7 Conclusions....................................................................................................... 202
5 Suggested guidelines on applying bootstrap DEA ................................................... 206
5.1 Assumptions ..................................................................................................... 206
5.2 Applying bootstrap DEA ................................................................................... 206
5.2.1 Step 1: Identify the underlying population ............................................... 207
5.2.2 Step 2: Enrich the empirical distribution .................................................. 209
5.2.3 Step 3: Apply the bootstrap ...................................................................... 210
5.3 Testing hypotheses........................................................................................... 211
5.3.1 Step 1: Define the null .............................................................................. 211
5.3.2 Step 2: Define the test statistic ................................................................. 212
5.3.3 Step 3: Confidence intervals and p-values ................................................ 212
5.3.4 Step 4: Accept or reject the null ............................................................... 213
6 An illustrative example: the Greek banking case .................................................... 215
6.1 Introduction...................................................................................................... 215
6.2 Contextual background .................................................................................... 218
6.3 Literature Review ............................................................................................. 224