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.估计技术和规模的希腊商业银行效率：信用风险、 资产负债表的活动和国际业务的影响 原文出处及作者：巴斯大学管理学院2007年硕士毕业论文，作者Fotios Pasiouras1.介绍希腊银行业经历了近几年重大的结构调整。重要的结构性、政策和环境的变化经常强调的学者和从业人员有欧盟单一市场的建立，欧元的介绍，国际化的竞争、利率自由化、放松管制和最近的兼并和收购浪潮。希腊的银行业也经历了相当大的改善，通信和计算技术，因为银行有扩张和现代化其分销网络，其中除了传统的分支机构和自动取款机，现在包括网上银行等替代分销渠道。作为希腊银行（2004 年）的年度报告的重点，希腊银行亦在升级其信用风险测量与管理系统，通过引入信用评分和概率默认模型近年来采取的主要步骤。此外，他们扩展他们的产品/服务组合，包括保险、 经纪业务和资产管理等活动，同时也增加了他们的资产负债表操作和非利息收入。最后，专注于巴尔干地区（如阿尔巴尼亚、保加利亚、前南斯拉夫马其顿共和国、罗马尼亚、塞尔维亚）的更广泛市场的全球化增加的趋势已添加到希腊银行在塞浦路斯和美国以前有限的国际活动。在国外经营的子公司的业绩预计将有父的银行，从而对未来的决定为进一步国际化的尝试对性能的影响。本研究的目的是要运用数据包络分析（DEA）和重新效率的希腊银行部门，同时考虑到几个以上讨论的问题进行调查。我们因此区分我们的论文从以前的希腊银行产业重点并在几个方面，下面讨论添加的见解。首先，我们第一次对效率的希腊银行的信用风险的影响通过检查其中包括贷款损失准备金作为附加输入Charnes et al.(1990 年)、 德雷克（2001 年）、 德雷克和大厅 (2003 年)，和德雷克等人（2006 年）。作为美斯特 (1996) 点出除非质量和风险控制的一个人也许会很容易误判一家银行的水平的低效 ；例如精打细算的银行信用评价或生产过高风险的贷款可能会被贴上标签一样高效，当相比银行花资源，以确保它们的贷款有较高的质量(p.1026)。我们估计效率的银行和无此输入调整为不同的信用风险水平和对效率的影响。第二，以往的研究中，希腊银行业，我们考虑资产负债表活动期间估计的效率得分。几个最近的研究审查效率的 DEA 或随机前沿技术的银行，承认银行在非传统的活动中更多地参与，包括任何非利息 （即费） 收入 （e.g. Lang和Welzel，1998年；德雷克，2001 年；托尔托萨Ausina，2003年） 或资产负债表项目（例如阿尔通巴什等人，2001 年 ；阿尔通巴什和查克，2001年；架和 Hassan，2003a、 b ；Bos 和 Colari，2005 年 ；饶，2005年） 作为额外的输出。然而，尽管他们希腊银行的重要性上升，这种活动没有被考虑在过去。再次，我们估计，银行的效率在我们的示例与无负债表外活动，以观察是否它将会对效率有影响。第三，我们比较所得的中介方法随之而来的银行的效率与利润导向的做法，最近在 dea 方法，提出了由德雷克等人（2006 年），在他们随机前沿方法的上下文中杰和美斯特 （2003 年） 的做法是一致的最新研究的结果。这使我们能够观察是否不同的输入/输出定义影响效率分数。第四，我们比较效率得分的希腊银行，扩大了其海外的业务（即国际希腊银行，以下简称 IGBs），与那些希腊银行的业务在国内市场都有限的（即纯粹的国内银行，以下简称 Pdb）。为了最好的我们的知识，没有研究开展了这种分析对于希腊。然而，在土耳其银行业的研究中，Isik和Hassan（2002 年）发现的证据，跨国公司的国内银行均优于纯粹国内银行的所有提高效率的措施（即成本效率、资源配置效率、技术效率、纯技术效率）除了规模效率。从我们的研究得出的结论可能是有用的希腊银行或其他正在考虑他们的业务的国际化的中型银行部门的经理。第五，我们运行回归来解释银行效率的一直在希腊 （赫里斯托普洛斯等人，2002年；Rezitis，2006年）。但是，在我们的例子中我们检查最近一段时间，遵循上文所述的许多变化。本文的其余部分是，如下所示：第2节文献侧重于希腊银行部门的效率。第 3 节规定 DEA 的简短的讨论。第4节给的数据和变量。第 5 节讨论实证分析的结果，并节 6 总结研究。2.文献综述Karafolas和Mantakas (1996) 使用二阶超越对数成本函数估计（第一次）在希腊银行部门的费用的一种计量形式和调查的规模经济。十一银行从 1980年至 1989 年期间使用的数据，他们发现虽然经营成本规模经济确实存在，但总成本规模经济并不存在。由银行的大小（即大、小银行）和时间段的子样本数据集的参与 (即1980年1984 年，1985年-1989 年) 并没有改变结果。最后，结果表明技术变革中，降低平均成本不发挥了统计学意义的作用。Noulas（1997 年）检查生产率增长的十个私营和十个国有银行经营在希腊在 1991 年和 1992 年，期间使用的Malmquist生产率指数和 DEA 测量效率。作者遵循调解方法，并发现生产率平均增长 8%左右，与国有银行表现出较高的增长比私人的。结果还表明增长的来源不同跨银行的两种类型。国有银行生产率增长是进步的由于技术，而私人银行的增长是进步的提高效率的结果。赫里斯托普洛斯和Tsionas (2001)在1993年1998年期间估计在希腊的商业银行业效率使用同方差与异方差性的前沿。他们发现平均技术效率约 80%的异方差模型和总体平均值的分布之一的 83%。他们还发现技术和资源配置低效率降低随时间较小，以及较大的银行。回归的低效率措施反对趋势指示在技术和资源配置效率低下的小改进银行同等 19.7%和39.1%，因此，大型银行的相应数字是10.4%和21.1%。赫里斯托普洛斯（2002 年）检查同一个多输入、多输出的柔性成本函数代表部门和差异方差前沿方法来测量技术效率的技术相同的样本。提高效率的措施对银行的各种特性的回归表示较大的银行都是比较小的效率较低和经济绩效、银行贷款和投资都呈正相关，成本效率。在后者的研究中，Tsionas et al.(2003) 使用赫里斯托普洛斯和Tsionas（2001 年）和赫里斯托普洛斯 et al.（2002 年） 相同的样本，但雇用DEA测量技术和资源配置效率和 Malmquist 全要素生产率方法来衡量生产力的变化。结果表明，大多数银行经营接近最佳的市场实践与整体效率水平达到 95%以上。较大的银行似乎比较小的效率更高，而资源配置低效率成本似乎要比技术效率低成本更重要。他们还记录正面但不是坚固的技术效率变化的主要原因是生产效率的提高，为中等规模的银行和大型银行的技术变化改进。Halkos 和Salamouris（2004 年）也使用 DEA 但按照不同的方法，对比以往的研究，通过使用财务比率作为输出和没有输入的措施。根据正在审议的今年15和 18 银行之间的样本范围。结果表明在1997 年1999 年期间平均效率宽变化与大小和效率之间的积极关系。此外，还有非系统的关系之间通过私有化公共银行的所有权转让和最后一期的性能。Apergis 和 Rezitis (2004) 指定超越对数成本函数的全要素生产率分析希腊银行部门、 技术变化率和增长率的成本结构。19821997年期间，他们使用中介和生产方法和样本的六家银行。这两个模型表明，重要的规模经济和技术变化和全要素生产率增长的负年度利率。Rezitis（2006年）使用相同的数据集，但运用的Malmquist生产率指数和 DEA 测度与分解生产力的增长和技术效率，分别。他还比较 1982年1992 年和 1993年1997 年的分时段，并雇用 Tobit回归来解释银行间效率上的差异。结果表明，总体技术效率的平均水平为91.3%，而生产率增长的整个期间平均上升2.4%。生产力的增长在二子期较高，归因于技术进步与效率是主要驱动力，直到1992年中的改进。此外，在第二次分时段纯效率较高，和规模效率较低，表明虽然银行取得较高的纯技术效率，但他们搬离最优规模。回归结果表明大小和专业化纯两方面产生积极的影响和规模效率。3.研究方法从方法论的角度来看，有几种方法可以用于检查的银行，如随机前沿分析(SFA)、厚厚的前沿方法 (TFA)、自由的分配办法（DFA）和DEA 效率。Et al.伯杰（1993 年），伯杰和汉弗莱 (1997年) 和戈达德等人（2001年）提供关键讨论和比较这些方法在银行业的上下文中。在本研究中，以下几个最近的研究我们使用 DEA 估计银行的效率。Dea方法，这是有关对我们的学习，知名的优点之一是它特别好与小样本工程。作为Maudos et al.(2002 年) 指出的那样，所有的技术测量效率，需要观测的最小数目的那个是的非参数和确定性的 DEA，作为参数技术指定大量的参数，使它有必要可用很大数量的观测。(p.511）。其他的 DEA 的优点是它不需要任何的假设做出关于分布的低效率，它不需要特定功能窗体上的数据在确定最有效决策单元 (动车组)。另一方面，DEA 的缺点是它假定数据是免费的测量误差，这是敏感的异常值。我们只简要的勾勒 DEA 在这里，而更详细和技术的讨论可以发现在Coelli et al.(1999 年)、库珀等人（2000年）和Thanassoulis（2001年）。通过下面的符号是那些用于Coelli（1996 年）和Coelli et al.(1999年)，由于我们使用他们的电脑程序深 2.1 估计效率得分。DEA 是使用线性规划法的生产前沿发展和的测量效率相对发达的前沿 （Charnes 等人，1978 年）。通过分段线性组合的样品（Thanassoulis，2001年）中的所有决策单元的输入输出对应的实际输入输出对应集构造决策单元 (动车组)，在我们的案例银行，样品的最佳实践生产前沿。每个 DMU 被分配一个范围0 和 1 之间，与分数等于 1 指示针对其余部分动车组在样品中的有效决策单元的效率得分。DEA 可以由假设（CRS）规模收益不变或变量返回到规模（VRS）执行。在他们的开创性研究，Charnes et al.(1978) 提出了模型输入的方向，并假定CRS。因此，此模型的输出是一个指示每个DMU的下CRS的总体技术效率 (OTE) 的分数。在更多的技术术语讨论 DEA，让我们假设是有 K 的输入数据和 M 输出每个决策单元 N （即银行） 上。为 ith DMU 它们都分别由向量xi和yi表示。K N 输入的矩阵、 X 和 Y，M N 输出矩阵表示的数据的所有 N 内燃动车组。特定的 DMU，CRS 下的输入为导向的测量计算如下：Min, s.t.yi +Y0,xi X0, 01 是高效率的标量得分和is N 1 向量的常数。如果 = 的 1 银行是高效，它位于边境上，而 if1 银行是低效的需要输入 1 减少各级以到达边境。线性规划是解决 N倍，一次在示例中，每个DMU的和的值获取为每个DMU代表其效率得分。银行家et al.（1984 年）建议使用规模（VRS）变量返回的公司将OTE分解为两个组件产品。第一次是下 VRS 技术效率或纯技术效率 (PTE)，涉及的管理者利用企业的给定的资源的能力。第二是规模效率 （SE），指的是利用规模经济，在哪里生产前沿展品 CRS 点经营。CRS 线性规划修改，以考虑VRS通过添加N1=1，whereN1isaN1向量的部分。根据VRS取得的成绩都高于或等于那些得到下 CRS 和 SE 的技术效率可以得到（即 SE = OTE/PTE）。ESTIMATING THE TECHNICAL AND SCALE EFFICIENCY OF GREEK COMMERCIAL BANKS: THE IMPACT OF REDIT RISK, OFF-BALANCE SHEET ACTIVIES, AND INTERNATIONAL OPERATIONS1. IntroductionThe Greek banking sector has undergone major restructuring in recent years. Important structural, policy and environmental changes that are frequently highlighted by both academics and practitioners are the establishment of the single EU market, the introduction of the euro, the internationalization of competition, interest rate liberalization, deregulation, and the recent wave of mergers and acquisitions.The Greek banking sector has also experienced considerable improvements in terms of communication and computing technology, as banks have expanded and modernized their distribution networks, which apart from the traditional branches and ATMs, now include alternative distribution channels such as internet banking. As the Annual Report of the Bank of Greece (2004) highlights, Greek banks have also taken major steps in recent years towards upgrading their credit risk measurement and management systems, by introducing credit scoring and probability default models. Furthermore, they have expanded their product/service portfolio to include activities such as insurance, brokerage and asset management, and at the same time increased their off-balance sheet operations and non-interest income.Finally, the increased trend towards globalization that focused on the wider market of the Balkans (e.g. Albania, Bulgaria, FYROM, Romania, Serbia) has added to the previously limited international activities of Greek banks in Cyprus and USA. The performance of the subsidiaries operating abroad is expected to have an impact on the performance of parent banks and consequently on future decisions for further internationalization attempts.The purpose of the present study is to employ data envelopment analysis (DEA) and reinvestigate the efficiency of the Greek banking sector, while considering several of the issues discussed above. We therefore differentiate our paper from previous ones that focus on the Greek banking industry and add insights in several respects, discussed below.First of all, we examine for the first time the impact of credit risk on the efficiency of Greek banks by including loan loss provisions as an additional input as in Charnes et al. (1990), Drake (2001), Drake and Hall (2003), and Drake et al. (2006)among others. As Mester (1996)points out “Unless quality and risk are controlled for, one might easily miscalculate a banks level of inefficiency; e.g. banks scrimping on credit evaluations or producing excessively risky loans might be labelled as efficient when compared to banks spending resources to ensure their loans are of higher quality” (p. 1026). We estimate the efficiency of banks with and without this input to adjust for different credit risk levels and examine its impact on efficiency.Second, unlike previous studies in the Greek banking sector, we consider off-balance sheet activities during the estimation of efficiency scores. Several recent studies that examine the efficiency of banks, with DEA or stochastic frontier techniques, acknowledge the increased involvement of banks in non-traditional activities and include either non-interest (i.e. fee) income (e.g.Lang and Welzel, 1998; Drake, 2001; Tortosa-Ausina, 2003) or off-balance sheet items (e.g. Altunbas et al., 2001; Altunbas and Chakravarty, 2001; Isik and Hassan, 2003a,b; Bos and Colari, 2005; Rao, 2005) as an additional output. However, despite their increased importance for Greek banks, such activities have not been considered in the past. Again, we estimate the efficiency of the banks in our sample with and without off-balance sheet activities to observe whether it will have an impact on efficiency.Third, we compare the results obtained from the intermediation approach that has been followed in most recent studies of banks efficiency with a profit-oriented approach that was recently proposed by Drake et al. (2006)in the context of DEA, and is in line with the approach of Berger and Mester (2003)in the context of their stochastic frontier approach. This allows us to observe if different input/output definitions affect efficiency scores.Fourth, we compare the efficiency scores of Greek banks that have expanded their operations abroad (i.e. international Greek banks, hereafter IGBs), with those of Greek banks whose operations are limited in the domestic market (i.e. purely domestic banks, hereafter PDBs). To the best of our knowledge, no study has undertaken such an analysis for Greece. However, in a study of the Turkish banking sector, Isik and Hassan (2002)found evidence that multinational domestic banks are superior to purely domestic banks in terms of all efficiency measures (i.e. cost efficiency, allocative efficiency, technical efficiency, pure technical efficiency) except for scale efficiency. The conclusions drawn from our study could be useful to the managers of Greek banks or other medium-sized banking sectors that are considering the internationalization of their operations.Fifth, we run regressions to explain the efficiency of banks, an approach that has been followed in only two of the past studies in Greece (Christopoulos et al., 2002; Rezitis, 2006). However, in our case we examine a most recent period that follows the numerous changes outlined above. The rest of the paper is as follows: Section 2 reviews the literature that focuses on the efficiency of the Greek banking sector. Section 3 provides a brief discussion of DEA. Section 4 presents the data and variables. Section 5 discusses the empirical results, and Section 6 concludes the study.2. Literature reviewsKarafolas and Mantakas (1996)use a second-order translog cost function to estimate (for the first time) an econometric form of the costs in the Greek banking sector and investigate economies of scale. Using data for eleven banks from the period 1980 to 1989, they find that although operating-cost scale economies do exist, total cost scale economies are not present. Participation of the dataset in sub-samples by banks size (i.e. large and small banks) and time periods (i.e. 19801984, 19851989) has not altered the results. Finally, the results indicate that technical change has not played a statistically significant role in the reduction of average cost.Noulas (1997) examines the productivity growth of ten private and ten state banks operating in Greece during 1991 and 1992, using the Malmquist productivity index and DEA to measure efficiency. The author follows the intermediation approach and finds that productivity growth averaged about 8%, with state banks showing higher growth than private ones. The results also indicate that the sources of the growth differ across the two types of banks. State banks productivity growth is a result of technological progress, while private banks growth is a result of increased efficiency.Christopoulos and Tsionas (2001) estimate the efficiency in the Greek commercial banking sector over the period 19931998 using homoscedastic and heteroscedastic frontiers. They find an average technical efficiency about 80% for the heteroscedastic model and 83% for the homoscedastic one. They also find that both technical and allocative inefficiencies decrease over time for smaller as well as larger banks. The regression of inefficiency measures against a trend indicates that the improvement in technical and allocative inefficiencies for small banks equal 19.7% and 39.1%, accordingly. The corresponding figures for large banks are 10.4% and 21.1%.Christopoulos et al. (2002)examine the same sample with a multi-input, multi-output flexible cost function to represent the technology of the sector and a heteroscedastic frontier approach to measure technical efficiency. Regression of the efficiency measures over various bank characteristics indicates that larger banks are less efficient than smaller ones, and that economic performance, bank loans and investments are positively related to cost efficiency.In a latter study, Tsionas et al. (2003) use the same sample as in Christopoulos and Tsionas (2001) and Christopoulos et al. (2002) but employ DEA to measure technical and allocative efficiency, and the Malmquist total factor productivity approach to measure productivity change. The results indicate that most of the banks operate close to the best market practices with overall efficiency levels over 95%. Larger banks appear to be more efficient than smaller ones, while allocative inefficiency costs seem to be more important than technical inefficiency costs. They also document a positive but not substantial technical efficiency change which is mainly attributed to efficiency improvement for medium-sized banks and to technical change improvement for large banks.Halkos and Salamouris (2004) also use DEA but follow a different approach, in contrast to previous studies, by using financial ratios as output measures and no input measures. The sample ranges between 15 and 18 banks depending on the year under consideration. The results indicate a wide variation in average efficiency over the period 19971999, and a positive relationship between size and efficiency. Furthermore, there is non-systematic relationship between transfer of ownership through privatization of public banks and last periods performance. Apergis and Rezitis (2004)specify a translog cost function to analyze the cost structure of the Greek banking sector, the rate of technical change and the rate of growth in total factor productivity. They use both the intermediation and the production approach and a sample of six banks over the period 19821997. Both models indicate significant economies of scale and negative annual rates of growth in technical change and in total factor productivity.Rezitis (2006) uses the same dataset but employs the Malmquist productivity index and DEA to measure and decompose productivity growth and technical efficiency, respectively. He also compares the 19821992 and 19931997 sub-periods, and employs Tobit regression to explain the differences in efficiency among banks. The results indicate that the average level of overall technical efficiency is 91.3%, while productivity growth increased on average by 2.4% over the entire period. The growth in productivity is higher in the second sub-period and is attributed to technical progress, in contrast to improvements in efficiency that was the main driver until 1992. Furthermore, during the second sub-period pure efficiency is higher, and scale efficiency is lower, indicating that although banks achieved higher pure technical efficiency, they moved away from optimal scale. The regression results indicate that size and specialization have a positive impact on both pure and scale efficiency.3. MethodologyFrom a methodological perspective, there are several approaches that can be used to examine the efficiency of banks, such as stochastic frontier analysis (SFA), thick frontier approach (TFA), distribution free approach (DFA), and DEA. Berger et al. (1993), Berger and Humphrey (1997) and Goddard et al. (2001) provide key discussions and comparisons of these methods in the context of banking.In the present study, following several recent studies we use DEA to estimate the efficiency of banks. One of the well-known advantages of DEA, which is relevant to our study, is that it works particularly well with small samples. As Maudos et al. (2002) point out, “Of all the techniques for measuring efficiency, the one that requires the smallest number of observations is the non-parametric and deterministic DEA, as parametric techniques specify a large number of parameters, making it necessary to have available a large number of observations.” (p. 511). Other advantages of DEA are that it does not require any assumption to be made about the distribution of inefficiency and that it does not require a particular functional form on the data in determining the most efficient decision making units (DMUs). On the other hand, the shortcomings of DEA are that it assumes data to be free of measurement error and it is sensitive to outliers. We only briefly outline DEA here, while more detailed and technical discussions can be found in Coelli et al. (1999), Cooper et al. (2000) and Thanassoulis (2001). The notations adopted below are those used in Coelli (1996) and Coelli et al. (1999), since we use their computer program DEAP 2.1 to estimate the efficiency scores.DEA uses linear programming for the development of production frontiers and the measurement of efficiency relative to the developed frontiers (Charnes et al., 1978). The best-practice production frontier for a sample of decision making units (DMUs), in our case banks, is constructed through a piecewise linear combination of actual inputoutput correspondence set that envelops the inputoutput correspondence of all DMUs in the sample (Thanassoulis, 2001). Each DMU is assigned an efficiency score that ranges between 0 and 1, with a score equal to 1 indicating an efficient DMU with respect to the rest DMUs in the sample.DEA can be implemented by assuming either constant returns to scale (CRS) or variable returns to scale (VRS). In their seminal study, Charnes et al. (1978)proposed a model that had an input orientation and assumed CRS. Hence, the output of this model is a score indicating the overall technical efficiency (OTE) of each DMU under CRS.To discuss DEA in more technical terms, let us assume that there is data on K inputs and M outputs on each of N DMUs (i.e. banks). For the ith DMU these are represented by the vectors xi and yi, respectively. The K N input matrix , X , and the M N output matrix , Y, represent the data for all N DMUs. The input oriented measure of a particular DMU, under CRS, is calculated as:Min, s.t.yi +Y0,xi X0, 0where1 is the scalar efficient score andis N1 vector of constants. If= 1 the bank is efficient as it lies on the frontier, whereas if1 the bank is inefficient and needs a 1 reduction in the inputs levels to reach the frontier. The linear programming is solved N times, once for each DMU in sample, and a value of is obtained for each DMU representing its efficiency score. Banker et al. (1984) suggested the use of variable returns to scale (VRS) that decomposes OTE into a product of two components. The first is technical efficiency under VRS or pure technical efficiency (PTE) and relates to the ability of managers to utilize firms given resources. The second is scale efficiency (SE) and refers to exploiting scale economies by operating at a point where the production frontier exhibits CRS. The CRS linear programming is modified to consider VRS by adding the convexityN1= 1, whereN1isaN1 vector of ones. The technical efficiency scores obtained under VRS are higher than or equal to those obtained under CRS and SE can be obtained by dividing OTE with PTE (i.e. SE = OTE/P