商业银行信用风险外文翻译文献

1、商业银行信用风险外文翻译文献 (含：英文原文及中文译文) 英文原文 ESTIMATING THE TECHNICAL AND SCALE EFFICIENCY OF GREEK COMMERCIAL BANKS: THE IMPACT OF REDIT RISK, OFF-BALANCE SHEET ACTIVIES, AND INTERNATIONAL OPERATIONSK Galanopoulos1. IntroductionThe Greek banking sector has undergone major restructuring in recent years. Import

2、ant 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 o

3、f 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 distri

4、bution 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. Furthe

5、rmore, 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 ma

6、rket 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 o

7、n 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

8、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), Dr

9、ake (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 leve l of inefficiency; e.g. banks scrimping on credit evaluations or producing excessively risky loans might be label

10、led 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 Gr

11、eek 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 e

12、ither 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 incre

13、ased 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 in

14、termediation 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 app

15、roach. 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

16、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 dom

17、estic 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 a

18、re 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

19、 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 re

20、sults, 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

21、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. 1980 1984, 1985 1989) has not altered the results. Finally,

22、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 m

23、easure 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 gr

24、owth 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 1993 1998 using homoscedastic and heteroscedastic frontiers. They find an average

25、 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 improve

26、ment 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

27、 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 rela

28、ted 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 chan

29、ge. 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 d

30、ocument 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 studie

31、s, 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 1997 1999, and a positive relationship between size and efficiency.

32、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 th

33、e 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 1982 1997. Both models indicate significant economies of scale and negative annual rates of growth in technical change and in total factor productivit

34、y.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 1982 1992 and 1993 1997 sub-periods, and employs Tobit regression to explain the differences in effici

35、ency 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

36、 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 in

37、dicate 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), d

38、istribution 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 bank

39、s. One of the well-known advantages of DEA, which is relevant to our study, is that it works particularly well with small samp les. 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

40、deterministic DEA, as parametric techniques specify a large number of parameters, m aking 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 n

41、ot 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 detaile

42、d 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 program

43、ming 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

44、of actual input output correspondence set that envelops the input output 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 sampl

45、e. 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 ef

46、ficiency (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 ,

47、 Y, represent the data for all N DMUs. The input oriented measure of a particular DMU, under CRS, is calculated as:Min , , s.t.y i +Y0,xi X 0, 0where1 is the scalar efficient score andis N1 vector of constants. 1 is a constant with high efficiency scalar scores and is N 1 vectors. If = 1 of the bank

48、 is efficient, it is located on the border, and if 1 banks are inefficient and need to enter 1 to reduce the levels to reach the border. Linear programming is solved N times, once in the example, each DMUs and s are obtained as each DMU represents its efficiency score.The banker et al. (1984) sugges

49、ted that firms using returns of scale (VRS) variables decompose the OTE into two component products. The first is the lower VRS technical efficiency or purely technical efficiency (PTE), which involves the ability of managers to use the companys given resources. The second is the scale efficiency (S

50、E), which refers to the use of economies of scale, where to produce front-line exhibits CRS point operations. The CRS linear programming was modified to consider the part of the VRS by adding N1=1, whereN1isaN 1 vectors. All achievements based on VRS are higher than or equal to those obtained by the

51、 lower CRS and SE (ie, SE = OTE/PTE).中文译文评估希腊商业银行效率:信用风险、资产负债表及国际业务作者：K Galanopoulos1引言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

52、 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

53、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

54、。 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

55、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

56、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 DE

57、A 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）使用二阶超额成本函数来首次估计希腊银行业成本的计量经济形式，并调查规模经济。他们使用1980年至1989年间十一家银行的数据，发现尽管运营成本规模经济确实存在，但总成本规模经济却不存在。根据银行规模（即大小银行）和时间段（即1980-1984,1985-1989），数据集参与子样本并未改变结果。最后，结果表明，技术变革在降低平均成本方面没有发挥统计上显着的作用。 Noulas（1997）研究了1991年和1992年在希腊经营的10家私营

58、和10家国有银行的生产率增长情况，用Malmquist生产率指数和DEA来衡量效率。笔者遵循中介方式，发现生产率增长平均约为8，国有银行的增长速度高于私人银行。结果还表明，两种银行的增长来源不同。国有银行的生产率增长是技术进步的结果，而私人银行的增长是效率提高的结果。Christopoulos和Tsionas（2001）使用同方差和异方差边界估计了1993-1998年期间希腊商业银行部门的效率。他们发现异方差模型的平均技术效率约为80，而同方差模型的平均技术效率为83。他们还发现，小型和大型银行的技术和资源配置效率都随着时间的推移而下降。相对于趋势而言，无效率指标的回归表明，小型银行技术和配置

59、低效率的提高相应地相当于19.7和39.1。大银行的相应数字分别为10.4和21.1。 Christopoulos等人（2002）用多输入，多输出灵活的成本函数来检验同一样本，以表示该部门的技术和用于衡量技术效率的异方差边界方法。对各银行特征的效率测算结果的回归表明，大银行的效率低于小银行，经济表现，银行贷款和投资与成本效益正相关。在后面的研究中，Tsionas等人（2003）使用与Christopoulos和Tsionas（2001）和Christopoulos等人相同的样本。 （2002年），但采用DEA来衡量技术和分配效率，以及Malmquist全要素生产率方法来衡量生产率变化。结果表明，大多数银行的运营接近最佳市场惯例，整体效率水平超过95。大型银行似乎比小型银行更有效率，而配置低效率成本似乎比技术无效率成本更重要。他们还记录到一个积极的但并非实质性的技术效率变化，这主要归