دانلود مقاله ISI انگلیسی شماره 9749 + ترجمه فارسی
عنوان فارسی مقاله

تجزیه و تحلیل عملکرد کسب و کار بانکی و ریسک بازار با استفاده از DEA فازی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی
9749 2013 8 صفحه PDF 18 صفحه WORD
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
The analysis of bank business performance and market risk—Applying Fuzzy DEA
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Economic Modelling, 32 (2013) 225–232

فهرست مطالب ترجمه فارسی
چکیده
 مقدمه
بررسی مقالات
روش پژوهش
اندازه گیری فازی مبتنی بر کارایی فوق العاده در DEA
نتایج تجربی
منبع داده ها و داده گردانی
نتایج تجربی
نتیجه
کلمات کلیدی
fi - فازی - بازده در ریسک - شبیه سازی تاریخی - بهره وری بانک
ترجمه چکیده
امروزه با شرایط مالی که مدام در حال تغییر است ، برای اجتناب از بحران تعطیلی، موسسات مالی نگران بهره وری و خطر شدید هستند . بنابراین، بهره وری و مدیریت ریسک ، اهداف مهم برای یک مدیر موسسه مالی هستند . تحلیل پوششی داده ها (DEA) یک رویکرد غیر پارامتری برای ارزیابی عملکرد بهره وری DMU است و متغیرهای مورد استفاده در DEA همه مقادیر دقیقی هستند . با این حال، هنگامی که متغیرهای ورودی یا خروجی فازی باشند ، عملکرد واحدهای تصمیم گیری باید توسط-DEA فازی ادامه یابند . در اساس عدم قطعیت ریسک ، این پژوهش قصد گسترش کاربرد مدل اندازه گیری فازی مبتنی بر اسلک (SBM فازی) دارد
ترجمه مقدمه
به این دلیل که موسسات مالی در سراسر جهان جهانی شدند ، فعالیت های تجاری صنایع مالی همچنان افزایش مییابد. ساختار بازار به دلیل تنوع و نوآوری محصولات موجود پیچیده تر میگردد . بنابراین، ریسک سرمایه گذاری برای موسسات مالی به همین ترتیب افزایش می یابد . با چنین تغییراتی در دولت اقتصادی، بانک ها دیگر لازم نیست تنها نقش واسطه صرفا پولی را ایفا کنند . آنها باید طیف وسیعی از کانال های سرمایه گذاری را به منظور احیای چنین شرایطی توسعه دهند . به هر حال، با داشتن هدف سود و ساخت در ذهن ، بانک ها طبیعتا یا سرمایه گذاری های خود را در محصولاتی با ریسک بالا افزایش می دهد و یا تجارت قوی تر را افزایش میدهند
پیش نمایش مقاله
پیش نمایش مقاله تجزیه و تحلیل عملکرد کسب و کار بانکی و ریسک بازار با استفاده از DEA فازی

چکیده انگلیسی

In the fast changing financial circumstances of nowadays, in avoiding the crisis of closing down, financial institutions are concerned about the efficiency and risk strictly in the meantime. Therefore, efficiency and risk management are goals for a financial institution administrator. Data Envelopment Analysis (DEA) is a non-parameter approach to evaluate the performance of DMU's efficiency and the variables used in the DEA are all accurate values. However, when the input or output variables are fuzzy, the performance of DMUs must proceed by the Fuzzy-DEA. On the basis of risk uncertainty, this research plans to apply the expanding model of Fuzzy Slack-Based Measurement (Fuzzy SBM). The efficiency scores estimated by Fuzzy SBM model are subordinate to functional form, which provides efficiency value region in different degrees of confidence, conforms to the characteristic of risk anticipation, and estimates the management achievement of Taiwan banking under market risk.

مقدمه انگلیسی

As financial institutions around the world become more internationalized and globalized, the trading activities of the financial industry continue to rise. The market structure is further complicated due to the diversity and innovativeness of products available. Therefore, the risk of investment for financial institutions likewise increases. With such changes in the economic state, banks no longer have the sole role of being the purely monetary intermediary. They must now develop a whole range of investment channels in order to survive under such conditions. However, bearing the objective of profit-making in mind, banks will naturally increase their investments in high-risk products or increase leveraged trading, which means that the high potential profits mask the high risks involved and increase the probability of a bank's bankruptcy due to poor management. For this reason, more attention must be paid to the high risks attached to the high potential profits. The topic of Risk Adjusted Performance Measurement has, in recent years, gained increasing awareness and has become more widely discussed as people place more importance on risk management. From the perspective of efficiency measurement, Data Envelopment Analysis (DEA) takes into consideration both inputs andoutputs. The mathematical method therefore provides a fair measurement of efficiency. Since this analytical model was first proposed, it has been widely applied in a whole range of industries. Most studies to date on bank efficiency have focused mainly upon the economies of scale and scope (Berger and Humphrey, 1991; Berger et al., 1987; Hunter and Timme, 1986; McAllister and McManus, 1993), total productivity (Aly et al., 1990; Favero and Papi, 1995; Fukuyama et al., 1999; Grabowski et al., 1993; Schaffnit et al., 1997; Zaim, 1995), and the efficiency effect (Barr et al., 1994; Casu and Molyneux, 2003; Cebenoyan et al., 1993; Chang, 1999; DeYoung and Hasan, 1998; Elyasiani et al., 1994). The fact that increasing importance is gradually being placed on risk management means that more attention is also given to DEA models that include risk in their equations. There are two issues concerning banks' efficiency and risk. One issue treats risk as exogenous in order to analyze efficiency effects (Ataullah et al., 2004; Barr et al., 1994; Berger and DeYoung, 1997; Chang and Chiu, 2006; Cebenoyan et al., 1993; Elyasiani et al., 1994; Pastor, 2002). The above results show that the efficiency level is significantly correlated with the risk indicators. The other issue treats risk as endogenous in order to analyze banks' efficiency (Altunbas et al., 2000; Chang, 1999; Chiu and Chen, 2008; Drake and Hall, 2003; Girardone et al., 2004; Hughes, 1999; Hughes et al., 2001; Mester, 1996; Pastor, 1999). However, the majority of literatures adopt the overdue loan ratio as the substitute variable for risks, which does not reflect the characteristic of uncertainty that risks display. Risk is defined as the presence of the characteristic uncertainty, and the degree of risk varies with the asset value fluctuation and the manager's attitude toward risk. Risk may therefore either bring profit or loss to the asset value. The basic function of capital in thiscontext is to help bear the possible loss incurred by taking risks. The appropriate provision of capital is therefore key to a stable fi- nancial structure, which can help prevent a situation of an inability to make payments. In 2002, the Basel Committee on Banking Supervision (BCBS) proposed the New Basel Capital Accord (Basel II), which sets out guidelines for international banks in terms of taking risks, and therefore, to prevent financial crises. In the section on minimum capital requirement outlined in Basel II, the internal rating uses Value at Risk (VaR) as the basis to estimate the maximum potential loss of the portfolio selection. In simple terms, the VaR ‘uses a single value to represent the maximum potential loss of an investment portfolio during a period of time, with a certain confidence level’. Hence, VaR is a prediction interval that provides different estimates according to the different confidence intervals, and therefore takes into account the characteristic of uncertainty that risks displays. While VaR is widely used to represent the level of risks entailed, the input and output values of the original DEA models are considered crisp values. This is a reoccurring issue encountered when using VaR to estimate the efficiency values of banks. Considering both domestic and foreign literatures, there have been none that have combined these two issues and provided an analytical discussion on the topic. Therefore, this paper seeks to combine the Slack-Based Measure of Efficiency (SBM) as proposed by Tone (2001), with the Fuzzy Measure Theory, and develops the non-radial Fuzzy Slack-Based Measure of Efficiency model (Fuzzy-SBM).

نتیجه گیری انگلیسی

With the increasing frequency of financial disasters and its devastating impact over the recent years, countries around the world have begun to pay much more attention to financial risk management. The fact that the financial industry is the supporting structure to a country's economic. This researchfirstly derives models from the theories, using trigonometry as the basis to develop the Fuzzy-SBM model for the empirical study. Subsequently, banks in Taiwan were used as the sample for the study, with the research carrying out interval estimation of VaR values for use as the input variables. We then used the Fuzzy-SBM model to estimate the upper and lower bounds of the efficiency value for the banks, while varying the a-cut value to represent the effect of risk volatility on the efficiency value. Empirical results from the research show that: 1) The performance of most DMUs varies according to the risk factor. 2) The a-cut value affects the efficiency value, and therefore risk volatility affects the efficiency value. The higher volatility leads to a greater difference between the upper and lower bounds of the efficiency value, while conversely, no volatility in risk means that the efficiency value is fixed. 3) For some DMUs, regardless of the a-cut value, their upper and lower bounds of efficiency value are equal, meaning that risk volatility does not affect their efficiency values and the risk variable does not affect their efficiencies. 4) The risk variable is a factor in the estimation of efficiency values and in the determination of the ranking of efficiencies. The Fuzzy-SBM model derived in this research paper uses trigonometry as the basis to estimate efficiency values as triangular membership functions. This model conforms to the characteristic of forecasting VaR and differs from traditional DEA models in that the results it produces can better demonstrate the implications of risk and the effects of risk on efficiency. The majority of literatures adopt the overdue loan ratio as the substitute variable for risks proxy variable, which does not reflect the characteristic of uncertainty that risks display (Altunbas et al., 2000; Chang, 1999; Chiu and Chen, 2008; Drake and Hall, 2003Girardone et al., 2004; Hughes, 1999; Hughes et al., 2001; Mester, 1996; Pastor, 1999). Thus, the main contribution of this article consists of the utilization of VaR as risk variables to reflect the features of riskfluctuation encountered by banks. Besides, in this article, the efficiency values calculated using the Fuzzy-SMB are membership functions, so theevaluated efficiency value is in fact an interval value with the function of anticipating the prospective efficiency performance. The results obtained differ from that calculated by traditional DEA models as a constant. Data Envelopment Analysis (DEA) is a non-parameter approach to evaluate the performance of DMU's efficiency and the variables used in DEA are all accurate values. However, when the input or output variables are fuzzy, the performance of DMUs must proceed by the Fuzzy-DEA. The limitations of this study consist in the risk assessment method suggested by Basel II. The method is able to produce the approximate estimations of the VaR of the banks in Taiwan; nevertheless, influenced by the limitations of incomplete data, this study simply can evaluate a part of the risk conditions. In addition, due to the different financial environment in each nation and the great diversity of banks' investment deployment, the situations of Taiwanese banks may not reflect the actual financial environment in other countries. This article, therefore, provides a more applicable DEA assessment method for reference.

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