發(fā)布時(shí)間：2018-08-28 06:57

【摘要】：隨著社會(huì)主義新農(nóng)村建設(shè)的全面推進(jìn)和金融體制改革的不斷深化,在農(nóng)業(yè)農(nóng)村經(jīng)濟(jì)中起著扶持、補(bǔ)充和引導(dǎo)功能的農(nóng)業(yè)發(fā)展銀行,一方面繼續(xù)完成政府交予的農(nóng)業(yè)政策性金融業(yè)務(wù),形成了以支持國(guó)家糧棉購(gòu)銷(xiāo)儲(chǔ)業(yè)務(wù)為主體,以支持農(nóng)業(yè)產(chǎn)業(yè)化經(jīng)營(yíng)和農(nóng)業(yè)農(nóng)村基礎(chǔ)設(shè)施建設(shè)為兩翼的業(yè)務(wù)發(fā)展格局,另一方面在政策允許范圍內(nèi)自主選擇兼顧社會(huì)效益和經(jīng)濟(jì)效益的商業(yè)性業(yè)務(wù),逐步形成以滿足“三農(nóng)”需要和市場(chǎng)需求為中心的全方位、綜合式服務(wù)。因而農(nóng)業(yè)發(fā)展銀行的信用風(fēng)險(xiǎn)不僅來(lái)自政策層面,還來(lái)自農(nóng)業(yè)農(nóng)村經(jīng)濟(jì)沖擊。選擇合適的模型評(píng)估和預(yù)測(cè)農(nóng)業(yè)發(fā)展銀行信用風(fēng)險(xiǎn),提高農(nóng)業(yè)發(fā)展銀行的風(fēng)險(xiǎn)管理水平顯得尤為重要。本文首先比較分析了諸如KMV、CreditMetrics、CPV等風(fēng)險(xiǎn)度量模型的特點(diǎn)、優(yōu)點(diǎn)和缺點(diǎn),了解各類信用風(fēng)險(xiǎn)度量模型的運(yùn)用環(huán)境,深入分析了壓力測(cè)試的實(shí)施框架,包括別風(fēng)險(xiǎn)因子,構(gòu)建風(fēng)險(xiǎn)傳導(dǎo)模型,選擇合適的壓力情景執(zhí)行壓力測(cè)試,分析壓力測(cè)試報(bào)告。綜合考慮其信用風(fēng)險(xiǎn)的特點(diǎn)和形成原因,最終選擇采用CPV模型對(duì)信用風(fēng)險(xiǎn)執(zhí)行壓力測(cè)。在模型構(gòu)造中,本文篩選出影響農(nóng)業(yè)發(fā)展銀行信用風(fēng)險(xiǎn)的宏觀因子和行業(yè)因子,使用Logit模型將不良貸款率轉(zhuǎn)化為中介指標(biāo)Y,以指標(biāo)Y作為因變量與宏觀和行業(yè)因子進(jìn)行多元線性回歸分析,建立風(fēng)險(xiǎn)傳導(dǎo)模型。通過(guò)自變量的自回歸和殘差項(xiàng)的蒙特卡洛模擬生成壓力情景,進(jìn)行宏觀壓力測(cè)試,定量分析宏觀和行業(yè)因子在中壓情景、強(qiáng)壓情景下對(duì)農(nóng)業(yè)發(fā)展銀行不良貸款率的影響。結(jié)果發(fā)現(xiàn):中央和地方財(cái)政支出增長(zhǎng)率,農(nóng)村居民人均純收入對(duì)農(nóng)發(fā)行不良貸款率的影響是顯著的,特別是農(nóng)村居民人均純收入對(duì)農(nóng)發(fā)行不良貸款率沖擊較強(qiáng)。通過(guò)歷史數(shù)據(jù)對(duì)宏觀因子和行業(yè)因子的自回歸,得出基準(zhǔn)情境下,中央和地方財(cái)政支出增長(zhǎng)率為13.75%,農(nóng)村居民人均純收入為10057.70元,此時(shí)農(nóng)業(yè)發(fā)展銀行的不良貸款率為0.45%,在中央和地方財(cái)政支出增長(zhǎng)率的中壓和強(qiáng)壓沖擊下,農(nóng)業(yè)發(fā)展銀行的不良貸款率分別為0.57%和0.68%。在農(nóng)村居民人均純收入的中壓和強(qiáng)壓沖擊下不良貸款率分別為0.55%和0.78%。最后提出建議:(1)培育先進(jìn)的風(fēng)險(xiǎn)管理文化。(2)完善內(nèi)控機(jī)制。(3)完善信貸審批監(jiān)控。(4)完善信用風(fēng)險(xiǎn)保障補(bǔ)償機(jī)制。(5)完善信用風(fēng)險(xiǎn)配套設(shè)施。
[Abstract]:With the comprehensive development of the new socialist countryside construction and the deepening of the financial system reform, the Agricultural Development Bank, which plays a supporting, supplementary and guiding role in the agricultural rural economy, On the one hand, it continued to complete the agricultural policy-oriented financial business entrusted by the government, forming a business development pattern with supporting the national grain and cotton purchase, marketing and storage business as the main body, and supporting the agricultural industrialization operation and the construction of agricultural rural infrastructure as its two wings. On the other hand, the commercial business with social and economic benefits should be chosen independently within the scope of policy, and a comprehensive and comprehensive service should be formed to meet the needs of agriculture, rural areas and farmers and market demand. Therefore, the credit risk of the Agricultural Development Bank comes not only from the policy level, but also from the agricultural rural economic impact. It is very important to select appropriate models to evaluate and predict the credit risk of the Agricultural Development Bank and to improve the risk management level of the Agricultural Development Bank. In this paper, the characteristics, advantages and disadvantages of risk measurement models such as KMV,CreditMetrics,CPV are compared, and the application environment of various credit risk measurement models is understood. The implementation framework of stress testing, including specific risk factors, is analyzed in depth. Build a risk conduction model, select appropriate stress scenarios to perform stress tests, and analyze stress test reports. Considering the characteristics and reasons of credit risk, CPV model is used to test credit risk. In the model construction, the macro factors and industry factors that affect the credit risk of Agricultural Development Bank are screened out in this paper. The Logit model is used to transform the non-performing loan rate into the intermediary index Y, and the risk conduction model is established by using the index Y as the dependent variable and the macro and industry factors as multiple linear regression analysis. Through the autoregressive of independent variables and Monte Carlo simulation of residual terms to generate stress scenarios, the macro stress test was carried out to quantitatively analyze the impact of macro and industry factors on the non-performing loan ratio of Agricultural Development Bank under the medium pressure scenario and the strong pressure scenario. The results show that the growth rate of central and local fiscal expenditure and the per capita net income of rural residents have a significant impact on the ratio of non-performing loans, especially the impact of rural residents' per capita net income on the ratio of non-performing loans. Through the autoregression of historical data to macro factors and industry factors, it is concluded that the growth rate of central and local fiscal expenditure is 13.75 yuan, and the per capita net income of rural residents is 10057.70 yuan. At this time, the non-performing loan rate of the Agricultural Development Bank is 0.45. Under the impact of the middle and strong pressure of the growth rate of the central and local fiscal expenditure, the non-performing loan rate of the Agricultural Development Bank is 0.57% and 0.68% respectively. The non-performing loan ratio was 0.55% and 0.78% respectively under the impact of medium pressure and strong pressure on the per capita net income of rural residents. Finally, some suggestions are put forward: (1) to cultivate advanced risk management culture, (2) to perfect internal control mechanism, (3) to perfect credit approval and supervision, (4) to perfect credit risk guarantee and compensation mechanism, and (5) to perfect supporting facilities for credit risk.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F832.33

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本文編號(hào)：2208632