發(fā)布時(shí)間：2018-01-15 13:46

  本文關(guān)鍵詞：基于Wilson模型的我國(guó)商業(yè)銀行信用風(fēng)險(xiǎn)壓力測(cè)試實(shí)證研究　出處：《山東大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 宏觀經(jīng)濟(jì) 壓力測(cè)試 信用風(fēng)險(xiǎn) Wilson模型 情景分析法

【摘要】：當(dāng)前,中國(guó)銀行業(yè)整體資產(chǎn)狀況面臨著巨大的下降風(fēng)險(xiǎn),主要是近年來(lái)經(jīng)濟(jì)增速放緩和實(shí)體經(jīng)濟(jì)疲軟等原因?qū)е?這體現(xiàn)出銀行資產(chǎn)質(zhì)量與經(jīng)濟(jì)周期變動(dòng)有著莫大的關(guān)聯(lián)。雖然與世界各國(guó)銀行業(yè)相比,我國(guó)整體不良貸款率長(zhǎng)期保持較低水平,但隨著我國(guó)不斷深化經(jīng)濟(jì)改革,在產(chǎn)業(yè)政策調(diào)整方面日趨完善,將勢(shì)必對(duì)銀行業(yè)整體資產(chǎn)質(zhì)量提出新的挑戰(zhàn)。在即將到來(lái)的很長(zhǎng)時(shí)間中,我國(guó)商業(yè)銀行的資產(chǎn)管理將要承受來(lái)自經(jīng)濟(jì)和金融環(huán)境的諸多壓力。從2008年開(kāi)始,我國(guó)信貸規(guī)模處于不斷擴(kuò)張的態(tài)勢(shì),不動(dòng)產(chǎn)行業(yè)、城投公司和其余與基礎(chǔ)建設(shè)、產(chǎn)能過(guò)剩等行業(yè)相關(guān)的企業(yè)成為了最大的獲益者。長(zhǎng)期以來(lái)我國(guó)堅(jiān)定不移地貫徹經(jīng)濟(jì)轉(zhuǎn)型政策,就是要解決產(chǎn)能過(guò)剩和落后企業(yè)淘汰的問(wèn)題,其中重要的方面就是嚴(yán)格把控銀行貸款集中度,防止新增信貸大規(guī)模的向這些企業(yè)流動(dòng),在這個(gè)過(guò)程中商業(yè)銀行的資產(chǎn)質(zhì)量下降的壓力自然會(huì)進(jìn)一步加大。在2011年Q3至2016年Q4的22個(gè)季度之內(nèi),商業(yè)銀行不良貸款率一直處于上升通道,沒(méi)有下降的跡象。商業(yè)銀行的不良率實(shí)際水平可能遠(yuǎn)高于披露出來(lái)的數(shù)據(jù),原因在于各家銀行對(duì)于不良貸款的核銷(xiāo)額和難以收回的貸款數(shù)額通常是在報(bào)表中合并披露,并不能做到精確的區(qū)分。觀察近年來(lái)的數(shù)據(jù),核銷(xiāo)額和不能收回的貸款數(shù)額都有快速上漲的可能性,這一問(wèn)題的存在對(duì)銀行業(yè)來(lái)說(shuō)是2017年的一大難題。再者,當(dāng)發(fā)生理財(cái)產(chǎn)品償付問(wèn)題的時(shí)候,雖然商業(yè)銀行只是扮演托管者的身份,從根本上說(shuō)并沒(méi)有義務(wù)來(lái)承擔(dān)損失,但國(guó)家考慮到社會(huì)安定因素,最終有銀行來(lái)買(mǎi)單的可能性極大。綜上所述,本文重點(diǎn)考察某些經(jīng)濟(jì)因素的變化是如何影響我國(guó)商業(yè)銀行不良貸款率,然后在此基礎(chǔ)上專(zhuān)門(mén)提出政策建議,這是具有重大現(xiàn)實(shí)意義的。本文的內(nèi)容分為三個(gè)主要部分:第一部分為理論辨析,主要分為三個(gè)組成部分:第一,對(duì)選題的研究背景和意義進(jìn)行介紹,并梳理國(guó)內(nèi)外關(guān)于宏觀壓力測(cè)試的相關(guān)文獻(xiàn)資料;第二,對(duì)信用風(fēng)險(xiǎn)理論和壓力測(cè)試?yán)碚撨M(jìn)行闡述;第三,對(duì)于巴塞爾Ⅲ中的相關(guān)內(nèi)容進(jìn)行歸納總結(jié),重點(diǎn)在于對(duì)巴塞爾Ⅲ中新的監(jiān)管標(biāo)準(zhǔn)在中國(guó)的適用情況的分析。第二部分為實(shí)證研究,這一部分以改良后的Wilson模型為基礎(chǔ),選取我國(guó)商業(yè)銀行2005Q1至2016Q4的數(shù)據(jù)進(jìn)行實(shí)證分析,探究當(dāng)宏觀經(jīng)濟(jì)變量變動(dòng)時(shí)我國(guó)商業(yè)銀行不良貸款率的波動(dòng)程度。依照我國(guó)商業(yè)銀行自身特點(diǎn)和宏觀經(jīng)濟(jì)的實(shí)際情況,選定的宏觀經(jīng)濟(jì)解釋變量為:國(guó)內(nèi)生產(chǎn)總值增長(zhǎng)率(GDP)、M2增長(zhǎng)率(M2)、出口金額增長(zhǎng)率(EX)、中國(guó)國(guó)房景氣指數(shù)(NRCI)、居民消費(fèi)價(jià)格指數(shù)增長(zhǎng)率(CPI)、公共財(cái)政收入增長(zhǎng)率(PFI)、固定資產(chǎn)投資價(jià)格指數(shù)(FAI)、社會(huì)消費(fèi)品零售總額增長(zhǎng)率(RS)、工業(yè)增加值增長(zhǎng)率(AVI)和一年期貸款基準(zhǔn)利率(SLR)。分析結(jié)果顯示:公共財(cái)政收入增長(zhǎng)率(PFI)、一年期貸款基準(zhǔn)利率(SLR)、中國(guó)國(guó)房景氣指數(shù)(NRCI)和固定資產(chǎn)投資價(jià)格指數(shù)(FAI)對(duì)不良貸款率有著十分顯著的影響,其中PFI、SLR與不良貸款率為正相關(guān)關(guān)系,NRCI、FAI與之為負(fù)相關(guān)關(guān)系。第三部分為壓力測(cè)試,主要是在實(shí)證推導(dǎo)出的模型的基礎(chǔ)上進(jìn)行壓力測(cè)試,設(shè)置了輕度沖擊、中度沖擊和重度沖擊三種不同情景。壓力測(cè)試結(jié)果表明,在銀行受到重度沖擊的情況下,預(yù)計(jì)2017年末我國(guó)商業(yè)銀行不良貸款余額為22,493.44億元,比2016年末上漲了 7,371.44億元,漲幅達(dá)到了 48.75%。預(yù)計(jì)2017年我國(guó)商業(yè)銀行的撥備覆蓋率為133.16%,說(shuō)明整體風(fēng)險(xiǎn)可控,但比2016年下降了 29.45%。
[Abstract]:At present, the overall assets of the China banking industry is facing the huge decline in risk, in recent years is mainly due to the economic slowdown and weakness in the real economy and other reasons, this reflects the quality of bank assets and changes in the economic cycle has a great Association. Although compared with the world banking industry, China's non-performing loan rate remained low for a long time, but along with the deepening of economic reform, the improvement of industrial policy adjustment, will inevitably bring new challenges to the overall quality of assets of commercial banks. In the coming for a long time, the asset management of China's commercial banks will have to bear a lot of pressure from the economic and financial environment. From the beginning of 2008. China's credit scale is constantly expanding trend, the real estate industry, the city investment company and other infrastructure, industry overcapacity and other related enterprises have become the biggest beneficiaries. Since China unswervingly implement the policy of economic transformation is to resolve the overcapacity and backward enterprises out of the question, which is an important aspect of the strict control of bank loan concentration, to prevent the new credit scale to these enterprises in the process of the flow, decrease banks' asset quality pressures will further increase. Within the 22 quarter of 2011 to Q3 Q4 in 2016, commercial banks non-performing loan ratio has been in a rising channel, there is no sign of decline. Commercial banks non-performing rate may be much higher than the actual level of disclosure of data, because the banks for non-performing loans and the amount of verification is difficult to recover the loan amount is usually incorporated in the report disclosure it is not accurate, sensitive. Observation data in recent years, and the amount of verification can not recover the loans are likely to rise rapidly, This problem is a big problem for banks in 2017. Furthermore, when financial products to pay, although commercial banks only played the custodian's identity, basically has no obligation to bear the loss, but the state taking into account the social stability factors, the final possibility to pay the bank greatly. In summary, this paper focuses on the changes of some economic factors how to influence the rate of non-performing loans of commercial banks in China, and then on the basis of specific policy recommendations, it is of great practical significance. This paper is divided into three main parts: the first part is the theoretical analysis, mainly divided into three parts: the first on the topic, the research background and significance are introduced, and combing at home and abroad on the macro stress testing related documents; second, the credit risk theory and pressure test theory Third, for the related elaboration; the content of Basel III in the summary, the focus is on the analysis of the application of new regulatory standards in the Basel III China. The second part is the empirical research, this part of the modified Wilson model as the foundation, selection of China's commercial banks 2005Q1 to 2016Q4 data for empirical analysis of the volatility of inquiry when the changes of macroeconomic variables when the non-performing loan ratio of commercial banks. According to the actual situation of China's commercial banks own characteristics and macro economy, explain the economic selected variables: the growth rate of GDP growth rate (GDP), M2 (M2), the amount of export growth rate (EX Chinese), the State Housing boom index (NRCI), the growth rate of the consumer price index (CPI), public finance revenue growth rate (PFI), the price index of investment in fixed assets (FAI), the total retail sales of social consumer goods (RS), the growth rate of industrial growth. The growth rate of value added (AVI) and the one-year benchmark lending rate (SLR). The analysis results show that the public finance income growth rate (PFI), the one-year benchmark lending rate (SLR), Chinese State Housing boom index (NRCI) and fixed asset investment price index (FAI) has a very significant impact. The rate of non-performing loans in which PFI, SLR and the rate of non-performing loans was a positive correlation between NRCI, FAI, and the correlation was negative. The third part is the pressure test, pressure test is mainly based on empirical model derived, set up a mild shock, moderate shock and severe impact of three different scenarios. The result of the stress tests in the bank by the severe impact, is expected by the end of 2017 China's commercial banks non-performing loans amounted to 2 trillion and 249 billion 344 million yuan, 737 billion 144 million yuan higher than the end of 2016, rose 48.75%. is expected in 2017 China's commercial banks provision coverage For 133.16%, the overall risk was controlled, but it was down 29.45%. in 2016.

【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F832.33

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