商業(yè)銀行系統(tǒng)性風(fēng)險(xiǎn)測度與宏觀審慎監(jiān)管
發(fā)布時(shí)間：2018-08-13 15:41
【摘要】：在金融市場上,商業(yè)銀行面臨的風(fēng)險(xiǎn)不僅受自身因素影響,還受到由經(jīng)濟(jì)周期、國家宏觀經(jīng)濟(jì)政策的變動(dòng)、外部金融沖擊等風(fēng)險(xiǎn)因素的作用,這種出于銀行部門間的相關(guān)性,造成不同銀行間,乃至商業(yè)銀行同整個(gè)銀行系統(tǒng)間的風(fēng)險(xiǎn)傳導(dǎo),致使風(fēng)險(xiǎn)從單個(gè)銀行向整個(gè)銀行系統(tǒng)的擴(kuò)散稱之為商業(yè)銀行系統(tǒng)性風(fēng)險(xiǎn)。2008年爆發(fā)的金融危機(jī),使得這種具備隱匿性、積累性和傳染性,會(huì)對銀行部門乃至實(shí)體經(jīng)濟(jì)帶來巨大的負(fù)外部性效應(yīng),并且不能通過一般的風(fēng)險(xiǎn)管理手段相互抵消或者削弱的系統(tǒng)性風(fēng)險(xiǎn)成為國內(nèi)外學(xué)術(shù)界和政府部門關(guān)注的焦點(diǎn)。為了合理測度中國商業(yè)銀行系統(tǒng)性風(fēng)險(xiǎn)的大小,為宏觀審慎監(jiān)管構(gòu)建監(jiān)管指標(biāo),本文首先采用Adian和Brunnermeier 2009年提出的CoVaR方法,利用2007年11月16日至2014年2月7日12家上市的商業(yè)銀行收盤價(jià)的周數(shù)據(jù),通過分位數(shù)回歸法測度各上市銀行對于銀行系統(tǒng)的風(fēng)險(xiǎn)貢獻(xiàn)度,之后選取國有商業(yè)銀行和股份制銀行中具有代表性的建設(shè)銀行和浦發(fā)銀行,利用即得的VaR和CCoVaR數(shù)據(jù),兼之兩家銀行的資產(chǎn)報(bào)酬率、不良貸款率、資產(chǎn)總額、權(quán)益乘數(shù)以及GDP增長率等有關(guān)數(shù)據(jù),構(gòu)建主成分方程,按照累計(jì)貢獻(xiàn)度大于85%的原則,選取了第一、第二和第三主成分,進(jìn)一步構(gòu)建考慮自回歸和滯后的EGARCH模型,實(shí)證結(jié)果顯示：t-i期的CoVaR、GDP增長率、權(quán)益乘數(shù)和不良貸款率同t期的CoVaR之間存在反比例關(guān)系,t-i期的資產(chǎn)總額同t期的CoVaR呈正比例關(guān)系,且不良貸款率與資產(chǎn)總額對當(dāng)期CoVaR的影響最大,在i=1(滯后一季度)的情況下,上述變量對建設(shè)銀行當(dāng)前CoVaR的解釋力度最大,在i=2(滯后半年)的情況下,上述變量對浦發(fā)銀行CoVaR的解釋力度最大,監(jiān)管部門可根據(jù)上述變量的變動(dòng)情況,對系統(tǒng)重要性銀行銀行未來一段時(shí)間風(fēng)險(xiǎn)的變動(dòng)情況進(jìn)行有的放矢的監(jiān)控。
[Abstract]:In the financial market, the risks faced by commercial banks are affected not only by their own factors, but also by risk factors such as economic cycles, changes in national macroeconomic policies, external financial shocks, and so on. Causing risk transmission between different banks, and even between commercial banks and the whole banking system, leading to the spread of risk from a single bank to the entire banking system called systemic risk in commercial banks. The financial crisis broke out in 2008, Making this kind of latent, accumulative and contagious, will bring huge negative externalities to the banking sector and even to the real economy. And the systemic risk which can not be offset or weakened by the general risk management means has become the focus of academic and governmental attention at home and abroad. In order to reasonably measure the systemic risk of Chinese commercial banks and construct the supervision index for macro-prudential supervision, this paper firstly adopts the CoVaR method proposed by Adian and Brunnermeier in 2009. Based on the weekly data of closing prices of 12 listed commercial banks from November 16, 2007 to February 7, 2014, the risk contribution of each listed bank to the banking system was measured by quantile regression method. Then select the representative state-owned commercial banks and joint-stock banks of China Construction Bank and Shanghai Development Bank, and use the obtained VaR and CCoVaR data, as well as the return on assets, non-performing loan ratio, total assets of the two banks. According to the principle of cumulative contribution greater than 85%, the first, second and third principal components are selected to further construct the EGARCH model considering autoregressions and hysteresis. The empirical results show that there is a inverse relationship between the growth rate of CoVaRN, the equity multiplier and the non-performing loan ratio and the CoVaR in t period. The total assets in t-I period are positively proportional to the CoVaR in t period. Non-performing loan ratio and total assets have the greatest influence on current CoVaR. In the case of ix1 (lag first quarter), the above variables explain the current CoVaR of CCB most strongly, and in the case of ix2 (lag half a year), The above variables explain the CoVaR of Pudong Development Bank most intensively. According to the change of these variables, regulators can monitor the risk of systemically important banks in a certain period of time.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F832.33
本文編號(hào)：2181429
[Abstract]:In the financial market, the risks faced by commercial banks are affected not only by their own factors, but also by risk factors such as economic cycles, changes in national macroeconomic policies, external financial shocks, and so on. Causing risk transmission between different banks, and even between commercial banks and the whole banking system, leading to the spread of risk from a single bank to the entire banking system called systemic risk in commercial banks. The financial crisis broke out in 2008, Making this kind of latent, accumulative and contagious, will bring huge negative externalities to the banking sector and even to the real economy. And the systemic risk which can not be offset or weakened by the general risk management means has become the focus of academic and governmental attention at home and abroad. In order to reasonably measure the systemic risk of Chinese commercial banks and construct the supervision index for macro-prudential supervision, this paper firstly adopts the CoVaR method proposed by Adian and Brunnermeier in 2009. Based on the weekly data of closing prices of 12 listed commercial banks from November 16, 2007 to February 7, 2014, the risk contribution of each listed bank to the banking system was measured by quantile regression method. Then select the representative state-owned commercial banks and joint-stock banks of China Construction Bank and Shanghai Development Bank, and use the obtained VaR and CCoVaR data, as well as the return on assets, non-performing loan ratio, total assets of the two banks. According to the principle of cumulative contribution greater than 85%, the first, second and third principal components are selected to further construct the EGARCH model considering autoregressions and hysteresis. The empirical results show that there is a inverse relationship between the growth rate of CoVaRN, the equity multiplier and the non-performing loan ratio and the CoVaR in t period. The total assets in t-I period are positively proportional to the CoVaR in t period. Non-performing loan ratio and total assets have the greatest influence on current CoVaR. In the case of ix1 (lag first quarter), the above variables explain the current CoVaR of CCB most strongly, and in the case of ix2 (lag half a year), The above variables explain the CoVaR of Pudong Development Bank most intensively. According to the change of these variables, regulators can monitor the risk of systemically important banks in a certain period of time.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F832.33
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，本文編號(hào)：2181429
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