本文關(guān)鍵詞：基于多重信貸網(wǎng)絡(luò)的銀企間風(fēng)險傳染與控制研究　出處：《東南大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 風(fēng)險傳染 風(fēng)險控制 信貸網(wǎng)絡(luò) 銀行主體 企業(yè)主體

【摘要】：現(xiàn)代經(jīng)濟(jì)體系的一個重要特征是主體間具有較高的連接。為此,一個主體的違約可能會引發(fā)若干與其具有直接或間接關(guān)聯(lián)的主體的違約,即所謂的多米諾骨牌效應(yīng)。正如相關(guān)學(xué)者的研究,現(xiàn)代經(jīng)濟(jì)體系中的主體間的關(guān)聯(lián)形成了一個網(wǎng)絡(luò),在這個網(wǎng)絡(luò)格局中,一個主體的違約不僅受其自身財務(wù)狀況的影響,同時亦受與其有關(guān)聯(lián)(如借貸關(guān)系)的鄰居主體的財務(wù)狀況的影響,而其鄰居主體的財務(wù)狀況亦受其鄰居主體的鄰居主體的影響,依此類推。同樣,一個主體違約,不僅會負(fù)向影響與其直接相連的鄰居主體,同時亦會通過對其鄰居主體的影響而將違約的負(fù)面效應(yīng)傳遞給其鄰居主體的鄰居主體。企業(yè)和銀行是現(xiàn)代經(jīng)濟(jì)體系的兩大重要主體,二者間的關(guān)聯(lián)也越來越緊密。企業(yè)主體的違約會產(chǎn)生銀行壞賬,當(dāng)銀行主體無法承受企業(yè)主體違約而產(chǎn)生的壞賬時,銀行主體會破產(chǎn)。銀行作為極其重要的經(jīng)濟(jì)主體,其破產(chǎn)將會帶來極大的負(fù)面效應(yīng)。為此,對銀企主體間的風(fēng)險傳染研究十分必要。基于此,本文在已有的研究基礎(chǔ)上,構(gòu)建了包含銀行主體和企業(yè)主體的內(nèi)生多重信貸網(wǎng)絡(luò)模型,并基于該模型對銀企主體間的風(fēng)險傳染與控制進(jìn)行研究。首先,基于企業(yè)主體間的商業(yè)信貸連接、銀行和企業(yè)主體間的銀行信貸連接及銀行主體間的拆借連接構(gòu)建了包含銀行主體和企業(yè)主體的多重信貸網(wǎng)絡(luò)模型,且主體間信貸需求量及主體間信貸連接的建立均為內(nèi)生。通過計算機(jī)仿真,所構(gòu)建的模型能夠刻畫現(xiàn)實中主體及主體關(guān)聯(lián)網(wǎng)絡(luò)的一些典型特征：當(dāng)企業(yè)規(guī)模大于一定閾值時候,企業(yè)規(guī)模服從冪律分布；銀行規(guī)�？捎镁哂袃缏晌膊康膶�(shù)正態(tài)分布來擬合；銀行-企業(yè)信貸網(wǎng)絡(luò)和銀行-銀行信貸網(wǎng)絡(luò)的銀行入度服從雙冪律分布。其次,基于多重信貸網(wǎng)絡(luò)模型,從信貸網(wǎng)絡(luò)結(jié)構(gòu)和主體行為對銀企主體間的風(fēng)險傳染進(jìn)行研究。對于網(wǎng)絡(luò)結(jié)構(gòu),分別研究了交易對手的隨機(jī)選擇數(shù)M、交易對手轉(zhuǎn)移概率參數(shù)γ的變化對銀企間風(fēng)險傳染的影響。研究表明M和λ對于主體的作用機(jī)理不同,隨著二者的不斷變動,相應(yīng)的研究變量(銀行主體、上游企業(yè)主體和下游企業(yè)主體累積破產(chǎn)數(shù)、社會產(chǎn)出、銀行主體的信貸、壞賬均值及標(biāo)準(zhǔn)差)表現(xiàn)出不同的變動路徑,但較大的M、λ都將導(dǎo)致較大的研究變量,銀企間風(fēng)險傳染加大,以經(jīng)濟(jì)的脆弱性換來社會產(chǎn)出的增大。另外,從企業(yè)主體和銀行主體行為兩方面,研究了主體行為對風(fēng)險傳染的影響。對于企業(yè)主體行為,研究表明隨著企業(yè)主體產(chǎn)量參數(shù)φ與β取值的不斷增大,相應(yīng)的研究變量呈現(xiàn)不同的變化趨勢。較大的φ值導(dǎo)致較大的研究變量,而較大的β值卻帶來相關(guān)研究變量的下降(銀行主體的信貸均值除外)。對于銀行主體行為,研究表明,隨著銀行主體風(fēng)險厭惡系數(shù)Ψ的增大,銀行主體累積破產(chǎn)數(shù)呈遞減趨勢,而上游企業(yè)主體累積破產(chǎn)數(shù)在高位波動后呈下降趨勢,下游企業(yè)主體累積破產(chǎn)數(shù)則在低位波動后呈上升趨勢。而隨著銀行主體分紅比率上限d的不斷增大,銀行主體的累積破產(chǎn)數(shù)呈現(xiàn)明顯遞增趨勢,上游企業(yè)主體和下游企業(yè)主體的累積破產(chǎn)數(shù)則呈現(xiàn)波動狀態(tài),無明顯趨勢。較大的存款比例下限υmin將帶來低的銀行主體和下游企業(yè)主體的累積破產(chǎn)數(shù)及較大的上游企業(yè)主體累積破產(chǎn)數(shù),當(dāng)達(dá)到一定閾值后,各部門主體累積破產(chǎn)數(shù)趨于穩(wěn)定。而對于綜合性參數(shù)α而言,隨著α的不斷增大,銀行主體的累積破產(chǎn)數(shù)呈現(xiàn)遞增趨勢,上游企業(yè)主體的累積破產(chǎn)數(shù)則呈現(xiàn)先增后減的趨勢,而下游企業(yè)主體的累積破產(chǎn)數(shù)則呈現(xiàn)先明顯遞增而后緩慢遞增的趨勢。以上參數(shù)的變動對其它研究變量的影響亦不同。最后,基于多重信貸網(wǎng)絡(luò)模型,對銀企主體間的風(fēng)險控制進(jìn)行研究。研究表明存款準(zhǔn)備金率的提升并不是控制銀企間風(fēng)險的較好手段。存款準(zhǔn)備金率的提升,雖然抑制了銀行主體的風(fēng)險投資比率,進(jìn)而減少了銀行主體的破產(chǎn)風(fēng)險,但存款準(zhǔn)備金率的提升亦減少了銀行系統(tǒng)對經(jīng)濟(jì)主體的信貸供給,亦會導(dǎo)致經(jīng)濟(jì)主體因流動性不足而破產(chǎn),并且宏觀經(jīng)濟(jì)產(chǎn)出也未必一定增加。而資產(chǎn)最大銀行主體與入度最大銀行主體等系統(tǒng)重要節(jié)點主體的違約給銀企系統(tǒng)中的相關(guān)部門主體帶來較大的負(fù)向影響。對于銀行主體和下游企業(yè)主體而言,資產(chǎn)最大銀行主體違約所引發(fā)的累積破產(chǎn)數(shù)演化路徑明顯高于由度最大銀行主體違約所引發(fā)的累積破產(chǎn)數(shù)演化路徑,而由度最大銀行主體違約所引發(fā)的上游企業(yè)主體每期累積破產(chǎn)數(shù)要高于由資產(chǎn)最大銀行主體違約所引發(fā)的累積破產(chǎn)數(shù)。這說明,僅預(yù)防那些“最大而不能倒閉”銀行主體“不倒”并不足夠,亦應(yīng)關(guān)注那些具有較大入度連接的銀行主體。而對于那些具有較大入度連接的資產(chǎn)較大的銀行主體,則更應(yīng)重點監(jiān)控。研究亦表明,債務(wù)透明度規(guī)則的實施可在社會產(chǎn)出變動不是很大的情況下減緩銀企間的風(fēng)險傳染；中央銀行的流動性供給利于銀企系統(tǒng)穩(wěn)定性的提高,并可促進(jìn)社會產(chǎn)出的提升。另外,對于不同的市場參與度,中央銀行的流動性供給效果相差不大�？傊�,本文基于銀企主體間的信貸連接,構(gòu)建了內(nèi)生的銀企多重信貸網(wǎng)絡(luò)模型,基于該網(wǎng)絡(luò)模型從信貸網(wǎng)絡(luò)結(jié)構(gòu)和主體行為兩個視角對銀企間風(fēng)險傳染進(jìn)行了研究,并對網(wǎng)絡(luò)視角下的銀企間風(fēng)險控制進(jìn)行了研究,從理論上對銀企主體間的風(fēng)險傳染與控制進(jìn)行了深入系統(tǒng)的研究,為監(jiān)管者提供了可供參考的模型和方法,具有較高的理論價值和現(xiàn)實意義。
[Abstract]:An important feature of modern economic system is the connection between the main body. Therefore, a subject of breach of contract may cause some subjects with directly or indirectly associated with the so-called Domino effect. As research scholars, related subjects in the modern economic system between the formation of a in the network, the network structure, the influence of default of a subject not only by its own financial situation, but also by its associated effects (such as lending) neighbors of the main financial status, influence, and its neighbor the main financial situation is subject to its neighbor neighbor subject and so on. The same subject a breach of contract, subject, not only would negatively influence directly connected neighbors subject, also through influence on its neighbor subject and transfer the negative effect of default to its neighbors and neighbors Subject. Enterprises and banks are two major subjects of modern economic system, relationship between the two is getting more and more close. The main body of the enterprise default will produce bad debts, when banks cannot afford enterprises subject and breach of bad debts, banks will go bankrupt. The main bank as a very important economic subject, its bankruptcy it will bring great negative effect. Therefore, it is necessary to study the risk contagion between the main bank. Based on this, this article on the basis of the study, constructed the bank subject and enterprise main endogenous multiple credit network model, based on the model of risk transmission and control between the main bank first. The connection between the main business enterprises, based on credit, banks and enterprises between bank credit and bank lending between the main connection connected by the multi subject and enterprise entity contains bank Credit network model, and the main credit demand and establish the connection between the subject of credit are endogenous. Through computer simulation, the model can describe some typical features of the subject and the reality subject Association Network: when the enterprise size is greater than a certain threshold value, the scale of enterprises follows the power-law distribution; logarithm of bank size available has a power-law tail of normal distribution to fit; Bank - enterprise credit network and bank - bank credit bank network penetration obeys the power-law distribution. Secondly, multiple credit network model based on the research of bank risk contagion between subjects from the credit network structure and behavior. For the random network structure. Choice of counterparties number M, counterparty transfer change probability parameter influence on the risk contagion between banks and enterprises. The research shows that the M and the main body of the different mechanism, With the continuous change of the two corresponding research variables (the banks of the main upstream enterprises and downstream enterprises, the main body of the cumulative number of bankruptcy, the main social output, bank credit, bad debts mean and standard deviation) showed changes in different ways, but the larger M, lambda will lead to larger research variables, bank risk infectious increase, the vulnerability of the economy to increase for social output. In addition, from the two aspects of enterprises and bank behavior, studied the effect of the main behavior of the risk of infection. For enterprise behavior, research shows that with the increasing of enterprises production parameter and beta value, study the corresponding variable trend different. Larger values of the study variables lead to larger, and the larger the beta value declines bring related research variables (except the main bank credit mean). For the main body of bank behavior, study shows, With the increase of the risk aversion coefficient is the main bank, the banks of the main cumulative number of bankruptcy decreased, while the upstream enterprises cumulative bankruptcy had decreased at a high level, the number of main downstream enterprises accumulated bankruptcy in the low volatility increased. With the increase of bank main payout ratio limit D, cumulative bankruptcy the number of the main banks showed increasing trend, the cumulative number of bankrupt enterprises and downstream enterprises upstream is fluctuated, no obvious trend. The proportion of deposits lower min will bring large main banks and downstream enterprises the main low cumulative number of bankruptcy and large enterprises upstream cumulative number of bankruptcy, when reaching a certain threshold after all, the main departments cumulative number of bankruptcy tends to be stable. For the comprehensive parameter, with the increasing of alpha, cumulative number of bankrupt banks subject increasing The cumulative number of upstream enterprise bankruptcy trend, subject showed a trend of first increase and then decrease while the cumulative number of downstream enterprises bankruptcy showed significantly increasing light and then slowly increasing trend. The above parameters change on other research variables are also different. Finally, the multiple credit network model based on bank risk subject the better way to control research. Research shows that the deposit reserve rate increase is not control between banks and risk. The deposit reserve rate increase, while suppressing the bank's risk investment ratio, thereby reducing the risk of bankruptcy of banks main body, but also improve the deposit reserve rate cut the supply of credit to the economy the main body of the bank the system, will also lead to the economic subject of bankruptcy due to lack of liquidity, and macro economic output may not necessarily increase. While the largest bank assets subject and the maximum principal bank penetration The main body of an important node system default to the relevant departments in the main bank system bring great negative effect. For the main banks and downstream enterprises subject, cumulative number of bankruptcy subject assets bank default caused by the maximum evolution path was significantly higher than that of cumulative number of bankruptcy by the largest bank degree caused by the breach of the main evolution path, and by most banks default caused by the main upstream enterprises of each subject should be higher than the cumulative number of cumulative bankruptcy bankruptcy by the largest bank entity assets caused by default. This shows that only prevent those "maximum fail" bank subject "down" is not enough, should also pay attention to those with larger banks. While the main connection degree for those with a greater penetration of connect assets larger banks subject, should be more focus on monitoring. The study also showed that the implementation of transparency rules can be in debt Social change is not great output under the condition of slow risk contagion between banks and enterprises; the supply of liquidity by the central bank to improve the stability of bank system, and promote the society to improve the output. In addition, for different market participation, the supply of liquidity effect of the central bank had little difference. In short, the bank credit subject based on the construction of the bank and credit connection, multi network model is endogenous, two aspects of the network model from the credit network structure and behavior are studied based on the risk contagion between banks and enterprises, and from the perspective of network between banks and risk control are studied, conducts a systematic research on the risk of infection and control of the main bank between the theoretical model and the method, provides the reference for regulators, has high theoretical value and practical significance.

【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：F832.4

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 周偉;何建敏;余德建;;不同趨勢下滬銅場內(nèi)外風(fēng)險傳染實證——基于整體傳染與時變傳染的分析[J];管理工程學(xué)報;2013年02期

2 趙勝民;謝曉聞;方意;;中國在全球股市風(fēng)險傳染網(wǎng)絡(luò)中的角色研究——基于次貸危機(jī)和歐債危機(jī)時期的樣本分析[J];財經(jīng)論叢;2013年05期

3 陳庭強(qiáng);何建敏;;基于復(fù)雜網(wǎng)絡(luò)的信用風(fēng)險傳染模型研究[J];軟科學(xué);2014年02期

4 賀根慶;;金融市場風(fēng)險傳染的原因及影響因素研究[J];征信;2014年07期

5 吳寶;李正衛(wèi);池仁勇;;社會資本、融資結(jié)網(wǎng)與企業(yè)間風(fēng)險傳染——浙江案例研究[J];社會學(xué)研究;2011年03期

6 陳春蓉;;中國房地產(chǎn)行業(yè)與金融行業(yè)的風(fēng)險傳染實證分析[J];商品與質(zhì)量;2011年S7期

7 尹群耀;陳庭強(qiáng);何建敏;吳亞麗;;基于濾子理論的信用風(fēng)險傳染模型[J];系統(tǒng)工程;2012年12期

8 張英奎;馬茜;姚水洪;;基于復(fù)雜網(wǎng)絡(luò)的銀行系統(tǒng)風(fēng)險傳染與防范[J];統(tǒng)計與決策;2013年10期

9 鄧晶;張加發(fā);李紅剛;;基于信貸網(wǎng)絡(luò)的經(jīng)濟(jì)波動和風(fēng)險傳染研究[J];北京師范大學(xué)學(xué)報(自然科學(xué)版);2013年04期

10 王倩;T.Hartmannwendels;;信用違約風(fēng)險傳染建模[J];金融研究;2008年10期

相關(guān)重要報紙文章 前9條

1 金融學(xué)博士 程實;風(fēng)險傳染引發(fā)美國經(jīng)濟(jì)惡性循環(huán)之憂[N];證券時報;2007年

2 本報記者 楊洋;“非法集資”頻發(fā) 銀行嚴(yán)防風(fēng)險傳染[N];金融時報;2014年

3 鮑仁;銀監(jiān)會：嚴(yán)控外部風(fēng)險傳染[N];期貨日報;2012年

4 朱文彬;銀監(jiān)會重兵布控銀行業(yè)外部風(fēng)險傳染[N];中國城鄉(xiāng)金融報;2013年

5 本報記者 史進(jìn)峰;信用違約、表外、外部風(fēng)險傳染 銀監(jiān)會鎖定三大監(jiān)管重點[N];21世紀(jì)經(jīng)濟(jì)報道;2013年

6 本報記者 鐘輝　實習(xí)記者 陳琳;48.65萬億“表外”逼近“表內(nèi)” 監(jiān)管警示風(fēng)險傳染[N];21世紀(jì)經(jīng)濟(jì)報道;2013年

7 經(jīng)濟(jì)視點報記者 吳春波;謹(jǐn)防地產(chǎn)行業(yè)風(fēng)險傳染社區(qū)銀行業(yè)[N];經(jīng)濟(jì)視點報;2014年

8 證券時報記者 賈壯;防范個別房企資金鏈斷裂風(fēng)險傳染[N];證券時報;2014年

9 本報記者 唐皎;銀監(jiān)會：警惕資產(chǎn)風(fēng)險傳染銀行系統(tǒng)[N];21世紀(jì)經(jīng)濟(jì)報道;2008年

相關(guān)博士學(xué)位論文 前7條

1 李麗;企業(yè)集團(tuán)內(nèi)部信用風(fēng)險傳染機(jī)理與信用風(fēng)險控制研究[D];電子科技大學(xué);2015年

2 楊揚(yáng);企業(yè)集團(tuán)信用風(fēng)險傳染與演化機(jī)理研究[D];電子科技大學(xué);2015年

3 隋新;基于多重信貸網(wǎng)絡(luò)的銀企間風(fēng)險傳染與控制研究[D];東南大學(xué);2016年

4 萬陽松;銀行間市場風(fēng)險傳染機(jī)制與免疫策略研究[D];上海交通大學(xué);2007年

5 李松恭;商業(yè)銀行保證組合信用風(fēng)險傳染與緩釋研究[D];浙江大學(xué);2010年

6 王彭;證券市場第三產(chǎn)業(yè)板塊行業(yè)間信用風(fēng)險傳染研究[D];湖南大學(xué);2014年

7 靳飛;中國股票市場的風(fēng)險傳染和投資轉(zhuǎn)移研究[D];電子科技大學(xué);2010年

相關(guān)碩士學(xué)位論文 前10條

1 朱學(xué)帥;資產(chǎn)價格聯(lián)動性及風(fēng)險傳染機(jī)制研究[D];南京信息工程大學(xué);2015年

2 喻冬冬;知識共享對供應(yīng)鏈違約風(fēng)險傳染的影響研究[D];長安大學(xué);2015年

3 李杰汀;基于復(fù)雜網(wǎng)絡(luò)的關(guān)聯(lián)信用風(fēng)險傳染建模與仿真研究[D];電子科技大學(xué);2015年

4 羅佳;擔(dān)保鏈風(fēng)險傳染運(yùn)行機(jī)理的研究[D];浙江工商大學(xué);2015年

5 張凡;銀行網(wǎng)絡(luò)視角下的風(fēng)險傳染研究[D];東北財經(jīng)大學(xué);2015年

6 廖凱亮;我國商業(yè)銀行同業(yè)業(yè)務(wù)風(fēng)險傳染的特征及因素分析[D];東北財經(jīng)大學(xué);2015年

7 汪媛;我國銀行間系統(tǒng)性風(fēng)險傳染及風(fēng)險防范研究[D];浙江工商大學(xué);2015年

8 邱寧;我國銀行間市場的風(fēng)險傳染效應(yīng)研究[D];浙江工商大學(xué);2015年

9 張巖;我國離岸外匯市場與境內(nèi)外匯市場收益率波動間的風(fēng)險傳染研究[D];中國海洋大學(xué);2015年

10 鄧爽玲;銀行間風(fēng)險傳染與系統(tǒng)性風(fēng)險研究[D];東北財經(jīng)大學(xué);2014年

，

本文編號：1371532