本文關(guān)鍵詞： 偏最小二乘法 CPV模型 蒙特卡羅模擬法 壓力測試 違約概率　出處：《復(fù)旦大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：現(xiàn)如今銀行業(yè)所需要處理的各類風(fēng)險中,信用風(fēng)險已成為最主要的一種,如何對信用風(fēng)險進(jìn)行監(jiān)控和對其可能引發(fā)的危機(jī)進(jìn)行預(yù)警是對銀行本身和監(jiān)管部門而言非常重要的課題.如果需要對信用風(fēng)險進(jìn)行識別和預(yù)防,最急需的是構(gòu)建一個科學(xué)的度量系統(tǒng).傳統(tǒng)的信用風(fēng)險評估方法是信用評級為主的定性模型,直至20世紀(jì)90年代后在計算機(jī)編程技術(shù)和現(xiàn)代金融工程方法的輔助之下,定量的信用風(fēng)險度量模型才得以產(chǎn)生并迅速發(fā)展.本文首先對比了其中幾個常用的信用風(fēng)險度量模型,即：KMV模型、CreditRisk+模型、CreditMetricsTM模型及其改進(jìn)版—貸款組合觀點(CreditPortfo-lio View,簡稱CPV)模型相互之間的優(yōu)劣.雖然方法論上各有千秋,但這四類模型的主要作用都是使用可觀測數(shù)據(jù)對資產(chǎn)組合或整個金融體系的信貸資產(chǎn)面臨的違約風(fēng)險進(jìn)行擬合,以期可以預(yù)知其未來的風(fēng)險水平.但事實上,利用過去或現(xiàn)在的數(shù)據(jù)所計算出的違約風(fēng)險都是基于過去或現(xiàn)在的市場現(xiàn)狀情況下的,但現(xiàn)實世界中的金融市場瞬息萬變,例如1997年亞洲金融風(fēng)暴、2007年次貸危機(jī)這類影響巨大的金融危機(jī)很難通過歷史數(shù)據(jù)預(yù)知,但一旦發(fā)生,就會使得以銀行首當(dāng)其沖的金融體系受到巨大沖擊,因此對這種異常情況下自身可能受到的損失銀行業(yè)及監(jiān)管機(jī)構(gòu)是必須考慮提前預(yù)防的,在這種情況下壓力測試這樣一個通過模擬經(jīng)濟(jì)受到?jīng)_擊(主要是負(fù)面沖擊)時的情景來計算風(fēng)險如何變化的工具應(yīng)運而生.本文通過比較認(rèn)為CPV模型這樣一個與宏觀因素直接掛鉤的模型對于用來進(jìn)行壓力測試比較實用,但是前人在建立宏觀因素與違約概率的關(guān)系時一般所使用的似無關(guān)回歸法在自變量之間存在線性相關(guān)關(guān)系時會使參數(shù)出現(xiàn)錯誤的估計,因此需要在選擇宏觀因素作為自變量時需要首先手工篩選相關(guān)性比較小的因素.就此本文提出使用偏最小二乘法來克服這個缺點,即用偏最小二乘法(PLS)代替原始模型中使用的似無關(guān)回歸法(SUR),在使用蒙特卡羅法進(jìn)行模擬的配合下建立壓力測試模型,以期對信用風(fēng)險在宏觀經(jīng)濟(jì)在未來某一時刻受到可能的打擊時的變化程度進(jìn)行評估,但在嘗試進(jìn)行實證時遇到因變量—違約概率數(shù)據(jù)難以尋找的情況,雖然國內(nèi)有學(xué)者嘗試直接使用不良貸款率進(jìn)行替換并進(jìn)行實證,但本文通過分析認(rèn)為這樣的做法并不合理,而是借助不良貸款率生成了違約概率的樣板數(shù)據(jù)進(jìn)行了詳盡的實證及壓力測試的模擬,并對進(jìn)一步提高模型精度提出了展望.
[Abstract]:Nowadays, the credit risk has become the most important one among all kinds of risks that the banking industry needs to deal with. How to monitor the credit risk and warn the possible crisis is a very important issue for the bank itself and the supervision department. If it is necessary to identify and prevent the credit risk. The most urgent thing is to build a scientific measurement system. The traditional credit risk assessment method is a qualitative model based on credit rating. Until 1990s, aided by computer programming techniques and modern financial engineering methods. Quantitative credit risk measurement model has been produced and developed rapidly. Firstly, this paper compares several commonly used credit risk measurement models, namely: KMV model and CreditRisk model. The CreditMetricsTM model and its improved version of the loan portfolio perspective are CreditPortfo-lio View. The pros and cons of CPV models are different in methodology. But the main role of these four models is to use observable data to fit the default risk of the portfolio or the credit assets of the whole financial system in order to predict the level of future risk. Default risks calculated from past or present data are based on past or present market conditions, but financial markets in the real world are rapidly changing, such as the Asian financial turmoil in 1997. In 2007, a financial crisis such as the subprime mortgage crisis is difficult to predict through historical data, but once it occurs, it will make the financial system which bears the brunt of the banks to suffer a huge impact. So banks and regulators must consider prevention ahead of time for possible losses to themselves in such exceptional circumstances. In this case, a stress test such as a simulated economic shock (mainly a negative shock). This paper thinks that CPV model, which is directly linked to macro factors, is more practical for stress testing. However, in establishing the relationship between macro factors and default probability, the similar independent regression method used in general makes the parameters misestimate when there is a linear correlation between independent variables. Therefore, when selecting macro factors as independent variables, we need to first manually screen the factors with small correlation. In this paper, we propose the use of partial least square method to overcome this shortcoming. In other words, the partial least square method (PLS) is used to replace the similar independent regression method used in the original model, and the pressure test model is established with the cooperation of Monte Carlo simulation. In order to assess the degree of credit risk change in the future when the macroeconomic is likely to be hit, but in the attempt to carry out empirical analysis of dependent variables-default probability data difficult to find the situation. Although some domestic scholars try to directly use the non-performing loan rate to replace and carry on the demonstration, but this article through the analysis thinks that this kind of practice is not reasonable. On the other hand, the model data of default probability is generated by non-performing loan ratio to simulate the model and stress test in detail, and the prospect of further improving the precision of the model is put forward.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F832.33;F224

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本文編號：1485448