本文選題：信用評(píng)分 + probit回歸　； 參考：《山東大學(xué)》2012年碩士論文

【摘要】：信用風(fēng)險(xiǎn)作為最主要的金融風(fēng)險(xiǎn)類(lèi)型,是當(dāng)前金融界的最大課題。我國(guó)信貸業(yè)正進(jìn)入飛速發(fā)展時(shí)期,許多銀行相繼提出建設(shè)零售銀行的宏偉藍(lán)圖,這使得各銀行信貸業(yè)務(wù)量日益巨大。傳統(tǒng)的人工授信已經(jīng)無(wú)法適應(yīng)這種需求,信用評(píng)分模型這一在國(guó)外銀行業(yè)和信貸業(yè)逐步興起的技術(shù),也必將在中國(guó)得到廣泛運(yùn)用。在銀行業(yè)競(jìng)爭(zhēng)日益激烈的情況下,信用評(píng)分的研究和模型的選取成為銀行面臨的一項(xiàng)極富有挑戰(zhàn)性的管理問(wèn)題。probit回歸是與logistic回歸十分類(lèi)似的廣義線性模型,用于解決因變量的二分類(lèi)問(wèn)題。在建立信用評(píng)分模型時(shí),logistic回歸是一種十分常用的統(tǒng)計(jì)方法,而probit回歸在這方面卻極少論及。本文利用probit回歸建立申請(qǐng)信用評(píng)分模型,計(jì)算每個(gè)客戶的違約概率,進(jìn)而將客戶分為兩類(lèi),并對(duì)模型的分類(lèi)效果進(jìn)行了檢驗(yàn)。 本文首先從監(jiān)管要求和銀行內(nèi)在需求兩個(gè)方面闡述了大力發(fā)展信用評(píng)分模型的必要性,并概括總結(jié)了信用評(píng)分模型在應(yīng)用中表現(xiàn)出來(lái)的巨大優(yōu)勢(shì)。 本文的第二章介紹了國(guó)內(nèi)外信用評(píng)分模型的發(fā)展現(xiàn)狀,并對(duì)現(xiàn)有的建立信用評(píng)分模型的常用方法進(jìn)行了描述和討論,并比較了這些方法的優(yōu)劣,最后又對(duì)變量指標(biāo)的選取和數(shù)據(jù)處理的常用方法進(jìn)行了介紹。 本文的第三章是重點(diǎn)內(nèi)容,首先介紹了建立信用評(píng)分模型的開(kāi)發(fā)流程和所要面對(duì)的問(wèn)題,這些問(wèn)題包括模型分類(lèi)、風(fēng)險(xiǎn)因素變量清單選取、壞樣本數(shù)據(jù)定義及識(shí)別、建模數(shù)據(jù)來(lái)源等。然后,重點(diǎn)論述了建立模型的probit回歸及檢驗(yàn)?zāi)Ｐ头诸?lèi)情況的ROC曲線及CAP曲線及相關(guān)量化指標(biāo)。 本文的第四章是一個(gè)完整的建模過(guò)程。本文先從數(shù)據(jù)集中分別按照好壞樣本比為1：1,2：1,3：1抽取建模數(shù)據(jù),利用這些數(shù)據(jù)的原始值和woe值進(jìn)行建模,并且用剩余的數(shù)據(jù)進(jìn)行了樣本外檢驗(yàn)。最后,對(duì)中各種情況下得到的結(jié)果進(jìn)行了比較,得出了一組最好的分類(lèi)結(jié)果。 本文的第五章是結(jié)論部分,對(duì)第四部分得到的結(jié)果進(jìn)行了總結(jié),指出了模型的不足,并展望了違約概率模型今后的發(fā)展。
[Abstract]:Credit risk, as the most important type of financial risk, is the biggest subject in the current financial circle.China's credit industry is entering a period of rapid development, many banks have put forward a grand blueprint for the construction of retail banks, which makes the amount of credit business of banks increasingly huge.Traditional artificial credit has been unable to meet this demand, and credit scoring model, which is gradually rising in foreign banking and credit industry, will be widely used in China.Under the increasingly fierce competition in the banking industry, the study of credit rating and the selection of models become a challenging management problem for banks. Probit regression is a generalized linear model similar to logistic regression.It is used to solve the problem of two classification of dependent variables.Logistic regression is a very common statistical method in establishing credit scoring model, but probit regression is rarely discussed in this respect.In this paper, the application credit rating model is established by probit regression, and the default probability of each customer is calculated, and then the customers are divided into two categories, and the classification effect of the model is tested.Firstly, this paper expounds the necessity of developing credit scoring model from two aspects of supervision requirements and internal requirements of banks, and summarizes the great advantages of credit scoring model in application.The second chapter introduces the development of credit scoring models at home and abroad, describes and discusses the common methods of establishing credit scoring models, and compares the advantages and disadvantages of these methods.Finally, the selection of variable indexes and common methods of data processing are introduced.The third chapter of this paper is the key content, first introduced the establishment of credit scoring model development process and the problems to be faced, including model classification, risk factors variable list selection, bad sample data definition and identification,Modeling data sources and so on.Then, the probit regression of the model and the ROC curve and CAP curve of the model classification are discussed in detail.The fourth chapter of this paper is a complete modeling process.This paper first extracts the modeling data from the data set according to the ratio of good and bad samples 1: 1: 2: 1: 3: 1, and uses the original value and woe value of these data to model the model, and tests the remaining data out of the sample.Finally, the results obtained in various cases are compared and a group of best classification results are obtained.The fifth chapter of this paper is the conclusion part, summarizes the results obtained in the fourth part, points out the shortcomings of the model, and looks forward to the future development of the probability of default model.
【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】：2012
【分類(lèi)號(hào)】：F224;F830.3

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