本文選題：生存分析 + Cox比例風(fēng)險(xiǎn)模型�。� 參考：《山東大學(xué)》2014年碩士論文

【摘要】：信用風(fēng)險(xiǎn)作為銀行業(yè)的主要風(fēng)險(xiǎn)之一,表現(xiàn)形式越來(lái)越多樣化,影響也逐漸擴(kuò)大,不僅會(huì)給金融行業(yè)帶來(lái)巨大的沖擊還會(huì)影響整個(gè)社會(huì)的穩(wěn)定,因此,其危害性也得到了越來(lái)越多的重視。為了有效的管理信用風(fēng)險(xiǎn),商業(yè)銀行也加快了信用風(fēng)險(xiǎn)研究的步伐。 發(fā)達(dá)國(guó)家的信用風(fēng)險(xiǎn)管理起步早,發(fā)展較成熟,逐漸實(shí)現(xiàn)定性向定量的發(fā)展。而我國(guó)的商業(yè)銀行信用風(fēng)險(xiǎn)管理還處于定性分析的階段,缺乏成熟的定量分析模型和技術(shù)。然而,隨著金融行業(yè)的不斷發(fā)展,傳統(tǒng)的信用風(fēng)險(xiǎn)的評(píng)級(jí)方法和傳統(tǒng)的信用風(fēng)險(xiǎn)模型已經(jīng)很難適應(yīng)發(fā)展需要,因此準(zhǔn)確合理的度量商業(yè)銀行的信用風(fēng)險(xiǎn)對(duì)商業(yè)銀行的信用風(fēng)險(xiǎn)管理尤其重要。 目前研究信用風(fēng)險(xiǎn)的模型較多,應(yīng)用較早有Logit模型、研究單一信用風(fēng)險(xiǎn)的KMV模型及測(cè)度組合信用風(fēng)險(xiǎn)的Credit Risk+等模型,這些模型在一定條件和假設(shè)下取得了較好的結(jié)果。然而Credit Risk+等模型需要企業(yè)信用評(píng)級(jí)的數(shù)據(jù),而我國(guó)缺乏企業(yè)信用評(píng)級(jí)的歷史數(shù)據(jù),所以這些模型不太適用我國(guó)的金融市場(chǎng)。隨著生存分析理論的發(fā)展和應(yīng)用的不斷推廣,基于生存分析的Cox比例風(fēng)險(xiǎn)模型在信用風(fēng)險(xiǎn)度量研究中顯示出一定的優(yōu)越性,Cox比例風(fēng)險(xiǎn)模型不需要信用評(píng)級(jí)數(shù)據(jù),而是研究對(duì)生存時(shí)間有影響的財(cái)務(wù)數(shù)據(jù),因此可以用來(lái)分析國(guó)內(nèi)企業(yè)的財(cái)務(wù)狀況。本文嘗試進(jìn)行Cox比例風(fēng)險(xiǎn)模型的實(shí)證研究。 本文基于現(xiàn)有的信用風(fēng)險(xiǎn)研究結(jié)果,首先,介紹了生存分析的基本理論并分析生存分析在信用風(fēng)險(xiǎn)度量中的作用。其次,詳細(xì)介紹了基于生存分析的Cox比例風(fēng)險(xiǎn)模型,并給出了模型參數(shù)和非參部分的估計(jì)方法。最后,基于實(shí)際數(shù)據(jù)建立Cox比例風(fēng)險(xiǎn)模型,進(jìn)而得到上市公司的違約概率,并對(duì)模型結(jié)果進(jìn)行分析,根據(jù)模型結(jié)果我們可以分析增加上市公司財(cái)務(wù)風(fēng)險(xiǎn)的因素(即危險(xiǎn)因素)和減少上市公司風(fēng)險(xiǎn)的因素(即保護(hù)因素)。通過(guò)分析模型的時(shí)點(diǎn)預(yù)測(cè)能力及準(zhǔn)確性,判斷模型可行,商業(yè)銀行可以根據(jù)模型結(jié)果判斷自身面臨的信用風(fēng)險(xiǎn)情況。本文通過(guò)CAP曲線、ROC曲線及KS檢驗(yàn)驗(yàn)證了模型的有效性和穩(wěn)定性,為模型在實(shí)際中的應(yīng)用提供了基礎(chǔ)。
[Abstract]:As one of the main risks of the banking industry, credit risk is becoming more and more diversified and its influence is gradually expanding. It will not only bring huge impact to the financial industry but also affect the stability of the whole society. Therefore, its harmfulness has also been paid more and more attention. In order to manage credit risk, commercial banks also accelerate their credit The pace of risk research.
The credit risk management in developed countries is early, mature and gradually realized to the qualitative and quantitative development. The credit risk management of commercial banks in China is still in the stage of qualitative analysis, lack of mature quantitative analysis model and technology. However, with the continuous development of the financial industry, the traditional credit risk rating method and tradition The credit risk model has been difficult to adapt to the development needs, so the accurate and reasonable measure of the credit risk of commercial banks is particularly important for the credit risk management of commercial banks.
At present, there are many models to study credit risk, the Logit model is used earlier, the KMV model of single credit risk and the Credit Risk+ model of measuring combination credit risk are studied. These models have obtained good results under certain conditions and assumptions. However, Credit Risk+ and other models need the data of enterprise credit rating, but China is short of enterprise. The historical data of the industry credit rating, so these models are not very suitable for China's financial market. With the development and application of survival analysis theory, the Cox proportional risk model based on survival analysis shows some advantages in the research of credit risk measurement. Cox does not need credit rating data, but it does not need the credit rating data. The financial data that affect the survival time can be used to analyze the financial situation of domestic enterprises. This paper attempts to conduct an empirical study on the Cox proportional hazards model.
Based on the existing research results of credit risk, this paper first introduces the basic theory of survival analysis and analyzes the role of survival analysis in credit risk measurement. Secondly, the Cox proportional risk model based on survival analysis is introduced in detail, and the model parameters and non parametric estimation methods are given. Finally, based on the actual data, the Cox is established. According to the results of the model, we can analyze the factors of increasing the financial risk of the listed companies (i.e., the risk factors) and the factors to reduce the risk of the listed companies (that is the protection factor). It is feasible that commercial banks can judge their credit risk according to the results of the model. In this paper, the validity and stability of the model are verified by CAP curve, ROC curve and KS test, which provides a basis for the application of the model in practice.

【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】：2014
【分類號(hào)】：F832.33;O213

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 馬堅(jiān),張衛(wèi)朋,劉新梅;現(xiàn)代信用風(fēng)險(xiǎn)模型比較分析[J];商業(yè)研究;2004年08期

2 施錫銓,鄒新月;典型判別分析在企業(yè)信用風(fēng)險(xiǎn)評(píng)估中的應(yīng)用[J];財(cái)經(jīng)研究;2001年10期

3 吳世農(nóng),盧賢義;我國(guó)上市公司財(cái)務(wù)困境的預(yù)測(cè)模型研究[J];經(jīng)濟(jì)研究;2001年06期

4 歐陽(yáng)秀子;;我國(guó)商業(yè)銀行信用風(fēng)險(xiǎn)度量模型的實(shí)證研究——基于KMV模型的實(shí)證分析[J];金融與經(jīng)濟(jì);2009年04期

5 陳靜;上市公司財(cái)務(wù)惡化預(yù)測(cè)的實(shí)證分析[J];會(huì)計(jì)研究;1999年04期

6 林娟;楊美萍;;中小企業(yè)財(cái)務(wù)預(yù)警創(chuàng)新指標(biāo)體系實(shí)證研究[J];會(huì)計(jì)之友(中旬刊);2010年02期

7 張玲;財(cái)務(wù)危機(jī)預(yù)警分析判別模型[J];數(shù)量經(jīng)濟(jì)技術(shù)經(jīng)濟(jì)研究;2000年03期

8 陸志明;何建敏;姜麗莉;;基于生存分析模型的企業(yè)財(cái)務(wù)困境預(yù)測(cè)[J];統(tǒng)計(jì)與決策;2007年21期

9 鄧曉嵐;陳朝暉;王宗軍;;公司財(cái)務(wù)困境的非參數(shù)生存分析模型評(píng)價(jià)[J];武漢理工大學(xué)學(xué)報(bào)(信息與管理工程版);2007年06期

10 余紅梅,何大衛(wèi);檢查Cox模型比例風(fēng)險(xiǎn)假定的幾種圖示法[J];中國(guó)衛(wèi)生統(tǒng)計(jì);2000年04期

，

本文編號(hào)：1853670