无追索权国内保理信用风险度量研究 —基于我国上市公司KMV模型的分析

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3.0 周伟光 2024-09-30 5 4 514.56KB 51 页 15积分

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浙江财经学院硕士学位论文

摘要

随着买方市场的形成，企业为了扩大销售份额纷纷采取赊销的方式，导致企

业存在着大量的应收账款，为了弥补资金缺口与加快资金周转，企业迫切需要金

融机构提供针对应收账款服务的金融产品，保理就是在这种背景下发展起来的，

它是一种集贸易融资、卖方信用调查、信用风险担保、应收账款管理的综合性金

融服务方案。

国内目前开展保理业务金融机构主要是银行，所以本文将银行作为保理商来

进行论述，而开展保理业务的最大风险便是信用风险，包括买卖双方的信用风

险，买卖双方的信用风险大小直接关系到银行保理授信能否收回，无追索权保理

又是保理业务中风险最大的一种，所以对银行来说如何有效度量买卖双方的信用

风险是开展此类业务的关键。国内在信用风险度量方面比较落后，主要以传统的

定性方法为主，导致很难对一些高信用风险业务（如无追索权保理）进行及时准

确的信用风险度量，而 KMV 模型是基于资本市场的一种前瞻性模型，能够进行

信用风险的提前预警与动态跟踪，能够很好运用于对无追索权保理业务的信用风

险进行度量，所以本文将用其作为信用主体信用风险度量的工具。而在保理业务

对象上，由于上市公司信息透明，信用状况好，符合无追索权保理对信用主体等

级要求高的特点，所以本文将以上市公司作为开展无追索权保理的目标对象来进

行分析。

本文首先对无追索权保理中信用风险的类型与特点进行了介绍与分析，接着

对目前信用风险度量的理论基础与模型进行了归纳，在对比信用风险度量的模型

的优缺点后，发现KMV模型能够适用于无追索权保理业务中信用风险度量的特

点，所以将该模型用于无追索权保理业务中信用风险的度量，同时对KMV模型信

用风险度量的原理做了介绍，并对其中一些参数如何设置进行了说明，接着进行

了实证分析。

在实证部分，本文分为三部分：一是验证KMV模型是否适用于我国资本市场

中上市公司的信用风险度量。采用的方法是选用农业、机械行业、化工行业三个

行业的ST公司与正常类公司作为两类样本，验证了KMV模型对这两类样本是否

具有较好的区分度，结果表明KMV模型基本能够区分这两类公司的信用风险状

况，说明了KMV模型能够用于对我国上市公司信用风险的度量。二是验证不用行

业中KMV模型中违约点的长期负债系数值是否不同，从而对KMV模型根据行业

进行修改。在第一部分实证中，KMV模型对于机械行业中ST与非ST样本信用风

险区分的效果不如农业与化工行业理想，本文认为这是由于在不同的行业，长期

浙江财经学院硕士学位论文

负债的年度分布具有不同的特点，所以需要根据行业特点对KMV模型中违约点中

长期负债系数进行了修改，传统的这一系数是0.5，通过对各个行业中ST公司与非

ST公司的违约距离做均值对比，用最能区分两类样本的长期负债系数点作为本行

业用于KMV模型计算时的长期负债系数，从而得到农业、机械、化工三个行业的

违约点长期负债系数值分别为1、0.3、0.3，农业的这一值偏高是由于中长期负债

的额度较小，且长期负债分布的年限较短造成的，其它两个行业固定资产多，从

而使得该值偏低。计算不同行业中违约点的长期负债系数的意义在于银行在采用

该模型对不同行业上市公司进行信用风险度量时，需要对违约点的长期负债系数

值根据行业进行调整，这样在反映行业特点情况下度量信用主体的信用风险精准

度更高。三是验证KMV模型是否适合运用于无追索权保理业务中。通过样本计算

结果发现，在公司被ST前两个季度KMV模型计算出来的违约距离，公司的违约

距离就出现降低，从而信用风险增大的情况，说明KMV模型能够提前半年以上就

能预测出上市公司信用状况下降的情况，而无追索权保理属于短期的贸易融资，

期限都在一年以内，大部分只有半年的期限，这就说明银行对上市公司开展该项

业务之前，可以用KMV模型对此类业务中买卖双方的信用风险进行预测与估计，

申请该项业务的主体能达到银行要求的信用等级以上的就可以为其办理无追索权

保理业务，从而为银行开展此类业务提供借鉴。本文最后利用KMV模型对两个开

展了无追索权保理业务的公司进行了信用风险的度量，通过与短期偿债能力指标

进行对比，发现两种方法得到结果具有较好的一致性，验证了KMV模型对无追索

保理中的信用主体的信用状况具有较好的区别能力，从而能够将KMV模型运用于

无追索权保理业务的信用风险度量。

通过实证分析，得到本文的结论，即KMV模型是度量上市公司信用风险的有

效模型，并能够运用于无追索权保理中信用主体的信用风险度量，具体运用中要

根据行业特点来调整KMV模型违约点的长期负债系数，从而使得KMV模型度量

的结果符合行业特点，保证度量结果的精确性。

关键字：无追索权保理；信用风险；KMV 模型；违约距离；预期违约率

浙江财经学院硕士学位论文

III

ABSTRACT

With the formation of a buyer's market, companies have to take the way of credit to

expand sales of shares credit the way, lead to the existence of a large number of

receivables, in order to make up for the funding gap and accelerate cash flow, corporate

urgent need for financial institutions providing financial products against receivables,

factoring is developed in this context and is a comprehensive financial services product,

which provides the seller of credit investigation, credit risk guarantees, accounts

receivable management.

The ongoing the factoring business financial institutions, mainly banks, so this

bank as a factoring business to be discussed, and to carry out the biggest risk is the

credit risk of the factoring business, including the credit risk of the seller and the buyer,

the seller and the buyer's credit risk size directly related to the bank factoring credit

recoverability of non-recourse factoring factoring business is a risk, so banks how to

effectively measure the credit risk of the seller and the buyer is the key to carry out such

operations. Domestic in terms of credit risk measurement is relatively backward, mainly

traditional qualitative methods, makes it difficult on some high-credit-risk business

(such as non-recourse factoring) timely and accurate credit risk measurement KMV

model is based on the capital a forward-looking model of the market, credit risk early

warning and dynamic tracking well applied to measure the credit risk of non-recourse

factoring business, so this will be used as a measure of credit issuer credit risk tools.

And in factoring business objects, information transparency of listed companies, credit

status, in line with the non-recourse factoring of the the credit main level requirements

and high, so this will be listed as a goal to carry out non-recourse factoring Objects to

be analyzed.

The first non-recourse factoring in the credit risk of the type and characteristics of

the presentation and analysis, then summarized the theoretical basis of the current credit

risk measurement and model, in contrast the strengths and weaknesses of the model for

credit risk measurement, after discovery KMV model can be applied to non-recourse

factoring business in credit risk measurement characteristics, so non-recourse factoring

credit risk metrics used in the model, at the same time introduced the principle of the the

KMV model credit risk measure on how to set some of the parameters are described,

followed by an empirical analysis.

浙江财经学院硕士学位论文

In the empirical part of the paper is divided into three parts: First, to verify whether

the KMV model apply to the credit risk measurement of listed companies in China's

capital market. Approach is the choice the ST companies with normal-class companies

in the three sectors of the agricultural machinery industry, chemical industry as two

types of samples, verified KMV model has a better distinction whether these two types

of samples, the results show that the KMV model can basically to distinguish between

these two types of the company's credit risk profile, the the KMV model used measure

of the credit risk of listed companies in China. The second is to validate the industry not

in the KMV model default point is different from the value of long-term liabilities,

KMV model to be modified according to the industry. In the first part of the empirical,

KMV model distinguish the effect of the credit risk of ST and non-ST samples

machinery industry than ideal for agriculture and the chemical industry, the paper argues

that this is due to the annual distribution of long-term liabilities have different

characteristics in a variety of industries, so needs based on industry characteristics in

default point of the KMV model medium-and long-term liabilities coefficient modified

the traditional coefficient is 0.5, do mean contrast of ST companies in various industries

with non-ST distance to default, with the most able to distinguish between two types of

the long-term liabilities coefficient of the sample point as the industry long-term

liabilities coefficients used in the calculation of the KMV model, resulting in the long-

term liabilities of agriculture, machinery, chemical industry three industry default point

coefficient values were 1,0.3,0.3, the value of agriculture high due to the small amount

of medium-and long-term liabilities, short years of long-term liabilities distribution

caused by the fixed assets of the other two sectors, so that the value is low. The

significance of the calculation of the coefficient of the long-term liabilities of the default

point in different industries bank credit risk measurement of listed companies of

different industries in the model, the coefficient values of long-term liabilities for breach

of contract in accordance with industry to adjust to reflect industry characteristics

measure the credit risk of credit subject higher accuracy. Is to verify whether the KMV

model suitable used in non-recourse factoring business. Sample calculations found two

quarters before the company is ST KMV model calculated the distance to default,

breach the distance reduced, thus the increased credit risk, the KMV model can advance

more than six months will be able to predict the listing before the decline in corporate

credit situation, without recourse factoring is short-term trade finance, term less than

one year, only six months period, which the Bank to carry out the business of listed

浙江财经学院硕士学位论文

companies, can be used KMV model such business buyers and sellers of credit risk

prediction and estimation, the main application for the business can reach more bank

credit rating requirements for its handling non-recourse factoring business for the bank

to carry out such business learn from. Finally, use of the the KMV model on two

launched a non-recourse factoring company credit risk measure, by comparison with the

short-term solvency indicators, the results have a good agreement found between the

two methods, validation KMV model better distinction the credit status of the main non-

recourse factoring credit, enabling KMV on a non-recourse factoring business of credit

risk measure.

Through empirical analysis, the paper concludes that the KMV model is an

effective model to measure the credit risk of listed companies, and credit risk measure

can be applied to non-recourse factoring credit subject specific use, according to the

characteristics of the industry to adjust KMV coefficient of long-term liabilities of the

model point of default, making the KMV model to measure the results in line with the

characteristics of the industry to ensure the accuracy of the measurement results.

Keywords: non-recourse factoring; credit risk; KMV model; distance to default;

expected default

浙江财经学院硕士学位论文

第一章导论 ..................................................................................................................... 1

第一节研究背景及选题意义 .................................................................................. 1

第二节文章研究目标与结构 .................................................................................. 2

第三节本文研究思路及方法 .................................................................................. 3

第四节创新及不足 .................................................................................................. 4

第二章文献综述 ............................................................................................................. 6

第一节保理信用风险研究的文献综述 .................................................................. 6

第二节 KMV模型的国内外研究现状..................................................................... 7

第三节文献小结 .................................................................................................... 10

第三章无追索权保理信用风险及度量的理论基础 ................................................... 12

第一节无追索国内保理信用风险及发展现状 .................................................... 12

第二节信用风险度量的方法及比较 .................................................................... 14

第三节信用风险度量的KMV模型....................................................................... 19

第四章实证分析 ........................................................................................................... 23

第一节 KMV模型信用甄别能力的检验............................................................... 23

第二节行业违约点的设定 .................................................................................... 28

第三节 KMV模型运用于上市公司保理的适用性分析....................................... 33

第四节实证结果的分析 ........................................................................................ 38

第五章结论 ................................................................................................................... 40

第一节主要结论 .................................................................................................... 40

第二节对策建议 .................................................................................................... 41

参考文献 ........................................................................................................................ 43

附录 ................................................................................................................................ 46

致谢............................................................................................................................... 54

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摘要：

浙江财经学院硕士学位论文I摘要随着买方市场的形成，企业为了扩大销售份额纷纷采取赊销的方式，导致企业存在着大量的应收账款，为了弥补资金缺口与加快资金周转，企业迫切需要金融机构提供针对应收账款服务的金融产品，保理就是在这种背景下发展起来的，它是一种集贸易融资、卖方信用调查、信用风险担保、应收账款管理的综合性金融服务方案。国内目前开展保理业务金融机构主要是银行，所以本文将银行作为保理商来进行论述，而开展保理业务的最大风险便是信用风险，包括买卖双方的信用风险，买卖双方的信用风险大小直接关系到银行保理授信能否收回，无追索权保理又是保理业务中风险最大的一种，所以对银行来说如何有效度量买卖双方的信用风险是开...

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