论文总字数：18098字

摘 要

互联网金融是一种新型的金融形态，它借助如今越来越发达、快捷的互联网工具进行金融业务办理。互联网金融业务相比于传统金融业务在融资方式上做出来较大改变：互联网平台为互联网金融提供了高效的业务办理和飞快的发展速度、减少了人们出行的时间和费用、覆盖了中国的大部分地区。但是，互联网金融不仅存在大量的网络安全风险问题，而且其所具有的信用风险同样不可忽视。本文运用KMV模型，对样本公司的违约距离(Default Distance,DD)和预期违约概率(Expected Default Frequency,EDF)进行测算，并根据影响互联网金融公司信用风险的因素进行实证分析，提出相应的有效建议。

第一步，充分介绍 KMV模型的结构，了解它在互联网金融公司应用的原理和计算方法；

第二步，选取16家已经上市的互联网金融公司为样本，运用MATLAB编写程序运行分别计算出样本公司2018年的违约距离(DD)和预期违约率（EDF），并对求出的数据进行分析研究，得出结论；

第三步，再运用Eviews软件对若干条影响公司信用风险的微观经济因素进行因子分析找出公共因子减少变量，再进行回归分析，做出相应优化改进。同时，也根据宏观经济因素进行相应的理论分析；

第四步，根据优化后模型中的影响因素做出相应性的建议，对信用风险进行有效的防范与控制。

关键词：互联网金融；信用风险；KMV模型；回归模型；影响因素

Measurement of Internet financial credit risk based on KMV model

And the empirical research of influencing factors

Abstract

Internet finance refers to a new type of finance that relies on Internet tools such as payment, cloud computing, social network and search engine to realize financing, payment and information intermediary business. Compared with the traditional financial business, Internet financial business has changed the traditional financing mode and has the advantages of low cost, high efficiency, wide coverage and fast development. Similarly, Internet finance not only has lots of network security risks, but also has credit risks that cannot be ignored. This paper uses the KMV model to calculate the default distance and expected default frequency of the sample companies, and makes an empirical analysis on the factors affecting credit risk, and puts forward corresponding effective suggestions.

Firstly, the structure of KMV model is fully introduced to understand the principle and calculation method of its application in Internet finance companies;

Secondly, 16 listed Internet finance companies are taken as samples, and the default distance (DD) and expected default frequency (EDF) of the sample companies in 2018 are calculated respectively based on KMV model by using MATLAB software, and the obtained data are analyzed and studied to draw conclusions;

Then, Eviews software is used to conduct regression analysis on several micro economic factors which are affecting corporate’s credit risk, and use factor analysis to reduce variables and make corresponding optimization and improvement.Also, according to the macroeconomic factors, author analyzes corresponding theories;

Finally, according to the influence factors in the optimized model, corresponding suggestions are made to prevent and control the credit risk effectively.

Key words: Internet finance; credit risk; KMV model; regression model; influenced factors

目录

第一章 引言 1

1.1 研究背景及意义 1

1.1.1 研究背景 1

1.1.2 研究意义 1

1.2 文献综述 2

1.2.1 KMV模型 2

1.2.2 互联网金融信用风险的度量 2

1.2.3 互联网金融信用风险的影响因素分析 3

1.2.4 文献述评 3

1.3 研究的技术路线 3

1.4 研究方法和创新点 4

第二章 基于KMV模型的互联网金融信用风险的度量及实证分析 5

2.1 KMV模型原理 5

2.2 基于KMV模型对互联网金融信用风险的度量 5

2.3 实证分析 6

2.3.1 参数设定 6

2.3.2 样本选取 7

2.3.3 标准化迭代算法 8

2.3.4 计算结果及比对分析 9

第三章 互联网金融信用风险的影响因素分析 11

3.1 影响互联网金融信用风险的因素 11

3.2 线性回归模型 11

3.2.1 数据选取及标准化 11

3.2.2 因子分析法 11

3.2.3 结果分析 14

第四章 互联网金融信用风险宏观分析 15

4.1 外部因素的影响 15

4.2 防范信用风险的措施 16

第五章 结论与展望 18

5.1结论 18

5.2展望 18

参考文献 19

致谢 20

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