摘要翻译:
该模型旨在根据俄罗斯会计准则中的财务报表数据来区分俄罗斯企业部门中的“好”和“坏”公司。数据样本包括126家俄罗斯上市公司--卢布债券发行人,约占公司债券发行人总数的36%。2008-2009年间有25家公司债务违约,约占违约案例的30%。样本中没有SPV公司。该模型显示样本内基尼系数约为73%,并给出了一个合理而简单的外部评级映射规则。该模型可用于计算俄罗斯公司的隐含信用评级,而许多俄罗斯公司并不具备这一评级。
---
英文标题:
《Simple Fuzzy Score for Russian Public Companies Risk of Default》
---
作者:
Sergey Ivliev
---
最新提交年份:
2010
---
分类信息:
一级分类:Quantitative Finance 数量金融学
二级分类:Risk Management 风险管理
分类描述:Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
--
一级分类:Quantitative Finance 数量金融学
二级分类:Statistical Finance 统计金融
分类描述:Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
--
---
英文摘要:
The model is aimed to discriminate the 'good' and the 'bad' companies in Russian corporate sector based on their financial statements data based on Russian Accounting Standards. The data sample consists of 126 Russian public companies- issuers of Ruble bonds which represent about 36% of total number of corporate bonds issuers. 25 companies have defaulted on their debt in 2008-2009 which represent around 30% of default cases. No SPV companies were included in the sample. The model shows in-sample Gini AR about 73% and gives a reasonable and simple rule of mapping to external ratings. The model can be used to calculate implied credit rating for Russian companies which many of them don't have.
---
PDF链接:
https://arxiv.org/pdf/1004.0685