100%
根据发生的可能性，可将信用风险损失分为三类： – 预期损失：银行合理预计信贷损失的平均值，是贷款成本的一部分，通过对贷款定价和提取准备金等进 行管理 – 非预期损失：超出预期水平的损失；随时发生，但不能预知发生时间及严重程度；利差收入难以弥补， 需动用资本金 – 极端损失：通过压力测试来检验。
5 金融工程案例分析课程， George Yuan， 2013-14
Altman Z-score Introduction
1 Estimation of the formula
The original Z-score formula was as follows: Z = 1.2T1 + 1.4T2 + 3.3T3 + 0.6T4 + 0.999T5.
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目录
一
信用风险管理的基本理论
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中南财大讲学大纲
信用风险和资产证券化衍生品定价方法介绍 袁先智（同济大学风险管理研究所）
第一部分: 信用风险理论介绍（2014年7日上午9:00am-11:30am） 第1章 信用风险介绍和信用评级 第2章 Merton 信用风险模型, KMV等模型应用介绍 第3章 信用风险价值量调整(CVA)介绍 第4章 单因子结构模型和信用风险价值量(Credit VaR) 介绍
9 金融工程案例分析课程， George Yuan， 2013-14
Altman Z-score Example
10 金融工程案例分析课程， George Yuan， 2013-14
Altman Z-score Example
11 金融工程案例分析课程， George Yuan， 2013-14
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目录
Altman Score in 信用风险分析
一
1. Altman Z-score Introduction 2. Original z-score component definitions variable definition weighting factor 3. Z-score estimated for private firms
7 金融工程案例分析课程， George Yuan， 2013-14
Altman Z-score Introduction
3. Z-score estimated for private firms Z' Score Bankruptcy Model: Z' = 0.717T1 + 0.847T2 + 3.107T3 + 0.420T4 + 0.998T5 T1 = (Current Assets − Current Liabilities) / Total Assets T2 = Retained Earnings / Total Assets T3 = Earnings Before Interest and Taxes / Total Assets T4 = Book Value of Equity / Total Liabilities T5 = Sales/ Total Assets Zones of Discrimination: Z' > 2.9 -“Safe” Zone 1.23 < Z' < 2.9 -“Grey” Zone Z' < 1.23 -“Distress” Zone
8 金融工程案例分析课程， George Yuan， 2013-14
Altman Z-score Introduction
4. Z-score estimated for non-manufacturers & emerging markets T1 = (Current Assets − Current Liabilities) / Total Assets T2 = Retained Earnings / Total Assets T3 = Earnings Before Interest and Taxes / Total Assets T4 = Book Value of Equity / Total Liabilities Z-Score bankruptcy model: Z'' = 6.56T1 + 3.26T2 + 6.72T3 + 1.05T4 Zones of discriminations: Z" > 3.75 -“Safe” Zone 1.1 < Z" < 3.75 -“Grey” Zone Z" < 1.75 -“Distress” Zone
T1 = Working Capital / Total Assets. Measures liquid assets in relation to the size of the company. T2 = Retained Earnings / Total Assets. Measures profitability that reflects the company's age and earning power. T3 = Earnings Before Interest and Taxes / Total Assets. Measures operating efficiency apart from tax and leveraging factors. It recognizes operating earnings as being important to long-term viability. T4 = Market Value of Equity / Book Value of Total Liabilities. Adds market dimension that can show up security price fluctuation as a possible red flag. T5 = Sales/ Total Assets. Standard measure for total asset turnover (varies greatly from industry to industry). Altman found that the ratio profile for the bankrupt group fell at -0.25 avg, and for the nonbankrupt group at +4.48 avg.
Credit Ratings
In the S&P rating system, AAA is the best rating. After that comes AA, A, BBB, BB, B, CCC, CC, and C The corresponding Moody’s ratings are Aaa, Aa, A, Baa, Ba, B,Caa, Ca, and C Bonds with ratings of BBB (or Baa) and above are considered to be “investment grade”
12 金融工程案例分析课程， George Yuan， 2013-14
关于风险的基本概念
风险：银行为了追求收益导致未来结果的不确定性
风险定义
风险描述：一般用未来结果的分布来描述，用给定假设条件下未来
结果对期望的偏离来计量。
风险பைடு நூலகம்损失
损失是风险事件发生后的实际结果； 风险是不确定性，但风险不等同于损失； 风险用潜在的损失规模和概率来计量。
风险与收益
承担风险是追求收益的必然结果，“高收益低风险” 只有承担风险才能带来收益，“零风险” 对收益的衡量，需要考虑风险因素，即经风险调整的收益
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信用风险可能带来的三种损失
信用风险损失的概率分布
概 率 A线 B线
99.9%
0.1%
损失（风险大小）
预期损失 VAR

非预期损失
极端损失
6 金融工程案例分析课程， George Yuan， 2013-14
Altman Z-score Introduction
2. Original z-score component definitions variable definition weighting factor T1 = Working Capital / Total Assets T2 = Retained Earnings / Total Assets T3 = Earnings Before Interest and Taxes / Total Assets T4 = Market Value of Equity / Total Liabilities T5 = Sales/ Total Assets Z score bankruptcy model: Z = 1.2T1 + 1.4T2 + 3.3T3 + 0.6T4 + .999T5 Zones of Discrimination: Z > 2.99 -“Safe” Zones 1.81 < Z < 2.99 -“Grey” Zones Z < 1.81 -“Distress” Zones
4. Z-score estimated for non-manufacturers & emerging markets
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目录-Altman Score in 信用风险分析
1. Altman Z-score Introduction
1 Estimation of the formula The Z-score is a linear combination of four or five common business ratios, weighted by coefficients. The coefficients were estimated by identifying a set of firms which had declared bankruptcy and then collecting a matched sample of firms which had survived, with matching by industry and approximate size (assets). Altman applied the statistical method of discriminant analysis to a dataset of publicly held manufacturers. The estimation was originally based on data from publicly held manufacturers, but has since been re-estimated based on other datasets for private manufacturing, non-manufacturing and service companies. The original data sample consisted of 66 firms, half of which had filed for bankruptcy under Chapter 7. All businesses in the database were manufacturers, and small firms with assets of < $1 million were eliminated.