10.2 practical issues in credit assessments

Copyright © 2018 CapitaLogic Limited
This presentation file is prepared in accordance with
Chapter 10 of the text book
“Managing Credit Risk Under The Basel III Framework, 3rd ed”
Website : https://sites.google.com/site/crmbasel
E-mail : crmbasel@gmail.com
Chapter 10
Practical Issues in
Credit Assessments

Copyright © 2018 CapitaLogic Limited 2
Declaration
 Copyright © 2018 CapitaLogic Limited.
 All rights reserved. No part of this presentation file may be
reproduced, in any form or by any means, without written
permission from CapitaLogic Limited.
 Authored by Dr. LAM Yat-fai (林日辉),
Director, CapitaLogic Limited,
Adjunct Professor of Finance, City University of Hong Kong,
Doctor of Business Administration,
CFA, CAIA, CAMS, FRM, PRM.

Outline
 Definition of default
 Domestic credit rating agencies
 Financial distress
 Altman's ZChina-score
 Earnings manipulation

Technical default
 A borrower fails to pay to the lender the
interest and/or principal in full on schedule
 Debt collection actions will then be initiated
by the lender to recover the whole or part of
the principal plus interest

Survival → overdue → default
Survival
Overdue
Default
Payment
Payment
Yes
Yes No
No

Cross default provision
 The default of a corporate bond will trigger
the default of the corporation and all corporate
bonds issued by the same corporation

Default under Basel III
 A borrower has an outstanding debt overdue for over ninety
days
 A borrower’s outstanding debt amount, after incorporating
the interest, has exceeded the prevailing credit limit
 A lender unilaterally reduces a borrower’s credit limit to an
amount below the current outstanding debt amount
 A lender is unlikely to recoup the principal and interest from
a borrower in full without taking recovery actions such as
selling collaterals pledged by the borrower

Outline

Domestic credit rating agencies
in America
 United States
 Egan-Jones Rating Company
 Canada
 DBRS
 Mexico
 HR Ratings
 Brazil
 Liberum Ratings

in Europe
 United Kingdom
 First Report
 Germany
 Scope Credit Rating
 Italy
 Cerved Group
 Russia
 National Rating Agency

in Japan
 Japan Credit Rating Agency Limited
 Rating and Investment Information, Inc.

in China
 Dagong Global Credit Rating
 Pengyuan Credit Rating
 China Chengxin (Asia Pacific) Credit Ratings Company
 China Lianhe Credit Rating
 Shanghai Brilliance Credit Rating & Investor Service

in India
 Brickwork Ratings India Private Limited
 Credit Analysis and Research Limited
 CRISIL
 ICRA Limited
 SMERA Ratings Limited

Basel III ECAI rating scale
Rating 3-year DR (%)
AAA and AA 0.10
A 0.25
BBB 1.00
BB 7.50
B 20.00

Outline

Default vs financial distress
 Default
 Some defaults but very few default records
 Data removed from major financial information providers
 Some countries apply confidential treatments to records of
defaulted corporations
 Horizon
 No. of defaults in 3 years > No. of defaults in 1 year
 A boarder definition of “bad” borrowers
 Records of financially distressed corporations are
accessible

Basel III ECAI Plus rating scale
Group Rating 3-year DR (%) PD (%)
Excellent AAA 0.03 0.0100
Good
AA (+,-) 0.10 0.0333
A (+,-) 0.25 0.0834
BBB (+,-) 1.00 0.3345
Moderate
BB (+,-) 7.50 2.5652
B (+,-) 20.00 7.1682
Bad
CCC 40.00 15.6567
CC 65.00 29.5270
C 95.00 63.1597

Financial distress of listed companies
 Rated “CCC” to “C” by credit rating agencies
 Listed companies
 Delisted due to reasons other than privatization
 Special treatment in China
 Negative cumulative earnings over two consecutive years
 Equity value < registered capital
 Likely to be dissolved
 Re-organization, settlement or liquidation
 Opinions from auditors
 Concerns from the CSRC

PD vs 3-year PFD
 3-year probability of financial distress
 Linear Probit regression to derive 3-year PFD
 
1
3
No. of defaults in 3 years
κ
No. of migrations to financial distress in 3 years
+ No. of defaults in 3 years
3-year DR = 3-year PFD κ
PD = 1 - 3-year PFD κ




High credit quality borrower
 Differentiation
 Good – to stay as investment grade in the
following three years
 Bad – to migrate to high yield grade or default in
the following three years

PD vs 3-year
probability of migration
1
3
No. of defaults in 3 years
κ
No. of migrations to high yield grade in 3 years
+ No. of defaults in 3 years
1 - Probabilty of migration to
PD = 1 -
high yield grade or default in 3 years κ

 
 
 

Low default borrower
 What is the PD of a group of
 Extremely high credit quality borrowers
 Very little or zero experience in default
 Justification
 Quantitative: Almost 0% historical default rate
 Qualitative: e.g. collateral = 10 times of EAD
Total no. of historical default records + 1
PD =
Total no. of historical records + 1

Outline

Altman’s ZChina-score for
listed companies in China
6
7
8
9
Total liabilities
X =
Total assets
Net profit
X =
Total assets
Currrent assets - Current liabilities
X =
Total assets
Retained earnings
X =
Total assets
 Explanatory variables

Altman’s ZChina-score for
listed companies in China

 Good ZChina > 0.9
 Moderate 0.5 < ZChina < 0.9
 Bad ZChina < 0.5
 Market value of equity/Total assets not an
explanatory financial ratio
China 6 7 8 9Z = 0.517 - 0.460X + 9.320X + 0.388X + 1.158X
Example 10.1

New lending operations
 Even financial distress records over 3 years
are not available
 Proxy by a similar group of listed companies
in terms of industry, country and asset size
 Cutoff scores and PD scaled up in accordance
with the experience of and subjective view
from a lender

Outline

SEC’s 2008 report on
financial statement frauds
38%
12% 12%
38%
Earnings manipulation Expenses manipulation
Improper disclosures Others

How good is the quality
of financial statements?
 All financial statements are subject to accounting
cosmetics
 To reject ALL loan applications assuming unreliable
quality
 Loss all lending businesses
 Good borrowers cannot obtain funding
 To accept ALL loan applications by assuming good
quality
 Potentially large default loss

Beneish’s M-score for
earnings manipulation
Net income - Cash flow from operations
TATA =
Total assets
Receivables
DSRI =
Total revenue
SGI = Total revenue
Fixed assets in current year - Property, plant and equipment
AQI =
Total assets
Total liab
LVGI =
ilities
Total assets
Selling, general and administrative expenses
SGAI =
Income
Gross profit
GMI =
Total revenue
Depreciation
DEPI =
Depreciation + Property, plant and equipment

Beneish’s M-score for
earnings manipulation

 Lower manipulation M > 2.22
 Moderate manipulation 1.78 < M < 2.22
 Higher manipulation M < 1.78
Examples 10.2 to 10.6
DSRI current year
M = 4.84 - 4.679 × TATA current year - 0.92 ×
DSRI previous year
SGI currrent year AQI current year
- 0.892 × - 0.404 ×
SGI previous year AQI previous year
LVGI current year
+ 0.327 ×
LVGI
SGAI current year
+ 0.172 ×
previous year SGAI previous year
GMI previous year DEPI previous year
- 0.528 × - 0.115 ×
GMI current year DEPI current year

Two-dimensional assessment

10.2 practical issues in credit assessments

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Ähnlich wie 10.2 practical issues in credit assessments

Ähnlich wie 10.2 practical issues in credit assessments (20)

Mehr von crmbasel

Mehr von crmbasel (17)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

10.2 practical issues in credit assessments