"Gestión estratégica del riesgo de impago en el marco financiero internacional" Moody’s Analytics y la Cámara de Comercio de Madrid - Miércoles 5 de marzo de 2014
Moody’s Analytics Essential Insight Serving Global Financial Markets
MARCH 2014
Company Overview – March 2014
3
3
MA extends Moody’s brand beyond credit ratings
Leading global provider of credit rating opinions, insight, and tools for credit risk
measurement and management
Independent provider of credit rating
opinions and related information for
nearly 100 years
Research, data, software, and related
professional services for financial risk
management
Company Overview – March 2014
Built on a foundation of credit research, MA combines Moody’s insight with the expertise of market innovators
Quantitative Credit Analysis
Economic Analysis
Financial Education
Risk Management Software
2002 2005 2006 2008 2010
Credit Research
2011
Insurance Information
Knowledge Process
Outsourcing
4
Structured Debt Instruments
1914
Company Overview – March 2014 5
Credit Research
& Risk Measurement
Structured Analytics
& Valuation
Enterprise Risk
Solutions
Outsourcing
Solutions
Training &
Certification
Economic &
Consumer
Credit Analytics
Our integrated capabilities fall into five areas of expertise that address specific needs
Company Overview – March 2014 6
Access to our expertise can be customized to fit your unique information and workflow needs
Our integrated solutions include:
Superior Analyst &
Economist Access
Web-based Research,
Monitoring Tools & Data
Services
Software Tools & Related
Services
Advisory Services
Training
Outsourcing
Company Overview – March 2014
Find out more about our award-winning solutions
www.moodysanalytics.com
7
Company Overview – March 2014
moodysanalytics.com
© 2012 Moody’s Analytics, Inc. and/or its licensors and affiliates (collectively, “MOODY’S”). All rights reserved. ALL INFORMATION CONTAINED HEREIN IS PROTECTED BY COPYRIGHT
LAW AND NONE OF SUCH INFORMATION MAY BE COPIED OR OTHERWISE REPRODUCED, REPACKAGED, FURTHER TRANSMITTED, TRANSFERRED, DISSEMINATED,
REDISTRIBUTED OR RESOLD, OR STORED FOR SUBSEQUENT USE FOR ANY SUCH PURPOSE, IN WHOLE OR IN PART, IN ANY FORM OR MANNER OR BY ANY MEANS
WHATSOEVER, BY ANY PERSON WITHOUT MOODY’S PRIOR WRITTEN CONSENT. All information contained herein is obtained by MOODY’S from sources believed by it to be accurate
and reliable. Because of the possibility of human or mechanical error as well as other factors, however, all information contained herein is provided “AS IS” without warranty of any kind.
Under no circumstances shall MOODY’S have any liability to any person or entity for (a) any loss or damage in whole or in part caused by, resulting from, or relating to, any error (negligent or
otherwise) or other circumstance or contingency within or outside the control of MOODY’S or any of its directors, officers, employees or agents in connection with the procurement, collection,
compilation, analysis, interpretation, communication, publication or delivery of any such information, or (b) any direct, indirect, special, consequential, compensatory or incidental damages
whatsoever (including without limitation, lost profits), even if MOODY’S is advised in advance of the possibility of such damages, resulting from the use of or inability to use, any such
information. The ratings, financial reporting analysis, projections, and other observations, if any, constituting part of the information contained herein are, and must be construed solely as,
statements of opinion and not statements of fact or recommendations to purchase, sell or hold any securities. NO WARRANTY, EXPRESS OR IMPLIED, AS TO THE ACCURACY,
TIMELINESS, COMPLETENESS, MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OF ANY SUCH RATING OR OTHER OPINION OR INFORMATION IS GIVEN OR
MADE BY MOODY’S IN ANY FORM OR MANNER WHATSOEVER. Each rating or other opinion must be weighed solely as one factor in any investment decision made by or on behalf of
any user of the information contained herein, and each such user must accordingly make its own study and evaluation of each security and of each issuer and guarantor of, and each
provider of credit support for, each security that it may consider purchasing, holding, or selling.
Moody’s Analytics: Últimas tendencias y mejores prácticas en la medición del riesgo de crédito
Pablo Barbagallo – Associate Director EMEA Miércoles 5 Marzo 2014
Calculating Default Risk, March 2014 - Madrid 10
Evolución en la Gestión de
Riesgos
Calculating Default Risk, March 2014 - Madrid
Risk Management en 2008 - The Risk Management Function
11
Group CEO
Group Chairman
Board of Directors
Human Resources
and Organization
External Relations
Audit
Regulatory,
Environment and
Innovation
Administration,
Finance and Control
Legal and Corporate
Affairs
Global ICT Global Procurement Global Business
Services
Generation, Energy
Management
Infrastructure and
Networks
Division
Iberia and Latam
Division
Renewable Energies
Division
International
Division
Engineering and
Research
Division
Upstream Gas Carbon Strategy
Risk
Management
Calculating Default Risk, March 2014 - Madrid
Risk Management HOY - The Risk Management Function
12
Group CEO
Group Chairman
Board of Directors
Risk Management
Human Resources
and Organization
External Relations
Audit
Regulatory,
Environment and
Innovation
Administration,
Finance and Control
Legal and Corporate
Affairs
Global ICT Global Procurement Global Business
Services
Generation, Energy
Management
Infrastructure and
Networks
Division
Iberia and Latam
Division
Renewable Energies
Division
International
Division
Engineering and
Research
Division
Upstream Gas Carbon Strategy
Holding shaping
activities
Holding
Safeguarding
Activities
Servicing Activities
Business Lines
Group Operating
Model
Risk
Management
Holding
Function
Calculating Default Risk, March 2014 - Madrid
Risk Management - Organizational Structure
13
The Holding Risk Management Function, directly depending on the Group’s CEO, consists of 6 Holding
Risk Management Units and comprises a total of about one hundred people.
CRO
Commodity
Risk Management
Credit and
Counterparty
Risk Management
Financial , Strategic
and Country
Risk Management
Industrial and
Environmental
Risk Management
Insurance Enterprise
Risk Management
CEO •In order to ensure the right level
of steering and controlling, the
Holding Risk Management
Function is structured in specific
Units specialized by risk typology
•The Local Risk Management
Units, at Division/Country level,
have a functional reporting to the
Chief Risk Officer. Country 1 Country 2 Country 3 Country 4 ….
Calculating Default Risk, March 2014 - Madrid
¿Qué pasa si no me modernizo? Selección adversa de clientes
14
o George Akerlof recibió el Premio Nobel de Economía (2001) por sus
investigaciones en los mercados con información asimétrica,
desarrolló el modelo del "mercado de limones".
o Se refiere al proceso de mercado en el cual ocurren "malos"
resultados debido a las asimetrías de información entre vendedores
y compradores: los "malos" productos o clientes serán
probablemente los seleccionados depresión en el mercado
o En el área crédito: los malos clientes (adversos) piden y
probablemente reciben más crédito del que pueden soportar.
Calculating Default Risk, March 2014 - Madrid
El problema de los limones
o En los mercados de coches de segunda mano, la gente que
compra automóviles usados no sabe si son "limones" (automóviles
malos) o "cerezas" (automóviles buenos). Los vendedores, por
otra parte, sí tienen esta información. A un precio dado los
vendedores estarán más dispuestos a vender "limones" que
"cerezas", guardando los automóviles buenos para ellos. Así, los
compradores aprenderán a suponer que casi todos los
automóviles usados son "limones".
o Se llega a la conclusión de que en el mercado de segunda mano
casi no existen coches buenos.
Calculating Default Risk, March 2014 - Madrid
Si en lugar de coches hablamos de clientes (o proveedores):
• Nos resulta difícil distinguir entre ‘buenos’ y ‘malos’ clientes
(proveedores)
• Todos mis clientes pagan el mismo precio por el crédito (interés)
• Empresas ‘buenas’ reciben condiciones no competitivas compran
de mi competidor
• Empresas ‘malas’ pagan poco interés hacen negocio conmigo
• Trabajo solo con clientes ‘malos’ = solo pocos me pagan
• Seguros: los uso para toda mi cartera sin distinción costo elevado
• Inversores: poca (o nada) inversión en mi empresa
Calculating Default Risk, March 2014 - Madrid 17
Crédito y Pymes: Evolución en
Europa
Calculating Default Risk, March 2014 - Madrid 18
¿Qué puede ser considerado como Pyme?
EU SME Definition
Segment # of
Employee
Firm
Revenue Assets
Medium <250 ≤ €50M ≤ €43M
Small <50 ≤ €10M ≤ €10M
Micro <10 ≤ €2M ≤ €2M
-
1,000
2,000
3,000
4,000
Italy France Spain Germany UK
Medium Small Micro# of SMEs
(2012, 000’)
3,865K
2,509K 2,498K
2,064K
1,643K
# of medium
business 19K 20K 17K 53K 26K
# of mid-market
companies 62K 36K N/A 21K 22K
+ or
Sources: Eurostat
España es tercera por
numero de Pymes en
Europa con casi 2.5
millones.
Calculating Default Risk, March 2014 - Madrid 19
Crédito en Europa: Bank Lending
SME loans and total business loans, 2007-2011
Source: OECD, “Financing SMEs and Entrepreneurs 2013: An OECD Scoreboard”, April 2013.
109 118 121 112 104
541
657 583
536 506
0
100
200
300
400
500
600
700
2007 2008 2009 2010 2011
GB
P B
illio
n
UK
SME Total
181 190 190 201 211
872 931 940 974 1,013
0
200
400
600
800
1000
1200
2007 2008 2009 2010 2011
EU
R B
illio
n
France
SME Total
187 191 193 206 202
994 1063 1053 1084 1100
0
300
600
900
1200
2007 2008 2009 2010 2011
EU
R B
illio
n
Italy
SME Total
394 357 263 210 174
991 929
868
665
528
0
200
400
600
800
1000
1200
2007 2008 2009 2010 2011
EU
R B
illio
n
Spain
SME Total
Calculating Default Risk, March 2014 - Madrid
SMEs lending decreased in Europe. A key driver is the lack of “good”
information
Market context – SME funding supply
Ireland* Spain Portugal France Netherlands Italy
-82%
-66%
-45% -37% -32%
-21%
Volume of new loans to non-financial companies
Source: IIF-Bain report on restoring financing and growth to Europe’s SMEs (October 2013)
Note: Percentage decrease on loan volumes calculated on a country-by-country basis from pre-crisis peak to June 2013
Key drivers
Frontline
staff
Working capital
financing Shift
SME financing needs
Credit assessment more complex and
requires clear understanding of the
prospect of the business
Banks’ restructurings
Investment
financing
Focus on
profitability
Severed connections with SME owners and
reduced banks’ familiarity with SMEs
Availability of
information/reliable credit
risk analysis
Lack of accurate, comprehensive and
timely information on SMEs
1
3
A “Bad demand”: customers
delaying payments
B “Good demand”: sales growth (sometimes
through increased exports)
C Capital investments made in boom years created capacity
that is now underutilised
2
Calculating Default Risk, March 2014 - Madrid
Access to finance problem no. 2 for SMEs, after finding customers
Mini-bond is the response
Market context – Key issues for SMEs
Source: European Commission & ECB, “The Survey on the Access to Finance of Small and Medium-sized Enterprises (SAFE)”, Dec. 2011
Key issues faced by SMEs
24.1 27.621.7 20.4
24.1
15.115.7
15.413.6
15.1
13.69.3
1717.5
13.6
14.6 14.3 12.8 17.4 14.6
12.2 11.8 12.712.4 12.2
7.7 7.8 7.6 7.7 7.7
0
10
20
30
40
50
60
70
80
90
100
TOTAL - EU27 1-9 employees 10-49 employees
50-249 employees
SMEs (combined)
Pe
rce
nta
ge o
f re
spo
nd
en
ts
Regulation %
Costs of production or labour %
Competition %
Availability of skilled staff or experienced managers %
Access to finance %
Finding customers %
Calculating Default Risk, March 2014 - Madrid 22
Access to Finance Is the 2nd Pressing Issue for SMEs
What is currently the most pressing problem your firm is facing?
% of respondents
27
16 14
13 12 12
7
25
11
18
11
16
12
6
0
10
20
30
Findingcustomers
Access tofinance
Costs ofproduction or
labor
Availability ofhuman capital
Regulation Competition Other
SMEs Large Companies
Note: % is net responses ( “improved” less “deteriorated”). N-7,510 firms in the Euro area.
Source: EU Survey on the Access to Finance of Small and Medium-Sized Enterprises – October 2012 to March 2013.
Calculating Default Risk, March 2014 - Madrid 23
Key Barriers to SME Financing
SMEs’ credit demand shift from property-based credit to
working capital loans entails more complex and time-
sensitive judgment
Banks’ pressure to reduce cost by cutting frontline staff
and moving to centralized credit decisions
Information on creditworthiness is often costly, dated
and unreliable (not audited)
Information on SME credit worthiness
With a heavy dependence on domestic markets, SMEs
have been coping with a sharp drop in demand as
business, consumers and government cut expenditures
Funding and investment do not flow toward the highest-
potential SMEs
* Source: Institute of International Finance and Bain & Company, “Restoring Finance and Growth to Europe’s SMEs”, October 2013; Association of Financial Markets in Europe, “Unlocking
Funding for European Investment and Growth”, June 2013.
Financial health of SMEs
Banks to shoulder less credit risk than pre-crisis
Deleveraging and reduced risk appetite are constraining
banks’ ability to extend fresh credit
High fixed costs of assessing credit worthiness make it
challenging for banks to provide relatively small, short-
term loans
Alternative funding providers face many barriers
Insufficient returns of SME debt compared to other
available and more liquid assets
Large volume of analysis is required to understand the
risks of each firm
Analyzing SMEs is costly and extremely difficult given
the small ticket size and the number of firms
Calculating Default Risk, March 2014 - Madrid 24
Como hacer frente a estos
problemas
Calculating Default Risk, March 2014 - Madrid
Diferentes metodologías para el análisis del riesgo crediticio
Para un análisis completo las tres metodologías deben ser estudiadas
Moody’s Analytics ofrece soluciones para todos los análisis.
Modelos
Cuantitativos /
Cualitativos
Fundamental
Analysis
Análisis con
indicadores
de mercado
Calculating Default Risk, March 2014 - Madrid
Diferentes metodologías para el análisis del riesgo crediticio
Para un análisis completo las tres metodologías deben ser estudiadas
Moody’s Analytics ofrece soluciones para todos los análisis.
Modelos
Cuantitativos /
Cualitativos
Calculating Default Risk, March 2014 - Madrid 27
Enfoque del Modelo de Moody’s Analytics
RiskCalc es un modelo estadístico que combina:
1) Estados financieros específicos a su compañía
2) Información con predicciones para una industria, basadas en el mercado
3) La mayor base de datos sobre default de empresas privadas a nivel mundial
4) Un análisis cualitativo (cuestionario) que captura factores no fácilmente reflejados en
las cuentas anuales
Los modelos RiskCalc son específicos para cada país; utilizan información
de mercado, y son validados y calibrados frecuentemente.
Tenemos un compromiso de mejorar el planteamiento metodológico de
acuerdo a los cambios que ocurran, para mejorar el rendimiento (ej. Nuevos
modelos RiskCalc 3.1)
Calculating Default Risk, March 2014 - Madrid
RiskCalc Plus – Workflow
Calculating Default Risk, March 2014 - Madrid
Qualitative Overlay: 12 Preguntas
Industry/Market
Customer Power
Diversification of Products
Company
Years in Relationship
Supplier Power
Conduct of Account
Management
Experience in Industry
Financial Reporting and Formal Planning
Risk Appetite
Balance Sheet Factors
Audit Method
Debtor Risk/Accounts Recievable Risk
Pro-forma Liquidity
Pro-forma interest coverage
Calculating Default Risk, March 2014 - Madrid
Models Available in RiskCalc (by geography)
North America Europe & Africa 1 Canada 1 Russia
2 Mexico 2 Austria
3 USA 3 Belgium
4 US Banks 4 Denmark
5 US Insurance 5 Finland
6 Large Firm North America 6 France
7 Germany
Asia 8 Italy
1 China 9 Netherlands
2 Japan 10 Norway
3 Korea 11 Portugal
4 Australia 12 South Africa
5 Singapore 13 Spain
14 Sweden
15 Switzerland
Emerging Markets 16 United Kingdom
Rest of the World
1 Emerging Markets
2 International Banks
Calculating Default Risk, March 2014 - Madrid 31
Calculating Default Risk, March 2014 - Madrid
Riesgo de crédito de las Pymes:
0 2 1 3
3 años - EDF acumulado
3 años
EDF anualizado
3 años
EDF a futuro
Tiempo
¿y cual es el riesgo durante el 3er año si la
empresa X resiste los próximos 24 meses ?
¿Cual es la probabilidad que la empresa X no me
pague en los próximos 36 meses?
¿Cómo puedo transformar la medida anterior en
riesgo anual?
Calculating Default Risk, March 2014 - Madrid
Case Study
¿Cómo podemos saber si los ratios son ‘buenos’ ?
Calculating Default Risk, March 2014 - Madrid
Case Study
Contribución relativa de cada ratio:
Los intereses que esta pagando la empresa son muy elevados para
el nivel de ventas que tiene
Solo un ratio es ‘bueno’ para la empresa : Pasivo - Efectivos y valores/Activos
Calculating Default Risk, March 2014 - Madrid
Case Study
Gráfico de sensibilidad relativa:
La compañía debería mejorar el ratio Utilidades antes de I-I-A / Intereses para mejorar su Rating.
El EDF es menos sensible a los otros ratios del modelo.
Calculating Default Risk, March 2014 - Madrid
Análisis del Impacto del Ciclo Económico
36
Building the Stress Test Graph
Combines MKMV’s distance to default factor for the past 10 years with a company’s
current financial statements.
Calculating Default Risk, March 2014 - Madrid
Respondemos a 12 preguntas de tipo cualitativo
Calculating Default Risk, March 2014 - Madrid
Combined EDF Score (Quantitative + Qualitative)
38
Final combined borrower rating
Calculating Default Risk, March 2014 - Madrid 39
Análisis cuantitativo
Calculating Default Risk, March 2014 - Madrid 40
Pymes: Cálculo de la probabilidad de impago y rating implícito
Recolección y depuración de los datos
Definir los indicadores financieros
Selección de variables y determinación del peso de los indicadores
Ajuste de acuerdo al sector industrial
Ajuste de acuerdo al Ciclo de Crédito
Calculating Default Risk, March 2014 - Madrid 41
Moodys Analytics’ Credit Research Database (CRD)
La base de datos más grande del mundo con datos de Pymes
12 Million Unique Private Firms
50 Million Financial statements
800,000 Defaults Worldwide
| Funding Environment | SME Credit Assessment (Individual Firms)
Calculating Default Risk, March 2014 - Madrid 42
Identificar Indicadores Relevantes para Estimar Default
Liquidez Rendimiento Actividad
Cobertura Deuda
Endeudamiento
Tamaño Crecimiento
Dentro de cada
categoría se toma
un numero limitado
de indicadores que
tengan:
• Alta capacidad
predictiva
• Disponibilidad
datos
• Comportamiento
intuitivo
1 1
( )
N K
i i i j j
i j
FSO EDF F T x I
» Each transformed ratio [T(xi)] is included in the regression, along with indicator variables for each industry [Ij]
» F is the Final Calibration taking into account the Central Default Tendency
Probabilidad de Impago: EDF
Calculating Default Risk, March 2014 - Madrid
Selección de ratios que nos dan buena información
ratio
Density
Insolvent Solvent
ratio
Density
Insolvent Solvent
“bad” ratio “good” ratio
43
| Funding Environment | SME Credit Assessment (Individual Firms)
Calculating Default Risk, March 2014 - Madrid
Estadísticas sobre los datos de Pymes en España
44
| Funding Environment | SME Credit Assessment (Individual Firms)
Calculating Default Risk, March 2014 - Madrid 45
Los indicadores y sus pesos son específicos a un país
» Hemos identificado indicadores y pesos específicos a un país para captar
la diferencia en los mercados locales
Model => Switzerland UK Spain Russia Emerging Markets
Activity 25% 13% 21% 18,2% 12%
Debt Coverage 16% 11% 18% 10,8% 19%
Growth 6% 7% 16% 2,0% 9%
Leverage 17% 30% 16% 33,7% 19%
Liquidity 19% 8% 12% 14,1% 19%
Profitability 12% 28% 16% 21,2% 19%
Size 5% 3% 4%
Calculating Default Risk, March 2014 - Madrid
0%
2%
4%
6%
8%
10%
12%
14%
Feb
-98
Mar
-98
Apr
-98
May
-98
Jun-
98
Jul-9
8
Aug
-98
Sep
-98
Oct
-98
Nov-
98
Dec-
98
Jan-
99
Feb
-99
Mar
-99
Apr
-99
FSO Mode (Financial Data)
The impact of industry effects
Finl Statement Only EDF
The Impact of Industry Market Information
0%
2%
4%
6%
8%
10%
12%
14%
Feb
-98
Mar
-98
Apr
-98
May
-98
Jun-
98
Jul-9
8
Aug
-98
Sep
-98
Oct
-98
Nov-
98
Dec-
98
Jan-
99
Feb
-99
Mar
-99
Apr
-99
Industry Median EDF
FSO Mode (Financial Data)
The impact of industry effectsThe Impact of Industry Market Information
Industry Market Factor
Finl Statement Only EDF
0%
2%
4%
6%
8%
10%
12%
14%
Feb
-98
Mar
-98
Apr
-98
May
-98
Jun-
98
Jul-9
8
Aug
-98
Sep
-98
Oct
-98
Nov-
98
Dec-
98
Jan-
99
Feb
-99
Mar
-99
Apr
-99
EDF
Industry Median EDF
FSO Mode (Financial Data)
The impact of industry effects
Industry Market Factor
Fin. Statement Only EDF
RiskCalc CCA EDF
The Impact of Industry Market Information
¿Cómo mejorar el poder predictivo del modelo?
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Aug Jul Jun May Apr
2001 2002
46
| Funding Environment | SME Credit Assessment (Individual Firms)
Calculating Default Risk, March 2014 - Madrid
Evolución de los Ratings implícitos de las Pymes en España 2004-2010
47
Calculating Default Risk, March 2014 - Madrid
moodys.com
.................................................
Pablo Barbagallo
Product Specialist
EMEA Sales
+44 (0) 20 7772 1669 tel
+44 (0) 7730 910158 mobile
Moody's Analytics UK Ltd.
One Canada Square
Canary Wharf
London, UK E14 5FA
www.moodys.com
.................................................
Calculating Default Risk, March 2014 - Madrid 49
© 2009 Moody’s Analytics, Inc. and/or its licensors and affiliates (collectively, “MOODY’S”). All rights reserved. ALL INFORMATION CONTAINED HEREIN IS PROTECTED BY
COPYRIGHT LAW AND NONE OF SUCH INFORMATION MAY BE COPIED OR OTHERWISE REPRODUCED, REPACKAGED, FURTHER TRANSMITTED, TRANSFERRED,
DISSEMINATED, REDISTRIBUTED OR RESOLD, OR STORED FOR SUBSEQUENT USE FOR ANY SUCH PURPOSE, IN WHOLE OR IN PART, IN ANY FORM OR MANNER OR
BY ANY MEANS WHATSOEVER, BY ANY PERSON WITHOUT MOODY’S PRIOR WRITTEN CONSENT. All information contained herein is obtained by MOODY’S from sources
believed by it to be accurate and reliable. Because of the possibility of human or mechanical error as well as other factors, however, all information contained herein is provided “AS
IS” without warranty of any kind. Under no circumstances shall MOODY’S have any liability to any person or entity for (a) any loss or damage in whole or in part caused by, resulting
from, or relating to, any error (negligent or otherwise) or other circumstance or contingency within or outside the control of MOODY’S or any of its directors, officers, employees or
agents in connection with the procurement, collection, compilation, analysis, interpretation, communication, publication or delivery of any such information, or (b) any direct, indirect,
special, consequential, compensatory or incidental damages whatsoever (including without limitation, lost profits), even if MOODY’S is advised in advance of the possibility of such
damages, resulting from the use of or inability to use, any such information. The ratings, financial reporting analysis, projections, and other observations, if any, constituting part of
the information contained herein are, and must be construed solely as, statements of opinion and not statements of fact or recommendations to purchase, sell or hold any securities.
NO WARRANTY, EXPRESS OR IMPLIED, AS TO THE ACCURACY, TIMELINESS, COMPLETENESS, MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OF
ANY SUCH RATING OR OTHER OPINION OR INFORMATION IS GIVEN OR MADE BY MOODY’S IN ANY FORM OR MANNER WHATSOEVER. Each rating or other opinion must
be weighed solely as one factor in any investment decision made by or on behalf of any user of the information contained herein, and each such user must accordingly make its own
study and evaluation of each security and of each issuer and guarantor of, and each provider of credit support for, each security that it may consider purchasing, holding, or selling.
Moody’s Analytics CreditView A Comprehensive View of Credit
January 2014
Moody’s Analytics, January 2014
Our integrated capabilities fall into five areas of expertise that address specific needs
Credit Research
& Risk Measurement
Structured Analytics
& Valuation
Enterprise Risk
Solutions
Outsourcing
Solutions
Training &
Certification
Economic &
Consumer
Credit Analytics
51
Moody’s Analytics, January 2014
A comprehensive view of credit requires each of these approaches
Moody’s Analytics offers best-in-class solutions for each approach
Quantitative
Approach
Fundamental
Analysis
& Research
Pure Credit
Market Prices
Different approaches in Credit Risk Management
Moody’s Analytics, January 2014
Moody’s CreditView’s Content
Rating Agency Research and Ratings
Issuer News and Reports (including Credit Opinion)
Industry Research
Topic Research
History of Ratings
Key Indicators and Financials
As reported, as adjusted, adjustment details
Peer comparison functionality
Market Signals
Market Implied Ratings (CDS, Bond, Equity markets)
Public EDF (Quantitative Probability of Default)
Monthly Default Reports and Default studies
Covenant Research and Database (HY Corps)
53
Portfolio and Alerts
Access to Moody’s Analysts
24/5 Customer Service
Events, conferences, webinars
Moody’s Analytics, January 2014
Moody’s CreditView – Rated Issuers Corporates, Financial Institutions, Sovereigns, Structured Finance, Covered Bonds
54
Rating Agency Research
Financial and Credit Ratios
Market Signals
Moody’s Analytics, January 2014
Workflow – Review the MIS Opinion and Ratings
Credit Opinion Description
• An excerpt of the four most requested sections of the Credit Opinion:
1. Rating Rationale 2. Rating Outlook 3. What Would Change the Rating – Up 4. What Would Change the Rating – Down
• Link to the Rating Factor Grid from the Credit Opinion
• Link to the full version of the Credit Opinion
Moody’s Analytics, January 2014
Recent Issuer News and Reports Description
• Up-to-date and forward-looking commentary and
opinions help you stay informed of evolving credit
concerns.
• Shows the last 120 days of issuer specific research
(no more than 5), plus anything written since the
Credit Opinion was published.
Workflow – Review Recent Issuer News and Reports
Moody’s Analytics, January 2014
Industry Research Description
• Periodic newsletters and industry research focus a
spotlight on the trends impacting credit, including macro-
and cross-discipline perspectives.
• Shows the last 2 years of Industry research with a
maximum of 5 reports.
• Selection is based on the Industry and Peer Group
designation of the issuer.
Workflow – Review Industry Research
Moody’s Analytics, January 2014
Workflow – Review Covenant Research (1/1)
Covenant Research Description
• As money flows back into the corporate debt markets,
understanding the details of the bond indentures protecting
your portfolio has become more important than ever.
Moody’s Covenant Quality Assessments (CQAs) help you
identify key risks lurking in the fine print and save you time
and worry over the details.
• This section displays the latest Covenants research for an
issuer.
Moody’s Analytics, January 2014
Workflow – Review Covenant Research (2/2)
59
Moody’s Analytics, January 2014
Workflow – Market Signals
Market Signals
• Moody’s Investors Service Rating: Current LT rating that matches the rating at the top of
the Overview page
• Market Implied Ratings (MIR) compares the signals for a given company to market-
wide measures allowing you to isolate changes in risk for individual issuers from the
noise of the markets.
• Expected Default Frequency (EDF) - a measure of the probability that a firm will default
over a specified period of time based on equity prices, financials and capital structure.
Market Signals Tab
Moody’s Analytics, January 2014
Market Signals Tab – View Market Implied Ratings
61
Link to Interactive Charts Page
Moody’s Analytics, January 2014
Market Signals Tab – View Public EDF
62
Moody’s Analytics, January 2014 63
• Telefonica displays the typical pattern of market changes leading MIS ratings.
• This is to be expected. Markets react instantly to negative news about an issuer. MIS follows a more deliberate
process. The rating system values stability, and strives to “rate through the cycle”. Market signals are more “point in
time”.
Market Implied Ratings vs. Moody’s Rating
Moody’s Analytics, January 2014
Interactive Charts – Median Credit Spreads
64
Moody’s Analytics, January 2014 65
• The Global
Telecommunications
sector has a CDC IR
gap of zero. This
makes Telekom
Malaysia’s gap of -2
stand out.
•Corporate sectors
are largely viewed as
lower risk vs. their
MIS ratings than
financials.
Users’ Tip: Use the
Sector view to
compare ratings gaps
across sectors, both
for relative value and
relative risk purposes
. Users’ Tip: If a
name is trading
cheaply to its sector,
it’s an especially
strong risk signal.
Interactive Charts – Sectors: MIR Sector Review
Sectors > Global
CDS Implied Gap Avg: 0
Moody’s Analytics, January 2014
Interactive Charts – Sectors: MIR Scatter Plot
66
Moody’s Analytics, January 2014 67
• You can use the
MIR Movers
feature to compare
an entity’s implied
ratings changes vs.
those of other
issuers over the
same time period.
Interactive Charts – Sectors: MIR Movers
Moody’s Analytics, January 2014 68
A company with a senior rating of A2 and a CDS-IR Gap of -5 notches has suffered a downgrade of 43% of the time over
the last 12 months, according to this rating gap-conditioned transition matrix.
Users Tip: This shows the tendency of MIS ratings to be “pulled towards” market trading levels. It reflects the markets’
ability to react to news more quickly than rating agencies, or than clients’ internal credit processes.
Interactive Charts – Transition Matrices
Moody’s Analytics, January 2014 69
Market Implied Ratings: Portfolio and Alerts
Moody’s Analytics, January 2014
Workflow – Financials for Corporates
Financial and Credit Ratios
• Key credit indicators relevant to the specific issuer, based on
Moody’s industry-specific rating methodology.
• Provide an upfront view of company performance and the ability
to drill down to the issuer’s financials - both ‘as-reported’ by the
company and ‘as-adjusted’ by Moody’s analysts.
• Moody’s Analyst adjusted financial data is globally comparable
and provides complete transparency and insight into what drives
Moody’s Corporate ratings.
Financial Metrics
Moody’s Analytics, January 2014
Key Indicators and Financials (1/2)
71
Look up a single name and analyze Financials
As Reported Data – Reported data is mapped
to Moody’s chart of accounts
Adjusted Data & Adjustment Details –
access to globally comparable financial data,
Standard and non-standard adjustments*
made by Moody’s Analysts
Credit Ratios – over 80 credit ratios on every
company
Moody’s Analytics, January 2014
Key Indicators and Financials (2/2)
72
Create a Portfolio, Run Reports and Compare Peers
Moody’s Analytics, January 2014
Workflow – Monthly Default Reports
• Monthly report containing default and
credit quality statistics, and market
commentary that enables you to monitor
the corporate bond market.
Moody’s Analytics, January 2014
74
Top 10 investor benefits and applications of CreditView
Top 10 Investor Benefits and Applications
1. 1. Make objective decisions with confidence.
2. 2. Review the most crucial components of an issuer’s Credit Opinion.
3. 3. Stay on top of evolving credit concerns.
4. 4. Identify key risks in the fine print using covenant quality research.
5. 5. Detect credit deterioration early with MIR®.
6. 6. Improve your default prediction.
7. 7. Screen for potential ratings changes.
8. 8. Conduct portfolio-level relative value analysis.
9. 9. Differentiate default rates by ratings gaps.
10. 10. Recognize different default patterns by rating category.
Escenarios Económicos Alternativos Efectos sobre Carteras de Crédito Corporativo
Dr. Juan M. Licari
Jefe del Área de Economía y Análisis Crediticio para Europa
Gestión estratégica del riesgo de impago en el marco financiero internacional
Cámara Oficial De Comercio e Industria de Madrid – 5 de Marzo de 2014
Escenarios Económicos Alternativos Efectos sobre Carteras de Crédito Corporativo
76
Agenda:
- Diseño de escenarios macroeconómicos: balance entre herramientas
cuantitativas y juicio experto.
- Conexión entre variables macro/sectoriales y riesgo crediticio:
1) Matrices de migración crediticia.
2) Modelos directos de default y pérdidas.
3) Modelaje de los factores que determinan defaults y pérdidas.
- Riesgo país y de mercado.
5 de Marzo de 2014
Cámara Oficial De Comercio e Industria De Madrid
c/Ribera del Loira 56-58 (Campo de las Naciones)
28042 Madrid
Escenarios Económicos Alternativos
77
Stronger Near-Term Rebound S1
S2 Mild Second Recession
S3 Deeper Second Recession
Protracted Slump S4
Baseline / Most Likely BL
Standard
Below Trend Long Term Growth S5
Oil Price Shock S6
Fed Baseline FB
Fed Adverse Scenario FA
EC-EBA Baseline EB
EC-EBA Adverse ES
Regulatory Driven
PRA-BoE Baseline UKB
Fed Severely Adverse FS
PRA-BoE Severe UKS
PRA-BoE Idiosyncratic UKS
78
Source: Moody’s Analytics
Unemployment Rate, (%, SA)
Nominal Exchange Rate, (USD per EUR)
GDP at Market Prices, % change (Bil. 2009 EUR, SAAR)
Consumer Price Index, y/y % change (2005=100)
Escenarios Económicos Alternativos – Euro Zone
79
Source: Moody’s Analytics
Escenarios Económicos Alternativos – Spain GDP at Market Prices, % change (Bil. 2008 EUR, SAAR)
House Price Index, (2005Q1=100, SA) Consumer Price Index, y/y % change (2005=100)
Unemployment Rate, (%, SA)
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Escenarios Económicos Alternativos Simulation-Based Outcomes – Euro-Zone Inflation Example
Inflation Rate,
History & Forecasts,
Euro-Zone Level
Inflation Rate
Distribution, Euro-
Zone Level
Inflation Rate
Distribution, Euro-
Zone Level
Conexión entre variables macro/sectoriales y riesgo crediticio
(1) Credit Rating Transitions Approach
Transition matrices for credit portfolios, two stage approach:
(i) probit model combined with (ii) quantile and time-series analysis
(2) Stressing PDs Directly
Time-Series and Dynamic Panel Data Techniques
(3) Stressing the key drivers of the PD models
Multivariate Time-Series and Dynamic Panel Data Techniques
81
82
(1) Stress Testing of Credit Migrations
Table 1 Average probabilities (1983M1 - 2007M1)
Aaa Aa A Baa Ba B Caa-c Def
Aaa 92.10% 7.52% 0.33% 0.00% 0.04% 0.00% 0.00% 0.00%
Aa 0.99% 90.49% 8.07% 0.37% 0.04% 0.03% 0.00% 0.02%
A 0.07% 2.76% 90.65% 5.67% 0.65% 0.15% 0.03% 0.02%
Baa 0.05% 0.24% 5.51% 87.91% 4.75% 1.14% 0.23% 0.17%
Ba 0.01% 0.07% 0.47% 6.35% 82.56% 8.60% 0.60% 1.33%
B 0.01% 0.05% 0.18% 0.52% 5.52% 82.90% 4.74% 6.08%
Caa-c 0.00% 0.02% 0.10% 1.20% 1.19% 7.12% 69.42% 20.96%
Table 2 Average probabilities (2007M6 - 2009M10)
Aaa Aa A Baa Ba B Caa-c Def
Aaa 78.15% 21.71% 0.04% 0.11% 0.00% 0.00% 0.00% 0.00%
Aa 0.05% 82.65% 16.03% 0.99% 0.11% 0.02% 0.07% 0.09%
A 0.00% 0.88% 89.58% 8.24% 0.44% 0.30% 0.15% 0.41%
Baa 0.01% 0.14% 2.20% 91.95% 4.40% 0.72% 0.20% 0.38%
Ba 0.00% 0.00% 0.04% 5.10% 81.25% 10.46% 1.83% 1.32%
B 0.00% 0.00% 0.07% 0.17% 3.35% 78.31% 13.55% 4.55%
Caa-c 0.00% 0.00% 0.00% 0.14% 0.23% 5.74% 71.19% 22.70%
Figure I: Bi-Modal Nature of Credit Transitions Bi-Modal Distribution of Baa to Ba Credit Migrations (Bar Chart) vs. a Normal, Symmetric Distribution (Green Solid Line)
01
02
03
04
0
Den
sity
0 .02 .04 .06 .08 .1baa_ba
83
0.2
.4.6
.81
Tra
nsi
tion
%
2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1Month of Transition
Binary_Probit_Regression O_1_Median_Variable
0.2
.4.6
.81
Tra
nsi
tion
%
2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1Month of Transition
Binary_Probit_Regression O_1_Median_Variable
Binary (Probit) Model Downgrade
0.1
.2.3
.4
Tra
nsitio
n %
2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1Month of Transition
Actuals Baseline
FSA Scenario4
Custom
0
.02
.04
.06
.08
Tra
nsitio
n %
2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1Month of Transition
Actuals Baseline
FSA Scenario4
Custom
CaaC to Default Baa to A
Binary (Probit) Model Upgrade
(1) Stress Testing of Credit Migrations
84
Firm-
level effects
Industry-level effects
Aggregate-level effects
Idiosyncratic risk matters
Industry- & credit quality-
specific sensitivities to macro
drivers
Rank order of firms varies
with economic conditions
Shape of economy-
wide distribution of
credit risk varies
with economic
conditions
(2) Stress Testing of PDs
0.00
0.05
0.10
0.15
0.20
0.00 0.05 0.10 0.15 0.20
Baseline vs. recession scenarios
Source: Moody’s Analytics
Current PD
Fu
ture P
D
Baseline Scenario
Recession Scenario
85
(2) Stress Testing of PDs
Under the S4 scenario GDP growth is
negative…
… which translates into high probabilities
of default
86
EDF (Expected Default Frequency):
Market-driven estimate of the probability that a company will default within the next year
EDF Drivers: (i) Asset Returns, (ii) Asset Volatility and (iii) Default Point
(3) Stress Testing of PD Drivers Illustration from option-pricing PD modes
1995 2000 2005 2010
Year
1995 2000 2005 2010
Year
1995 2000 2005 2010
Year
1995 2000 2005 2010
Year
1990 1995 2000 2005 2010
Year
Construction Consumer Products Health Care
Services Trade
R
ate
s (
%)
-10 -5
0
5
1
0
R
ate
s (
%)
-1
0 -5
0
5
1
0
R
ate
s (
%)
-1
0 -5
0
5
1
0
R
ate
s (
%)
-10 -5
0
5
1
0
R
ate
s (
%)
-10 -5
0
5
1
0
Sales Growth Rates
GDP Growth Rates
(3) Stress Testing of PD Drivers Balance-sheet driven PD models
87
MACROECONOMIC
Driver North
America
Western
Europe
Real GDP growth X X
Real consumption growth X
Real investment growth X
Real export growth X X
Unemployment rate X X
CPI inflation rate X X
PPI inflation rate X X
Corporate profit growth X
FINANCIAL
Driver North
America
Western
Europe
Stock index growth X X
Yield curve X X
Short-term interest rate X
Baa spread X X
Ted spread X X
S&P 500 volatility X
* Wherever possible, firms are matched up with macroeconomic or financial data specific to their country of incorporation. The W. Europe
models also include US real GDP growth, to proxy for global growth. In the aggregate-level models, we use weighted averages of the
constituent countries’ macro drivers, where the weights are based on each country’s representation in the Public Firm EDF universe. The yield
curve is defined as the long-term less the short-term government bond rate. The Baa spread is defined as the Moody’s Baa yield less the 10-
year Treasury yield. The Ted spread is defined as 3-month LIBOR less the 3-month T-bill yield. The 30-day moving average of the standard
deviation of the percent change in the S&P 500 is used to measure volatility.
88
(3) Stress Testing of PD Drivers Examples of Macroeconomic Factors behind the ST exercise
Income Statement
Sales/Revenue
-Cost of Goods Sold (COGS)
-Selling, General and Administrative Expense (SGA)
-Depreciation/Amortization (AMORT)
-Other Operating Expense (OthrExp)
Total Operating Profit
+Financial Income
-Interest Expenses
Profit before Tax
-Tax
Net Income
Responds to the Cycle
Responds to Interest Rates
Variable costs such as Cost
of Goods Sold move
together with changes in
Sales.
Fixed costs, such as
Depreciation/Amortization
move slowly when Sales
decrease.
Sales Growth
COGS Changes
SGA Changes
Interest Expense
Changes
A Pro-Forma Income Statement Relates
Changes in Sales to Changes in Income
(3) Stress Testing of PD Drivers Balance-sheet driven PD models
89
.01
.02
.03
.04
.05
2012m1 2014m1 2016m1 2018m1
DANONE
.1.2
.3.4
.5.6
2012m1 2014m1 2016m1 2018m1
MARKS AND SPENCER GROUP PLC
.2.4
.6.8
1
2012m1 2014m1 2016m1 2018m1
BAYERISCHE MOTOREN WERKE AKTIENGESE
.02
.04
.06
.08
.1
2012m1 2014m1 2016m1 2018m1
EXPERIAN PLC
BL S1 S2 S3 S4 Source: Moody’s Analytics
90
(3) Stress Testing of PD Drivers Final output: Firm-level stressed PDs
Riesgo País y de Mercado
91
92
Country Risk Modelling Through-the-cycle view: Fiscal Space and Survival Rates
Fiscal Space Survival 10-Yr Yield Fiscal Space Survival 10-Yr Yield
Increase in debt-
to-GDP ratio, ppts
Upper limit on 10-
year bonds, %
Increase in debt-
to-GDP ratio, ppts
Upper limit on 10-
year bonds, %
South Korea 243 > 10 Netherlands 161 7.1
Australia 240 > 10 Canada 157 8.1
Hong Kong 234 9.3 Germany 151 6.0
Norway 230 > 10 Austria 143 5.3
Singapore 225 8.6 U.K. 132 6.7
New Zealand 224 > 10 France 121 5.3
Taiwan 218 6.6 Iceland 118 > 10
Luxembourg 217 6.9 Belgium 104 6.2
Sweden 203 6.5 Spain No Space 5.0
Denmark 192 8.1 Ireland No Space 7.2
Switzerland 191 6.1 Italy No Space 2.8
Israel 180 > 10 Portugal No Space 4.7
Finland 179 5.7 Greece No Space 2.0
U.S. 172 8.9
Source: Moody's Analytics
93
20
40
60
80
100120
2009m1 2010m7 2012m1 2013m7 2015m1 2016m7Months
f_swisscdsusdsr5ycorp_blf_swisscdsusdsr5ycorp_s1
f_swisscdsusdsr5ycorp_s2
020
40
60
2009m1 2010m7 2012m1 2013m7 2015m1 2016m7Months
f_zctocdseursr5ycorp_bl f_zctocdseursr5ycorp_s1
f_zctocdseursr5ycorp_s2
050
100150200250
2004m12006m12008m12010m12012m12014m12016m1Months
f_chinagovcdsusdsr5ycorp_blf_chinagovcdsusdsr5ycorp_s1
f_chinagovcdsusdsr5ycorp_s2
Country Risk Modelling Point-in-time and market driven analytics
94
Stress Testing of Swap Rate Curves
-15
-10
-50
5
leve
l PC
A
.51
1.5
2
leve
l DN
S
2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1
DNS PCA
EUR Swap Curve
Level Factor
-2-1
01
2
slo
pe P
CA
01
23
slop
e D
NS
2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1
DNS PCA
EUR Swap Curve
Slope Factor
02
46
Rate
s,%
2000
m1
2001
m1
2002
m1
2003
m1
2004
m1
2005
m1
2006
m1
2007
m1
2008
m1
2009
m1
2010
m1
2011
m1
2012
m1
2013
m1
Maturities: 1M-360M
Euro Swap Rates
95
-10
12
3
Term
Pre
miu
m
02
46
Rate
s (
%)
2000
m1
2002
m1
2004
m1
2006
m1
2008
m1
2012
m1
2014
m1
2016
m1
2018
m1
2010
m1
EURO Swap Curve: Baseline
Stress Testing of Swap Rate Curves
-10
12
3
Te
rm P
rem
ium
02
46
Rate
s (
%)
2000
m1
2002
m1
2004
m1
2006
m1
2008
m1
2010
m1
2012
m1
2014
m1
2016
m1
2018
m1
Euro Zone Crisis
EUR Swap Curve vs Term Premium
© 2014 Moody’s Analytics, Inc. and/or its licensors and affiliates (collectively, “MOODY’S”). All rights reserved. ALL INFORMATION CONTAINED HEREIN IS PROTECTED BY
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96