moody's analytics - gestión estratégica del riesgo de impago ......2014/05/03 ·...

"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

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

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

Insurance Information

Knowledge Process

Outsourcing

Structured Debt Instruments

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

Find out more about our award-winning solutions

www.moodysanalytics.com

moodysanalytics.com

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

Group CEO

Group Chairman

Board of Directors

Human Resources

and Organization

External Relations

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

Management

Risk Management HOY - The Risk Management Function

Group CEO

Group Chairman

Board of Directors

Risk Management

Human Resources

and Organization

External Relations

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

Management

Holding

Function

Risk Management - Organizational Structure

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.

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 ….

¿Qué pasa si no me modernizo? Selección adversa de clientes

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.

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.

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

Crédito y Pymes: Evolución en

Europa

¿Qué puede ser considerado como Pyme?

EU SME Definition

Segment # of

Employee

Revenue Assets

Medium <250 ≤ €50M ≤ €43M

Small <50 ≤ €10M ≤ €10M

Micro <10 ≤ €2M ≤ €2M

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

Sources: Eurostat

España es tercera por

numero de Pymes en

Europa con casi 2.5

millones.

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

657 583

536 506

2007 2008 2009 2010 2011

SME Total

181 190 190 201 211

872 931 940 974 1,013

2007 2008 2009 2010 2011

France

SME Total

187 191 193 206 202

994 1063 1053 1084 1100

2007 2008 2009 2010 2011

SME Total

394 357 263 210 174

991 929

2007 2008 2009 2010 2011

SME Total

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

-45% -37% -32%

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

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

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

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

15.115.7

15.413.6

13.69.3

1717.5

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

TOTAL - EU27 1-9 employees 10-49 employees

50-249 employees

SMEs (combined)

Regulation %

Costs of production or labour %

Competition %

Availability of skilled staff or experienced managers %

Access to finance %

Finding customers %

Access to Finance Is the 2nd Pressing Issue for SMEs

What is currently the most pressing problem your firm is facing?

% of respondents

13 12 12

Findingcustomers

Access tofinance

Costs ofproduction or

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.

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

Como hacer frente a estos

problemas

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

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

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)

RiskCalc Plus – Workflow

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

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

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?

Case Study

¿Cómo podemos saber si los ratios son ‘buenos’ ?

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

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.

Análisis del Impacto del Ciclo Económico

Building the Stress Test Graph

Combines MKMV’s distance to default factor for the past 10 years with a company’s

current financial statements.

Respondemos a 12 preguntas de tipo cualitativo

Combined EDF Score (Quantitative + Qualitative)

Final combined borrower rating

Análisis cuantitativo

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

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)

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

• Comportamiento

intuitivo

i i i j 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

Selección de ratios que nos dan buena información

Density

Insolvent Solvent

Density

Insolvent Solvent

“bad” ratio “good” ratio

Estadísticas sobre los datos de Pymes en España

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%

FSO Mode (Financial Data)

The impact of industry effects

Finl Statement Only EDF

The Impact of Industry Market Information

Industry Median EDF

The impact of industry effectsThe Impact of Industry Market Information

Industry Market Factor

Finl Statement Only EDF

Industry Median EDF

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

Evolución de los Ratings implícitos de las Pymes en España 2004-2010

moodys.com

.................................................

Pablo Barbagallo

Product Specialist

EMEA Sales

+44 (0) 20 7772 1669 tel

+44 (0) 7730 910158 mobile

Pablo.Barbagallo@moodys.com

Moody's Analytics UK Ltd.

One Canada Square

Canary Wharf

London, UK E14 5FA

www.moodys.com

.................................................

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

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 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)

Portfolio and Alerts

Access to Moody’s Analysts

24/5 Customer Service

Events, conferences, webinars

Moody’s CreditView – Rated Issuers Corporates, Financial Institutions, Sovereigns, Structured Finance, Covered Bonds

Rating Agency Research

Financial and Credit Ratios

Market Signals

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

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

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

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.

Workflow – Review Covenant Research (2/2)

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

Market Signals Tab – View Market Implied Ratings

Link to Interactive Charts Page

Market Signals Tab – View Public EDF

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

Interactive Charts – Median Credit Spreads

• 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

Interactive Charts – Sectors: MIR Scatter Plot

• 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

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

Market Implied Ratings: Portfolio and Alerts

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

Key Indicators and Financials (1/2)

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

Key Indicators and Financials (2/2)

Create a Portfolio, Run Reports and Compare Peers

Workflow – Monthly Default Reports

• Monthly report containing default and

credit quality statistics, and market

commentary that enables you to monitor

the corporate bond market.

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

juan.licari@moodys.com

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

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

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

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

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)

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

(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)

0 .02 .04 .06 .08 .1baa_ba

2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1Month of Transition

Binary_Probit_Regression O_1_Median_Variable

2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1Month of Transition

Binary_Probit_Regression O_1_Median_Variable

Binary (Probit) Model Downgrade

nsitio

2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1Month of Transition

Actuals Baseline

FSA Scenario4

Custom

nsitio

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

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

Baseline vs. recession scenarios

Current PD

ture P

Baseline Scenario

Recession Scenario

(2) Stress Testing of PDs

Under the S4 scenario GDP growth is

negative…

… which translates into high probabilities

of default

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

1990 1995 2000 2005 2010

Construction Consumer Products Health Care

Services Trade

-10 -5

Sales Growth Rates

GDP Growth Rates

(3) Stress Testing of PD Drivers Balance-sheet driven PD models

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.

(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

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

2012m1 2014m1 2016m1 2018m1

DANONE

2012m1 2014m1 2016m1 2018m1

MARKS AND SPENCER GROUP PLC

2012m1 2014m1 2016m1 2018m1

BAYERISCHE MOTOREN WERKE AKTIENGESE

2012m1 2014m1 2016m1 2018m1

EXPERIAN PLC

BL S1 S2 S3 S4 Source: Moody’s Analytics

(3) Stress Testing of PD Drivers Final output: Firm-level stressed PDs

Riesgo País y de Mercado

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

100120

2009m1 2010m7 2012m1 2013m7 2015m1 2016m7Months

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f_swisscdsusdsr5ycorp_s2

2009m1 2010m7 2012m1 2013m7 2015m1 2016m7Months

f_zctocdseursr5ycorp_bl f_zctocdseursr5ycorp_s1

f_zctocdseursr5ycorp_s2

100150200250

2004m12006m12008m12010m12012m12014m12016m1Months

f_chinagovcdsusdsr5ycorp_blf_chinagovcdsusdsr5ycorp_s1

f_chinagovcdsusdsr5ycorp_s2

Country Risk Modelling Point-in-time and market driven analytics

Stress Testing of Swap Rate Curves

2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1

DNS PCA

EUR Swap Curve

Level Factor

2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1

DNS PCA

EUR Swap Curve

Slope Factor

Maturities: 1M-360M

Euro Swap Rates

EURO Swap Curve: Baseline

Stress Testing of Swap Rate Curves

Euro Zone Crisis

EUR Swap Curve vs Term Premium

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