Stress Testing x David Mermelstein de SAS

Stress testing financiero
David A. Mermelstein
SAS INSTITUTE ARGENTINA

david.mermelstein@sas.com

Agosto 2011

Copyright © 2010 SAS Institute Inc. All rights reserved.

Agenda
De los escenarios tipo “business as usual” a los
escenarios de estrés financiero
¿Qué es stress-testing?
Metodologías y buenas prácticas
Soluciones SAS para gestión de riesgos y stress-testing:
SAS Risk Management for Banking ®
» Características
» Cumplimiento regulatorio: La reciente com A 5203 del BCRA
SAS Risk Dimensions ®
» Características
» Ejemplos
Consideraciones finales
2

Copyright © 2010, SAS Institute Inc. All rights reserved.

Copyright © 2010, SAS Institute Inc. All rights reserved. 1

Agenda
» Características
» Características
» Ejemplos
3


Escenas de un lunes negro (08/08/2011)

4



Titulares financieros de estos días…

EU banks might have to raise €29 billion, Goldman
says

A survey by Goldman Sachs Group shows that the 91
banks in the EU that were subjected to stress tests this
year might have to raise €29 billion. Nine of the banks
could fail, Goldman analysts said in a report. "A month
ahead of the results release, there appears to be little
consensus about how much might have to be raised and
what the impact will be," the analysts wrote. Bloomberg (06
Jun.)

5


Titulares financieros de estos días…

European bank "fragilities" complicate Greek crisis,
Rehn says

Olli Rehn, the EU's economic and monetary affairs
commissioner, said efforts to resolve Greece's sovereign-
debt crisis are complicated by a regulatory failure to require
European banks to raise enough capital to weather a
default. A Greek default is not an option, Rehn said,
because of "fragilities" of the region's banking system.
However, others are voicing concerns that officials are
delaying the inevitable, which could result in additional
economic pain. Bloomberg (07 Jun.)

6



Colas pesadas, riesgos extremos, cisnes negros, y la
necesidad del análisis de escenarios

7


Cuando el escenario de “business as usual” no
alcanza

• Mercados con volatilidad histórica poco
representativa, sometidos a eventos discretos (jumps)
• Ausencia de información histórica
• Mercados con cambios estructurales importantes (ej.
devaluación)
• Mercados en tendencias no sostenibles (ej. burbujas)
• Instrumentos con respuestas no lineales a precios con
factores de riesgo no capturados por un modelo VaR
(ej. volatilidad implícita en opciones)

8

8


Agenda
» Características
» Características
» Ejemplos
9


Definiendo la práctica de stress-testing
Consiste en someter la valuación de los portafolios a
condiciones macro-financieras extremadamente
adversas, pero plausibles, y estimar el impacto en los
KRI de la entidad
Permite entender, cuantificar y manejar los diversos
riesgos a los que se tiene exposición.
Incluso cuando la mayoría de los escenarios de estrés
nunca se materializarán, se debería contar con planes
de contingencia para enfrentarlos.

10



Tres preguntas básicas en stress-testing

¿Cuánto se puede perder ante ciertos escenarios?

¿Bajo qué escenario se sufrirían pérdidas mayores
a cierto umbral?

¿Qué grado de vulnerabilidad se enfrenta frente a
dichos escenarios y, eventualmente cómo mitigar?

11

11

Características clave

Provee información detallada de la cola de las distribuciones de
P&L

Complementa el análisis
estadístico tradicional e
incorpora juicio experto

Funciona como sistema de
alertas tempranas

Permite acciones y mitigantes “forward looking” contra
eventuales faltantes de liquidez y capital

12

12


Agenda
» Características
» Características
» Ejemplos
13


Stress-testing en 4 grandes pasos
1. Determinar factores de riesgo y especificarlos en variables
de impacto.

2. Estimar los escenarios macro-financieros eventuales para el
horizonte temporal de análisis.

3. Para cada escenario, simular la valuación de los
instrumentos en cartera, incorporando efectos de riesgo de
mercado, crédito, ALM, e incluso operativos, contemplando
efectos de correlación.

4. Agregar los resultados y generar una función de distribución
de pérdidas.

14



Enfoques metodológicos

Dos clases principales:

Análisis de sensibilidad: ¿En qué medida cambia el
valor del portafolio ante cambios en un factor de
riesgo/parámetro?

Análisis de escenarios: ¿Cómo impacta en el valor
del portafolio la materialización de cierto escenario de
estrés?

Escenarios Top-down vs. Bottom-up

15

15

Alternativas metodológicas construyendo
escenarios
Hipotético: Juicio experto
Evento: Utilizar parámetros de un evento extremo real
ocurrido en el pasado (Event risk)
Híbridos: Movimientos extremos históricos, no vinculados
a un evento de crisis puntual
«Reverse stress testing»
Simulación: Utilizar modelos macroeconométricos y/o
métodos de Monte Carlo para realizar simulaciones
estocásticas de los drivers fundamentales. Complementar
con “modelos satélite”.
16



Ejemplos de eventos y sus disparadores
1973: First oil crisis – increase of oil prices by OPEC
1979: Second oil crisis – cut of Iranian oil supply
1987: Black Monday – stock market crash in the US
1991: Gulf war – oil price increase
1992: European Monetary System crisis – weak currency speculation
1995: Tequila crisis – Mexican current account deficit
1997: East Asian crisis – US dollar peg cutting
1998: LTCM – Russian Debt & Currency spur LTCM collapse
2001: September 11 – terrorist attacks in the US
2001: .com Tech Equities Bubble
2007–08: Sub-prime mortgages crisis – rise in home foreclosures
2010: Sovereign Debt Crisis

17

17

Enfoque de stress-testing propuesto en
Mermelstein (2009), y ADB (2010)
Los fundamentals macroeconómicos

Fuente: ADB (2010) 18



Los fundamentals macroeconómicos (cont.)



Variables «gatillo» en momentos de estrés




Factores de riesgo (drivers)



Factores de riesgo (drivers) (cont.)




El balance «estilizado» de la entidad



Estructura del modelo




Esquema general del modelo

25


Características necesarias para todo ejercicio de
stress-testing
Relevancia respecto a la situación/ operaciones de la
firma
Realismo respecto al escenario macro-financiero, con
enfoque «forward looking»
Consistencia interna entre los factores de riesgo
modelizados
Granularidad suficiente
Plasmar hechos estilizados de los mercados: colas
pesadas, estacionariedad/mean-reverting, jumps,
volatility clustering, evolución de la liquidez,
estacionalidades, correlaciones dinámicas, etc. 26

26


Aspectos organizacionales

Comité de stress-testing: Poner frente a frente a los
que toman y los que miden los riesgos para plasmar
sus puntos de vista en los escenarios
Definir reportes y sus contenidos
Fijar límites, responsables de accionar sobre dichos
límites
Planes de contingencia y cursos de acción frente a
escenarios

27

27

Una buena práctica de stress-testing debería permitir:
Flexibilidad para desarrollar análisis de escenarios y de
sensibilidad tanto en forma bottom-up como top-down
Posibilidad de implementar stress-tests integrados a distintos
niveles organizacionales (ej. unidades de negocio, firmwide,
etc.) y a través de los distintos tipos de riesgos (ej. mercado,
crédito, NII, liquidez, etc.)
Satisfacer requerimientos regulatorios/ratings
Aplicar las evaluaciones a medidas de performance usuales (ej.
RAROC)
Fijar límites de exposición en estrés e integrarlos con los otros
límites
Transparencia y trazabilidad de los ejercicios
Comunicación al board y al senior management diaria
28



Stress Testing: Enfoque holístico
Desde el enfoque de silos…
Stress Factor Stress Factor Stress Factor … Stress Factor

Retail/Mortgage Investment Banking Treasury &
C&I Credit Risk …
Credit Risk Market Risk Liquidity risk

Economic Capital Economic Capital Stressed VaR … Earnings at Risk

hacia un enfoque integral…
Stress and scenario tests

Retail/Mortgage Investment Banking Treasury &
C&I Credit Risk
Credit Risk Market Risk … Liquidity risk

Risk exposure
29


Integración micro-macro
Yesterday – Today - Tomorrow
Reporting/Management

Portfolio 1 Portfolio 2 Portfolio 3 Portfolio 4

Market Risk Rep-Risk
Liquidity

Credit Risk Oper. Risk xx-Risk

Scenario Generator

Micro economic & Macro-economic/external factors
Financial Market
endogenous factors
Labour Market
Productivity
Environment
…..

30



Agenda
» Características
» Características
» Ejemplos
31


32



SAS Banking Solutions Architecture

Banking Customer Credit Risk
Analytics Analytics Scoring Management
Architecture for Banking for Banking for Banking

Detail Data Store for Banking

33


SAS Risk Management for Banking
Riesgos Financieros
SAS® Risk Management for Banking: Riesgos Financieros
Reporting

Riesgo de Mercado Riesgo de Crédito Adm. Activos/Pasivos Fraudes / Lavado
Riesgos IR, EQ, FX Expuestos Potenciales GAP Analysis Análisis por Reglas

Stress Testing Stress Testing Stress Testing Patrones Desconocidos

VaR de Mercado VaR de Crédito Ingresos Netos por Interés Advanced Analytics

Riesgo de Liquidez Riesgo de Liquidez Redes Sociales

Data Management – Data Models & Flows – DDS & Data Marts

Sistemas Fuente de Información
Enterprise Data Warehouse Sistemas Transaccionales Otros Sistemas de Riesgo

34



SAS Market Risk for Banking
Market Credit Risk
Risk

Simulation approach
Model based ALM Firmwide
Risk
Empirical based
Analytical approach
Delta Normal approximation
Sensitivity approach
Delta, gamma, vega, theta …(“Greeks”)
Scenario approach
Stress test
Decision making – portfolio optimization

35


SAS Market Risk for Banking
Market Credit Risk
Risk
Value complex market instruments

Perform stress tests and calculate VaR, expected ALM Firmwide
shortfall and other risk measures using a variety of Risk

methods

Historical simulation, covariance simulation, analytical
models and advanced user-defined models.

Decompose portfolio risk in additive risk contributions,
and analyze the relative importance of risk factors in
determining portfolio loss.

Perform back tests and scenario tests of the model.

Analyze the effect of static and dynamic hedges and
trade strategies, and determine optimal portfolios
36



SAS Credit Risk for Banking
Market Credit
Risk Risk

Exposure calculation
Current exposure ALM Firmwide
Risk

Potential exposure
Scenario exposure
Actuarial model
Structural factors model
Dynamic transition matrix model
Portfolio Optimization

37


SAS Credit Risk for Banking
Market Credit
Risk Risk

Calculate and stress test credit exposures, taking into
account the effect of netting, collateral and margining, as
ALM Firmwide
well as credit derivatives book. Risk

Perform advanced simulation of potential future exposure. Calculate
portfolio credit risk measures using advanced portfolio credit risk
models, such as actuarial models, multivariate Merton models and
reduced form stochastic transition matrix models.

Optimize the credit portfolio with respect to assets held or collateral
needed or both.

Credit Portfolio Management
Credit Risk+
Credit Metrics
KMV

38



SAS Asset and Liability Management
for Banking
Model and value traditional balance-sheet instruments, such as loans
and deposits, and their associated (off balance) hedges
Include options such as prepayment and withdrawal
Maturity mismatch analysis (Current | Simulation | Scenario)
Repricing mismatch analysis (Current | Simulation | Scenario) Market Credit Risk
Risk
Duration analysis
Analyze optimal cash flow replication hedges
ALM Firmwide
Fund Transfer Pricing (risk spreads – ej. Liquidity, credit) Risk

Stress testing & modeling of liquidity risk, NII & economic value
Re-investment method of matured asset cash flows and recoveries
Model customer choice of asset reset time
Model customer choice of funding volumes (non-maturing liabilities)

39


Snapshot of Traditional Vs. Advanced
Net Cumulative Gap Profile
Traditional Liquidity Risk Management: 100
80 Time to
a. Runoff Liquidity Gaps 60 insolvency
40
b. Liquidity Ratios 20
0
-20 1 D 8D 14 D 1M 3M 1Y 3Y 6Y 10 Y
Now, focus on going concern behavioral modeling
-40
under stress scenarios!
-60

a. Modeling Net Funding Requirements of Encumbered Assets and Liabilities
Reduced Cash Increased Cash Net Funding
inflows Less outflows Requirements
Business operations + Business operations +
Maturing assets + Maturing liabilities +
Early asset puts + Early liability calls +
Assets pledged + Off balance sheet
Credit lines (standby) + commitments +
Derivative positions Derivative positions

b. Counterbalancing Capacity comprising of Unencumbered Assets
c. Periodic Simulation of Contingency Funding Plan 40



Gaps de liquidez probabilísticos

Modeled
Dynamic Evolution Component

1

2

3

4

41


ALM dinámico

Dynamic ALM is a forward-looking risk analysis that:
projects balance sheet components and the resulting P&L
under different market scenarios
taking into account realistic evolution of the balance sheet
over a multi-period horizon (ranging from months to years)

42



ALM dinámico en Banca

Main aim is simulation of Net Interest Income
Scenarios can be stochastic, but often deterministic
Monthly time steps over a 1 to 3 year horizon
Evolution of the balance sheet is driven by user-defined
parameters
Newly simulated production can have different
amortization schemes and maturity profiles
Future margins are typically user-defined and can
depend on product and scenario

43


SAS Firmwide Risk for Banking
Market Credit Risk
Risk

Correlated risk aggregation approach
Correlated approach (Multi-normality assumption) ALM Firm wide
Risk
Copula approach (Normal, t, mixture, user-defined)
Full risk simulation based approach
Capital allocation
Calculate risk-based performance based on the
effect from balance sheet items as well as off-
balance-sheet items. (i.e. Risk adjusted
profitability (RAROC))
Sample economic capital calculations provided

44



SAS® Risk Management for Banking
– the solution
Integrated risk solution for banks = Enterprise Risk Management
(ERM) - interlinking, modelling, simulation, transparency
Remove current variety of point risk solutions
Reduce Spreadsheet-Risk through improved integration
Enables standardization across risk infrastructure
Adapts to individual customer requirements by application of the
SAS technology
Provides capabilities to support changing requirements to meet
future needs
SAS investing in new developments in technology and solutions for
Risk

45


Reporting

46



Standard Market Risk Analysis

47


Standard Market Risk Analysis Reporting

48



Standard Market Risk Cubes

49


50



Duration Report

51


Net Interest Income

52



Balance Sheet Forecast

53


Re-pricing Gap

54



Earnings at Risk

55


Economic Value

56



FTP

57


Portfolio Optimization

58



SAS Information Delivery Portal

59



60



Agenda
» Características
» Características
» Ejemplos
61


Algunos tópicos de la comunicación A5203
Modelización y gestión integral de riesgos financieros
Análisis de escenarios
Pruebas de estrés
Responsabilidades, roles y políticas
Sistemas:
Medir exposiciones vigentes y las que puedan surgir
Evaluar riesgos asociados a activos, pasivos, y posiciones fuera del balance
Utilizar conceptos financieros y técnicas de medición generalmente aceptados
Incorporar todas las posiciones relevantes en tiempo y forma
Considerar todas las fuentes significativas de riesgo
Considerar el uso de escenarios múltiples
Considerar exposiciones en diferentes monedas

Reporting – Dashboards - Alertas tempranas

Las soluciones SAS proveen de todas las características regulatorias exigidas, además de
funcionalidades adicionales que están a la vanguardia en lo que hace a riesgos financieros
62



Market Risk Management
Regulaciones locales (Com “A” 5203)

63


Example: local regulation requirements

Interest Rate Risk Capital Calculation (Com ‘A’ 3959)

Market Risk Capital Calculation (Com ‘A’ 3959)

64




Market Risk: embedded option measures

65



Market Risk: embedded options valuation model

66



SAS RMfB ofrece lo exigido por la regulación y mucho más

The capabilities that are mentioned in the rules are
Value at Risk: specific calculation methodology is undefined but
we assumed is historical simulation VaR. The 10 day and “at
least 99% confidence” requirement is standard. SAS Market
Risk supports historical simulation, covariance simulation,
analytical models and advanced user-defined models. Job
Name: MARKET_ANALYSIS
Sensitivities: the ruling mentions Gamma and Vega as the two
sensitivity measures to be calculated for bonds, equity and
foreign exchange portfolios. SAS Market Risk calculates Delta,
Gamma and Theta out of the box. Job name:
SENS_GREEKS — Greeks sensitivity analysis.
Embedded option valuation: the rules only refer to the Black
Scholes model. SAS Market Risk supports European embedded
option interest rate models that include Ho-Lee, Hull-White,
Black-Karasinski, Vasicek and Black. Only Hull-White pricing
is supported for American options.
67


Liquidity Risk Management
Local definitions (Com “A” 5203)

68



Liquidity Risk Management
Local definitions (Com “A” 5203)

69


Liquidity Risk: concepts

70




71



72




73



74




75



76



ALM Requirements vs. SAS Tool / Engine
Group Capabilities

Liquidity Static and Dynamic Gaps
Liquidity & Repricing
Earnings NII, EaR

Economic Value NPV & VaR

Stress Testing Single and multifactor stress testing

Reports Out of the box and customized

Limit Management Optimization & Reporting

77

77

Agenda
» Características
» Características
» Ejemplos
78




MDDBs Output datasets Reportes

79


SAS® Risk Dimensions Overview
Risk Dimensions provides a complete
environment for calculating a wide range of risk
measures, e.g.
» Mark-to-Market
» Cashflow analysis
» Value at Risk
» Stress-testing
» Expected Shortfall
» Exposure metrics
» Risk Adjusted Return on Capital
Risk Dimensions includes the framework for
managing data, analysing the data and reporting
the results.
80



SAS Risk Analytics – Risk Dimensions
An open ended risk engine
Access to SAS core functionality
» Advanced models and simulation
» Advanced optimization
» ...
» Interactive graphics & reporting
Contains method development
framework & process logic
» RF transformations
» Counterparty
» Instrument
» Mitigant
» Post-process
That is driven by:
Interactive GUI, Web or
SAS language, SAS tools and/or 3rd
party tools, C, C++

81


Pre-configured Analytics
Analytical methods
Simulation methods
» Covariance matrix
» Historical
» Monte-Carlo
» EVT models
» Mixed Sim. Method
» Copula
ALM type analysis
Cash flow analysis
Advanced portfolio optimization
Credit exposure & netting
Performance attribution and Risk adjusted Performance
Backtesting
Trading rules (cash account) within simulation horizons 82



Flexible Model / MC Engine
Modelling subsystem based on SAS/ETS
module
Can be driven by GUI, code or a
combination thereof
Analyzes general systems of nonlinear
models
Advanced solve and estimation
methods
Advanced MC and forecast capabilities
Standard time series models
GBM, ARCH, GARCH, Vasicek,
Mean Reversion, Jump Diffusion
In-house developed models
Correlation between risk factors using
copula functionality
Covariance matrix estimation

83


Example: GUI features

Interactive risk analysis
Interactive graphics &
hierarchy drill-down
» RF contributions
» Exposure profiles
» ..
Model analyzer
Curve analyzer
SAS/Insight 3D analysis
…..

84

84



Pricing and method structure
Advanced subsystem
for pricing integration
and building
customized analysis
SAS code
SAS built in functs
C, C++
Method structure
RF transformation
Counterparty rating
Instrument pricing
Mitigation
Post-processing

85


Risk Dimensions process overview
Scenario methods Method structure Analysis results
Supporting
analytics
Model based SAS functions, procedures,
libraries, C interface,…
Covariance matrix

Fitted models
SAS data set

Risk factor MDDB
transformation Instrument ......... OLAP
Non-model input
based Counterparty User defined reports
method
Historical

User scenario/stress Pre-defined Registered
test processes reports
RF curve/surface Delta-normal, VaR, ES,
Cash flow analysis, ALM,
portfolio optimization, ….

86



Pre-Configured Risk Applications
Robust Modeling Methodology Library
Stress Tests can utilize any number of methodologies
Simulation & Time Series Methods
Covariance matrices
Historical simulation
Monte Carlo engine
Arma: Arch/Garch
Proprietary user defined models

Interest Rate
Vasicek Model

Option Pricing
Black-Scholes
CRR Binomial Trees

Exposure
Stochastic collateral modeling
Counterparty Netting
Credit Migration & Default Modeling
Merton Models
Actuarial Models
Structural Factors
Markov Processes
Stochastic Mitigation Modeling

Risk Aggregation Methodology
Copula based risk aggregation
87


Algunos tipos de instrumentos financieros
soportados
Warrant Double barrier options
Swaption Extendible options
Asian options Extreme Spread options
Barrier options Forward start options
Binary barrier options Hindsight options
Binary options Lookback options
Chooser options Power options
Compound options Reset options
Double binary barrier options Soft barrier options
Supershare options

88



SAS Risk Dimensions GUI

89


SAS Risk Dimensions – Ciclo de modelización

90



SAS Risk Dimensions – Análisis, post-procesamiento & Reporting

91


Agenda
» Características
» Características
» Ejemplos
92



Agenda
» Características
» Características
» Ejemplos
93


• Cumplir con los requisitos regulatorios actuales y anticiparse a los futuros
• Generar análisis y reportes para una gestión mas proactiva y asertiva de escenarios de
Objetivos stress

• Pasar de la metodología manual y ad-hoc a una gestión proactiva de escenarios de stress
• Incorporar factores de stress de riesgo (macroeconómicos, propios de los portafolios, etc.)
que se reflejen en impactos en los estados financieros e índices de capitalización
• Romper con los análisis fragmentados por tipo de riesgo (crédito, mercado, liquidez y
Desafíos operacional)
• Proceso manual de extracción, transformación y carga de datos

• Habilidad para integrar los distintos tipos de riesgos en una sola aplicación (riesgo de
crédito, mercado, liquidez y operacional)
• Habilidad para generar simulaciones basadas en escenarios regulatorios y propios
Requerimientos • Automatizar el proceso de captura de datos para generación de modelos y reportes

• Ampliar el uso de escenarios de stress para la gestión interna y no solo para cumplimiento
regulatorio
• Contar con una calculadora avanzada que permita ingresar valores de factores de
riesgo, actualizarlos y generar automáticamente información financiera estresada
Beneficios • Potenciar el uso de herramientas de BI avanzadas así como el uso de Microsoft Office
para reportes internos y externos

94



Stress Testing x David Mermelstein de SAS

Stress Testing x David Mermelstein de SAS

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