Stress Testing x David Mermelstein de SAS

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

Copyright © 2010 SAS Institute Inc. All rights reserved.

Stress testing financieroDavid A. MermelsteinSAS INSTITUTE ARGENTINA

[email protected]

Agosto 2011

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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


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Agenda






» Características



» Características

» Ejemplos


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


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� 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.)

Titulares financieros de estos días…

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� 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.)

Titulares financieros de estos días…


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

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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)

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Agenda






» Características



» Características

» Ejemplos


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� 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.

Definiendo la práctica de stress-testing


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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?

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Características clave

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

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� 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


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Agenda






» Características



» Características

» Ejemplos


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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.

Stress-testing en 4 grandes pasos


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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

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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”.


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� 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

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Ejemplos de eventos y sus disparadores

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

Fuente: ADB (2010)


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

Fuente: ADB (2010)

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Enfoque de stress-testing propuesto en Mermelstein (2009), y ADB (2010)Variables «gatillo» en momentos de estrés

Fuente: ADB (2010)


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Enfoque de stress-testing propuesto en Mermelstein (2009), y ADB (2010)Factores de riesgo (drivers)

Fuente: ADB (2010)

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Enfoque de stress-testing propuesto en Mermelstein (2009), y ADB (2010)Factores de riesgo (drivers) (cont.)

Fuente: ADB (2010)


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Enfoque de stress-testing propuesto en Mermelstein (2009), y ADB (2010)El balance «estilizado» de la entidad

Fuente: ADB (2010)

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Enfoque de stress-testing propuesto en Mermelstein (2009), y ADB (2010)Estructura del modelo

Fuente: ADB (2010)


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

Esquema general del modelo

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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


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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

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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


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Stress Testing: Enfoque holístico

Desde el enfoque de silos…

hacia un enfoque integral…

C&I Credit Risk

Stress Factor Stress Factor Stress FactorStress Factor

Economic Capital Economic Capital Earnings at Risk

Investment Banking Market Risk

Treasury & Liquidity risk

Retail/Mortgage Credit Risk

Stressed VaR

…

…

…

Stress and scenario tests

Risk exposure

C&I Credit RiskInvestment Banking

Market RiskTreasury &

Liquidity risk

Retail/Mortgage Credit Risk …

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Oper. RiskCredit Risk xx-Risk

Rep-RiskLiquidity

Market Risk

Integración micro-macro

Macro-economic/external factorsFinancial Market

Labour Market

Productivity

Environment

…..

Reporting/Management

Micro economic &

endogenous factors

Yesterday – Today - Tomorrow

Scenario Generator

Portfolio 3Portfolio 1 Portfolio 2 Portfolio 4


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Agenda






» Características



» Características

» Ejemplos


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SAS Banking Solutions Architecture

Detail Data Store for Banking

Banking Analytics

Architecture

Customer Analytics

for Banking

Risk Management for Banking

CreditScoring

for Banking

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SAS Risk Management for BankingRiesgos Financieros

Reporting

SAS® Risk Management for Banking: Riesgos Financieros

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

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

Otros Sistemas de RiesgoEnterprise Data Warehouse Sistemas Transaccionales

Sistemas Fuente de Información


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SAS Market Risk for Banking

� Simulation approach

� Model based

� Empirical based

� Analytical approach

� Delta Normal approximation

� Sensitivity approach

� Delta, gamma, vega, theta …(“Greeks”)

� Scenario approach

� Stress test

� Decision making – portfolio optimization

Market Risk

Credit Risk

ALM Firmwide Risk

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SAS Market Risk for BankingMarket Risk

Credit Risk

ALM Firmwide Risk

� Value complex market instruments

� Perform stress tests and calculate VaR, expected

shortfall and other risk measures using a variety of

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


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SAS Credit Risk for Banking

� Exposure calculation

� Current exposure

� Potential exposure

� Scenario exposure

� Actuarial model

� Structural factors model

� Dynamic transition matrix model

� Portfolio Optimization

Market Risk

Credit Risk

ALM Firmwide Risk

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SAS Credit Risk for BankingMarket Risk

Credit Risk

ALM Firmwide Risk

� Calculate and stress test credit exposures, taking into

account the effect of netting, collateral and margining, as

well as credit derivatives book.

� 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


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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)

� Duration analysis

� Analyze optimal cash flow replication hedges

� Fund Transfer Pricing (risk spreads – ej. Liquidity, credit)

� 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)

Market Risk

Credit Risk

ALM Firmwide Risk

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Snapshot of Traditional Vs. Advanced

Traditional Liquidity Risk Management:

a. Runoff Liquidity Gaps

b. Liquidity Ratios

Now, focus on going concern behavioral modeling

under stress scenarios!

a. Modeling Net Funding Requirements of Encumbered Assets and Liabilities

b. Counterbalancing Capacity comprising of Unencumbered Assets

c. Periodic Simulation of Contingency Funding Plan

-60

-40

-20

0

20

40

60

80

100

1 D 8 D 14 D 1 M 3 M 1 Y 3 Y 6 Y 10 Y

Net Cumulative Gap Profile

Time to

insolvency

Reduced Cash

inflowsIncreased Cash

outflowsLessNet Funding

Requirements

Business operations +

Maturing assets +

Early asset puts +

Assets pledged + Credit lines (standby) +

Derivative positions

Business operations +

Maturing liabilities +

Early liability calls +

Off balance sheet commitments +

Derivative positions


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Gaps de liquidez probabilísticos

1

2

3

4

Modeled

ComponentDynamic Evolution

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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)


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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

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SAS Firmwide Risk for Banking

� Correlated risk aggregation approach

� Correlated approach (Multi-normality assumption)

� 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

Market Risk

Credit Risk

ALM Firm wide Risk


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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

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Reporting


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SAS Risk Management for BankingStandard Market Risk Analysis

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SAS Risk Management for BankingStandard Market Risk Analysis Reporting


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SAS Risk Management for BankingStandard Market Risk Cubes

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Duration Report

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Net Interest Income


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Balance Sheet Forecast

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Re-pricing Gap


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Earnings at Risk

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Economic Value


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FTP

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Portfolio Optimization


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SAS Information Delivery Portal

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SAS Risk Management for Banking


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Agenda






» Características



» Características

» Ejemplos


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Algunos tópicos de la comunicación A5203Modelización 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


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Market Risk Management

� Regulaciones locales (Com “A” 5203)

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Example: local regulation requirements

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

� Market Risk Capital Calculation (Com ‘A’ 3959)


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� Market Risk: embedded option measures

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� Market Risk: embedded options valuation model


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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.

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Liquidity Risk Management

� Local definitions (Com “A” 5203)


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Liquidity Risk Management

� Local definitions (Com “A” 5203)

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� Liquidity Risk: concepts


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ALM Requirements vs. SAS Tool / Engine

Group Capabilities

Liquidity Static and Dynamic GapsLiquidity & 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

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Agenda






» Características



» Características

» Ejemplos



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SAS Risk Dimensions ®

MDDBs Output datasets Reportes

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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.


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� 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++

SAS Risk Analytics – Risk Dimensions

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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


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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

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Example: GUI features

� Interactive risk analysis

� Interactive graphics & hierarchy drill-down

» RF contributions

» Exposure profiles

» ..

� Model analyzer

� Curve analyzer

� SAS/Insight 3D analysis

� …..


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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

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Risk factor transformation Instrument

inputCounterparty

method

.........

Supporting analytics

� SAS functions, procedures,

libraries, C interface,…

Pre-defined processes

� Delta-normal, VaR, ES,

Cash flow analysis, ALM,

portfolio optimization, ….

Scenario methods

Non-model based

� Historical

�User scenario/stress

test

� RF curve/surface

Model based

� Covariance matrix

�Fitted models

Method structure Analysis results

�SAS data set

�MDDB

� OLAP

� User defined reports

Risk Dimensions process overview

Registered reports


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� 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

Pre-Configured Risk ApplicationsRobust Modeling Methodology LibraryStress Tests can utilize any number of methodologies

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Algunos tipos de instrumentos financierossoportados

� Warrant

� Swaption

� Asian options

� Barrier options

� Binary barrier options

� Binary options

� Chooser options

� Compound options

� Double binary barrier options

� Double barrier options

� Extendible options

� Extreme Spread options

� Forward start options

� Hindsight options

� Lookback options

� Power options

� Reset options

� Soft barrier options

� Supershare options


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SAS Risk Dimensions GUI

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SAS Risk Dimensions – Ciclo de modelización


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SAS Risk Dimensions – Análisis, post-procesamiento & Reporting

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Agenda






» Características



» Características

» Ejemplos



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Agenda






» Características



» Características

» Ejemplos


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Consideraciones finales

Objetivos

• 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 stress

Desafíos

• 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 operacional)

• Proceso manual de extracción, transformación y carga de datos

Requerimientos

• 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

• Automatizar el proceso de captura de datos para generación de modelos y reportes

Beneficios

• 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

• Potenciar el uso de herramientas de BI avanzadas así como el uso de Microsoft Office para reportes internos y externos


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Preguntas…

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¡ Muchas gracias !

[email protected]

Referencias:

Mermelstein, David A. (2009) «Hacia un indicador de vulnerabilidad bancaria basado en en pruebas de estrés (stress-testing)», Master in Finance’s final essay, mimeo.

Kiguel M., Loser C. y D. A. Mermelstein (2010) «A Macroprudential Framework for the Early Detection of Banking Problems in Emerging Economies»,Working paper Nro. 44, Working Paper Series on Regional Economic Integration, Asian Development Bank, March.