Modelos Avan%E7ados Para Risco Operacional - Christian Mittelberg

7/28/2019 Modelos Avan%E7ados Para Risco Operacional - Christian Mittelberg

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RiskDeutsche Bank

Advanced Models for OR

Christian Mittelberg

Head Operational Risk Management, Americas23.10.2012

,


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Agenda

1. Risk Management at Deutsche Bank

. vance easurement pproac

3. 2nd Generation Capital Modeling

Christian Mittelberg23.10.2012Risk

Deutsche Bank


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Deutsche Bank in NumbersPresent in over 72 countries with 3,064 branches worldwide


Deutsche Bank

Deutsche Bank Group June 30, 2012*

Headcount 100,654

Assets 2,241bn

RWA 373bn

Net Revenues (1H2012) 17.2bn

Net Income (1H2012) 2.1bn

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*Deutsche Bank Interim Report as of June 30, 2012


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Deutsche Bank in BrazilA leading local banking franchise with over 100 years of history

DB Brazil

CorporateFinance

Global MarketsEquities

TransactionBanking

Brokerage services offered tolocal qualified investors and

foreign investors

Derivatives and Structuredtrades related to Equityproducts / underlying

Research coverage

Foreign Trade Services,including Import and Export

related financing andservices

Domestic Custody and Trustservices

Cash Management offered to

Capital Markets Coverage

Debt Capital Markets

including issuance of debtand offering of shares)

Mergers & Acquisitions

Advisory services (i.e. expertopinions , company

FX, Fixed Income andCommodities

Derivatives includingFutures, Options Swaps etc.

Structured Products,including Credit Derivatives,Notes linked to assets)

Global MarketsDebt


Deutsche Bank 3

Pillars

Global coverage of clients and globally integrated business and product suites

Comprehensive business and product platform with outstanding level of capacity and expertise

Mature operation in Brazil, focus on expansion, growth and leverage of synergies of the Corporate Investment Bank (CIB)

Institutionsevaluation etc.)


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Risk- PeopleA global Organization

1,502

North

America9.3%

271

30

182UK

12.7%

369

Cont.Europe18.6%

543

GER

51.5%

Japan1.0%

Asia6.2%


Deutsche Bank

4

SouthAmerica

0.1%

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Pacific0.5%

2,9142,914 eemployeesmployees (FTE)(FTE) worldwideworldwideFTE, June 2012

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RiskManagement Team

Chief Risk Officer

Stuart Lewis

Frank Kuhnke

Global HeadMarket Risk

Management

David Stevens Peter Yearley Stephan Wilken

Chief Credit OfficerPBC

Chief Credit OfficerAWM

Deputy CRO,Enterprise RiskManagement,Risk Strategy

Christian Sewing Konrad Joy

RiskChief Operating

Officer

Chief Credit OfficerCB&S, GTB

Divisional Chief Credit Officers


Deutsche Bank

Vincent Choo

Regional CROAPAC

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Global HeadOperational Risk

Management(Interim)

Michael Berkowitz Richard Ferguson

Regional CROAmericas

Regional Chief Risk Officers


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Agenda





Deutsche Bank


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AMA model development at Deutsche Bank

2000 Economic capital with LDA (top-down model)

1999 Systematic collection of loss data

2007 Regulatory approval

2006 Implementation production engine / AMA application submitted

2005 Official EC calculation with AMA model

2003 Implementation of prototype

2001 Kick-off AMA project


Deutsche Bank

Ongoing model improvement & extensions

Since 2008 Annual Audits from BaFin

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AMA Core Model Allocation(via model)

Group Level

Capital*(before EL & QA)

A re ate Loss Distribution Correlation /

Group-Level

Deutsche Banks AMA model overviewLoss distribution approach (LDA) based on historical loss data

Business Divisions

Gross Cell Losses

Net Cell Losses

Severity

Diversification

X

Business Divisions

EventTypes

BL&ET-Level

KRI

RCSAEL

-

Frequency


QA

Capital* after EL & QA

Divisional Level

-

InsuranceModule


Deutsche Bank

Insurance ParametersInsurance Parameters


Allocation

Relevant Loss Data

Internal

Loss Data

dbIRS

External

Loss Data

ORXScenarios

InternalScenarios

Public

Loss Data

OpVar

* Capital = Economic Capital (EC) and Regulatory Capital (RC)

Allocation

Product /Country Level

Country

Product

Input Data

InsuranceContracts

Insurance

Data Adjustments

& Allocation

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AMA Core ModelCapital*(before EL & QA)

Aggregate Loss Distribution Correlation /

Group-Level

Insurance modeling

Gross Cell Losses

Net Cell Losses

Severity

vers ca on

X

Business Divisions

EventTypes

BL&ET-Level

Frequency

InsuranceModule


Deutsche Bank

Insurance ParametersInsurance ParametersRelevant Loss Data

Internal

Loss Data

dbIRS

ExternalLoss Data

ORXScenarios

Internal

ScenariosPublic

Loss Data

OpVar

Input Data

InsuranceContracts

InsuranceData

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Reflection of InsuranceDBs state-of-the-art insurance model approved for RC calculation

Net Losses

Net losses

Insurance modelling

Mapping of event types to insurance policies

Gross LossesMapping Policies

Insurance

Simulatedgross losses


Deutsche Bank

Limits & deductible

Rating of insurer & haircuts

Insurance payment is calculated for each of

the simulated gross losses separately

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Insurance mappingConnecting the insurance and OR worlds

Fidelity

OR event types Insurance policies

Infrastructure

Exec., Delivery & Proc. Mgmt

Clients, Products & Bus. Pract.

Property Damage

General Liability

Professional Indemnity

Serv. & Elec. Break-D.

Burglary, Theft, Rob.

80%

10%10%


Deutsche Bank 11

Employers Pract. Liab.

Not insured

Employ. Pract. & Workpl. Safety

random mapping


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Forward-looking capital adjustments

AMA Core Model

Allocation(via model)

Group Level


Aggregate Loss Distribution Correlation /

Group-Level

Business Divisions

Gross Cell Losses

Net Cell Losses

Severity

X

Business Divisions

EventTypes

BL&ET-Level

KRI

RCSAEL

Capital Sub-

Frequency


QA


Divisional Level

QA Sub-

Insurance

Module


Deutsche Bank 12

Insurance ParametersInsurance Parameters


Allocation

Relevant Loss Data

Internal

Loss Data

dbIRS

ExternalLoss Data

ORXScenarios

InternalScenarios

PublicLoss Data

OpVar

* Capital = Economic Capital (EC) and Regulatory Capital (RC)

Allocation

Product /Country Level

Country

Product

Input Data

InsuranceContracts

InsuranceData Adjustments

& Allocation


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Qualitative adjustment in DBs OR model

Qualitative adjustment (QA) applied to contributory capital of business lines

QA is separated from the quantitative capital calculation

Transparent way of how risk management can influence capital

Main facts on QA

Risk indicators and self assessment are main components

QA score (applied on BL/ET level) plus penalty component (inappropriate loss datacollection, KRI/SAT minimum standards, etc.)

Measurement of risk sensitivity and coverage determines effective range

Key risk indicators


Deutsche Bank

, , , , - , ,

Business specific KRIs, e.g. nostro reconciliations, outstanding confirmations,average processing time of customer complaints

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Qualitative adjustment in a nutshell

Imperfection of risk

measurement instruments(KRI, SAT)

From QA scorecard

(score as output frominstruments)

Inappropriate data

collection, KRI/SATminimum standards,

+60%

e.g.

%Coverage

Risk Sensitivity

Effective Ran e

Score

%

QA Percentage(13%)

maps out Penalty

Component

Final QA( 13%)

36%

+36%

1

2

3

4

5


Deutsche Bank

Qualitative adjustment (QA) calculated ondivisional level and aggregated to Group level(Group shown above)

Final QA percentage applied to capital afterdeduction of expected loss

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Maximum Range

of Adjustment

60%

. .


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Embedded Continous Validation & Improvement

Framework & Model Risk Management

Risk Profile ReviewsAMA Production

Weekly Themes

Deep Dive Identification

Deep Dive Schedule

Top Risk Analysis

Idea Log

Project Identification

Program Governance

Validation


Deutsche Bank 15

DB ORM Plan


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Agenda





Deutsche Bank


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Exposure-based modeling beyond LDAResearch on structural models for OR modeling

Drawbacks of current LDA model based on historiclosses and stochastic severities

No immediate capital impact of management action likede-risking

Current

Impact of new losses (especially external) difficult to

rationalize, e.g. OR exposure does not change overnight

Exposure-based thinking more natural than thinking interms of loss distributions (based on historical data)

Use of exposure-based models to substitute /supplement LDA approach

Number and size of losses closely related to currentportfolio

DBs

CurrentPortfolio

=Exposure

LossPotential

(size &freq.)

OR

Capital


Deutsche Bank

Support of OR management

Discussion centered around actual exposure (portfolioand market environment)

Eases discussions with business

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MaximumLoss


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Exposure-based modeling of litigation-related ORChallenges

Proof of assumptions

Litigation trigger event can be modeled as credit event (default or rating downgrade)

Exposure can be derived from face volume of own share of issues

Data & calibration

Internal issuance and litigation history

Current portfolio of issuances with corresponding credit ratings / PDs

Calibration of required model parameters (credit risk parameters, exposures, stochastic LGDs)

No need to develop new modeling techniques since standard methodology used for credit risk ECcan be utilized (factor model, rating migration, )

Integration in standard LDA model used for other OR categories


Deutsche Bank

Combination with remaining cells of standard LDA model

Correlation/diversification between exposure-based model and LDA model

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Exposure-based OR modeling of litigation related ORLeveraging credit risk approach


Deutsche Bank 19


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Integration of EBOR model into LDA core modelDependence structure modeled via common copula for frequencies

AMA Core Model Capital*(before EL & QA)

Group-Level

Exposure-based model similar

to cell of BL/ET matrix

Gross Cell Losses

Net Cell Losses

ggrega e oss s r u on

Business Divisions

EventTypes

BL&ET-Level

SeverityFrequency

Frequency

Issuance 1K

Litigation 1L

Current Portfolio

MonteCarlo

Exposure-Based Model


Deutsche Bank 20

Aggregate Loss LGD class 2LGD class 1 LGD class

=

=

N

i

L

1

iiagg )LossRatio()Exposure(

Link viacopula

LossMitigants


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Q & A Session


Deutsche Bank

AMAAMAAMAAMA

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Modelos Avan%E7ados Para Risco Operacional - Christian Mittelberg

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