Prasad Kodali

7/28/2019 Prasad Kodali

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O erational Risk Mana ement and

MeasurementMa 2010


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Agenda

Measurement

Philosophy

Model inputs

Model Methodology

Assessment

Evolution of Scenario Analysis

Granularity of ScenarioAnalysis

Client and Regulatory focus


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Operational Risk Roles

Firm Operational Risk Department

Sets Operational Risk policies and procedures

Creates standards for assessments

Calculate Capital

Report to Board and management committees

Manage regulatory communication

Business Line Risk Management

Manage day to day risk

Execute assessment programs (RCSA, SA etc. )

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Measurement Philosophy

Model on basis of relevant, quantitative data where available and reliable

Scenario Analysis (SA) data is used:

as a proxy for external data

to introduce forward-looking elements into the model

to assist in the management of Operational Risk

Conservative judgments are made according to the quality of the data (e.g.

maximum correlation calculated is used)

No overrides are made to model inputs

Transparent and Easy to explain

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Measurement Inputs

Internal Loss Data Scenario Anal sis

A direct input into the model

Direct losses > $20k are used to model

A direct input into the model

Used to model the severity distribution.

distributions.

profile to be reflected in the AMA model.

Addresses data paucity in the tail ofsome risk t e severit distributions.

x erna oss a a

An indirect input into the model

A key input to the Scenario Analysis

RCSAs

An indirect input into the model

process, it provides participants with

potential OR exposures relevant to the

Firm.

Used to identify and assess inherent risk,

residual risk, control performance and

appropriateness of mitigating actions. Also used to determine correlations

between risk types and benchmarking.

RCSAs trigger and inform the Scenario

Analysis process.

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Measurement Methodology

Model Inputs Model Processes Model Outputs

Frequency o fInternal Loss Data

Frequency

Monte Carlo

Internal Loss Data (>20K)

Weight x1Aggregate Loss

Severity-ID Simulation

Scenario Analysis (>$10MM) Weight (1-x)1

AMA Capital

Economic Capital

(ALD)

RCSA Internal

Data

External

Data

BusinessJudgment

Severity

Severity-SA2

Above process is performed for each unit of measure

Capital is aggregated using a Gaussian copula

BEICFs Audit/SOX

Management Focus Items

used based on empirical research

Multiple model runs are executed in a structured process to reducesimulation variance

1The weights of ILD and SA data are derived using a scorecard approachfor each unit of measure

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2Empirical Simulation found to be conservative and stable relative to fittedseverity distributions for SA data.


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Assessment Evolution of Scenario Analysis

Various methodologies for collecting estimates were considered :

Granularity: Individual scenario, risk event type

Structure of estimates: synthetic point, percentiles, frequencies by severity buckets

and probabilities by severity buckets

Criteria used to evaluate these methods:

Effectiveness in capturing changes in risks by engaging Biz units

Ability to aggregate estimates

Acknowledge and leverage the consensus based decision making culture of Morgan

taney

Collection of frequency estimates by standardized severity bucket at the Risk

2006 Q4

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Assessment Process

RCSAs are refreshed every quarter for all business units

ORD and BU work to ether to validated existin risks and identif an new risks

Triggers for Scenario Analysis re-assessment:

Change in RCSA risks or scores External Events

Change in Business environment

SA scores are stale, meaning they have not been re-assessed for more than 6 quarters

3. The last step is toestimate the frequencyOperational Risk Event Annual Frequency by Severity Bucket

within each severity

bucket

2. Rank Scenarios with

1 2 3 4 5 6 Total

$10M - $20M $20M - $50M $50M - $100M $100M - $250M $250-$1B $1B+ Freq.

# Events 1 1 1 0 0 0

# Years 5 15 20 0 0 0 $ 100,000,000

Frequency 0.20 0.07 0.05 0.00 0.00 0.00 0.32

Upper Bound

respect to potentialseverity of loss.

1. The first step is to

# Rank

1 3

Scenario

Theft of intellectual/physical property

. . . . . . .

could l ead to a largeloss.

2 2

3 1

4

Theft of confidential information (both MS and client)

Fraudulent trading (Misrepresentation by counterparty, Forgery))

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Assessment Scenario Analysis Granularity

MorganStanley

HRBCM/CS

Banking

Trading

Management

Management

Equities Fixed Income GCMM&A Core MB/PE

Note:

Orange boxes represent low est level of modeling unit as w ell as lowest level at which sc enario analysis was held

CommoditiesEx-Commodities

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wor s ops are con uc e on e rm w e eve ,

BCM/CS: Business Continui ty Management and Corporate Servic es

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