Creating competitive advantage with risk management

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James Church, VP of Product and R&D, FINCAD

James Church has more than 15 years of software industry experience and is responsible for the strategic direction and development of all FINCAD products. Church was previously Vice President of OLAP product management at Business Objects and, before that, Director of OLAP product management at Crystal Decisions. He studied Computer Science at North Staffordshire University in the U.K.

Peyman Mestchian, Managing Partner, Chartis Research

Peyman Mestchian is Managing Partner at Chartis overseeing research strategy, key commercial relationships, and advisory services. His area of interest and research is the application of information technology to risk management and he is an established thought-leader and writer on the subject. Previously, Peyman was a Director of the Business Risk Consulting Practice at Ernst & Young and Global Head of the Enterprise Risk Management Practice at SAS Institute. Peyman is a Fellow of the Institute of Risk Management (FIRM).

Creating Competitive Advantage

with

Risk Management

© Copyright Chartis Research Ltd. 2015. All rights reserved.

Key market trends

5

Increased Capital RequirementsGlobal regulatory drivers Preserving shareholder value

Raise new equity capital

Shrink balance sheets

by selling assets

Increase retained earnings


Capital adequacy vs. Capital efficiency

6

Integrated, enterprise-wide

management of:

Stress testing

Model risk management

Risk data aggregation

High performance risk architecture

Enablers:

Falling

supply

Tightened eligibility

requirements

Hoarding by banks

Rising

Demand

Basel 3 liquidity requirements

Regulatory requirements to increase clearing

Increased collateralization for OTC derivatives

More frequent margin calls


Stress testing

7

Enterprise-wide

All risk-types and dependencies

Compound effect of market

and credit risk on each other

Overlaps market/credit,

credit/operational

Reverse stress testing

Key objectives:

LIQUIDITY

RISK

Parameter Forecasts

Good: back to pre-crisis

Base: fragile recovery

Bad: double dip

Worst: end of the world

Scenario Definitions

Business Plans Risk & Return Factors

Scenario Outcomes

Projected B/S & PnL

Risk and Return Profile

Regulatory Ratios

External Rating

Aggressive: Expand Business

Balanced: Selective growth

Deleverage: No new business

Market: FW, IR, Equities

Credit: CDS, PD, LGD

Op: Individual KPI’s

B/S & LI: Roll-overs, pre-pays

Macro: GDP, CPI…

Micro: Industry specific…

Market: FX, IR, Equities…


3rd party models validated against

expected outcomes and against

internal “golden source” models

8


Define/adjust use-case test sets

Check vs. use-case test sets

Re-c

alib

rate

Model

Inventory

Model risk

identification

Model risk

assessment

Ongoing

monitoring

Model risk

mitigation

Model

validation

Model

review

Model developers, owners

and users

Formalised control

framework for development,

implementation and use

First line of defence

Model risk management unit

Independent validation

testing, annual review, on-

going monitoring

Second line of defence

Internal audit

Audit compliance with

policies, procedures and

standards within first and

second line of defence

Third line of defence

Input

Process

Output

– Reports

– Usage

– Performance

– Conceptual soundness/calculations

– System implementation

– Back-testing

– Data quality

– Data aggregation

– Hypothesis assumptions

– Data availability/soundness

– Scenario proofing

– Regulatory requirements

– Documentation


Risk data aggregation

9

Integrated data and compute environments

Next generation data grids

Shared firm wide data standards & data

description languages

Hardware acceleration, parallelized compute

functions

Real add hoc risk calculations Integration of

RWA calculations into pricing library particularly

XVA calculations

Risk & Finance ecosystem data

governance & quality and drive towards model free

reporting architecture

Array oriented data management systems

replace RDBMS

Grid oriented middle office risk platforms with

direct front office analytics integration

Advanced

Early

Strategic linkage

Tactical linkage Compute management Data management

Reactive/slow Reactive/slow

Dynamic/responsive


High performance risk architecture

10

External reporting

(supervisors,

shareholder, etc.)

External reporting availability

varies: On-demand, end of

day, monthly, quarterly, next

trade date

Presentations: Scheduled, on-demand, real-time

Static Interactive Data-visualisation

Data at rest Streaming Data

Batch and Delayed In-Memory

Pre-defined

Reports

User defined

ReportsSpreadsheets

User drill-down

BI analysis

Configurable

Dashboards

Alerts and

Notifications

Data: Indirect access, read only, direct delayed access, immediate access

Conventional Data-store(s) Map

Reduce

Stored

Procedures

In-memory

ReferencePositions

Netting

AgreementsHistory

Processed

AggregatedProcessed

Aggregated

data

Incremental

Stream Data

Services: Periodic, regular, on-demand, continuous

Data transfers at different rates: Batch loads,

incremental changes, streamed data Transactional, end of day, periodic, data

processing speeds vary depending on

data store capabilities, use of in-memory,

complexity of computation, hardware

performance and numerous other factors

Batch 1

Batch 2 Batch 4

Batch 3

Collateral and margin management/risk based

pricing/performance management

Risk modelling & analytics: RWA exposure calculations, XVA

analytics, VaR scenarios, stress testing, research, etc…

– Event Processing

– Distributed processingCEP Grid

Market data

feedsTrade data Collateral data Historical data 3rd party data Reference data

Source data availability/delivery varies:

on-demand, end of day, end of month,

streamed/real/time, time-zone

dependent, etc.

External systems (market data, SDR/TRs, exchanges, CCPs, etc…)

Prepared reports Query Response Streamed data


Implement real-time collateral optimization engine

Enterprise-wide inventory of collateral assets

Subjecting liquidity risk to stress testing and reverse stress testing

Enterprise-wide view of counterpartycredit risk


Timely collection of accurate and granular data and information

Adaptable technology upgrades

Future outlook and recommendations

11

Priorities for 2015 and beyond:

Integration of risk analytics

across the enterprise

Business/

Front Office

CVA

Pricing/Trading

CVA

Collateral

Credit/Market

Exposures

Liquidity

RWA

Risk Adjusted

PerformanceFinance and

Treasury

Asset Valuation

ALM

Risk and

Compliance

CVA Charge

Capital

Management

Capital RatioReporting

Stress

Testing

EEPE, VaR, Stressed

VaR

Business/

Front Office

CVA

Pricing/Trading

CVA

Collateral

Credit/Market

Exposures

Liquidity

RWA

Risk Adjusted

Performance

Finance and

Treasury

Asset Valuation

ALM

Risk and

Compliance

CVA Charge

Capital

Management

Capital Ratio Reporting

Stress

Testing

EEPE, VaR, Stressed

VaR

www.chartis-research.com


“Risk management capabilities will increasingly become one of the differentiating characteristics between competing funds. ”

Alex Lurye, Head of Global Portfolio

Construction and Risk, Citadel


Increasing Complexity


82%Can’t effectively manage

multi-currency CSAs


70%Have difficultymanaging basis


COMPETITIVE ADVANTAGE

FAST

ACCURATE

FLEXIBLE

TRANSPARENT

HOLISTIC


Fast

Business Driver

Demand for timely risk information

1 Universal Algorithmic Differentiation™Fast calculation of risk

2 Risk re-projectionFlexible risk calculations for bond and swap portfolios

3 Highly scalable analytics platformDeployable to grids and cloud

Key Technology Enablers


Fast

Does your analytics platform deliver computationally-intensive risk

calculations in a timely manner?


Accurate

Business Drivers

Improved asset and liability matching

Efficient use of capital


1 Advanced modeling frameworkAccurately model multi-asset class portfolios

2 Consistent modeling assumptionsAbility to compare portfolio valuation and risk results


Accurate

Does your analytics platform ensure consistent assumptions are used for

accurate valuation and risk?


Flexible

Business Driver

Capitalize on new investment opportunities

1 Highly customizable curve buildingFlexibility to represent the market as accurately as you need

2 Broad multi-currency, multi-asset class coverageAbility to work with new asset classes and investment strategies

3 Integrate with existing systemsAccess trades, market data, and reference data from many sources



Flexible

Does your analytics platform enable you to find and capitalize on new

investment opportunities?


Transparent

Business Drivers

Increased focus on model validation

Greater investor demand for transparency

1 Full documentation for all modelsTransparency for model validation teams

2 Audit trail for changes to modeling assumptionsTrace model changes and understand their impact

3 Replay valuations from any point in timeTest modeling assumptions from any perspective



Transparent

Does your analytics platform keep an audit trail and document changes to

models and valuations?


Holistic

Business Drivers

Consistent assumptions within in across teams

Holistic view of risk across the enterprise

1 Consistent modeling assumptionsConsistent view across organizational silos

2 Consistent valuation and risk analyticsAccurate valuation and risk across portfolios

3 Managed sharing of assumptions and resultsEnsure all users have the same valuation and risk models



Holistic

Does your analytics platform enable you to share assumptions across the

enterprise consistently?


“Investors will most likely prefer firms that can demonstrate robust and effective risk management practices. ”

Alex Lurye, Head of Global Portfolio

Construction and Risk, Citadel


James [email protected]

Creating competitive advantage with risk management

Economy & Finance

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