Post on 15-Jul-2015
© 2015 - Proprietary and Confidential Information of FINCAD
Creating Competitive AdvantageWith Risk Management
Thursday, February 5, 2015
11:00 am (EST) | 8:00 am (PST) | 4:00 pm (GMT) | 12:00 am (HKT)
© 2015 - Proprietary and Confidential Information of FINCAD2
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© 2015 - Proprietary and Confidential Information of FINCAD3
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).
© 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
© Copyright Chartis Research Ltd. 2015. All rights reserved.
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
© Copyright Chartis Research Ltd. 2015. All rights reserved.
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…
© Copyright Chartis Research Ltd. 2015. All rights reserved.
3rd party models validated against
expected outcomes and against
internal “golden source” models
8
Model risk management
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
© Copyright Chartis Research Ltd. 2015. All rights reserved.
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
© Copyright Chartis Research Ltd. 2015. All rights reserved.
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
© Copyright Chartis Research Ltd. 2015. All rights reserved.
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
Model risk management
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
© 2015 - Proprietary and Confidential Information of FINCAD13
“Risk management capabilities will increasingly become one of the differentiating characteristics between competing funds. ”
Alex Lurye, Head of Global Portfolio
Construction and Risk, Citadel
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82%Can’t effectively manage
multi-currency CSAs
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COMPETITIVE ADVANTAGE
FAST
ACCURATE
FLEXIBLE
TRANSPARENT
HOLISTIC
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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
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Fast
Does your analytics platform deliver computationally-intensive risk
calculations in a timely manner?
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Accurate
Business Drivers
Improved asset and liability matching
Efficient use of capital
Key Technology Enablers
1 Advanced modeling frameworkAccurately model multi-asset class portfolios
2 Consistent modeling assumptionsAbility to compare portfolio valuation and risk results
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Accurate
Does your analytics platform ensure consistent assumptions are used for
accurate valuation and risk?
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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
Key Technology Enablers
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Flexible
Does your analytics platform enable you to find and capitalize on new
investment opportunities?
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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
Key Technology Enablers
© 2015 - Proprietary and Confidential Information of FINCAD25
Transparent
Does your analytics platform keep an audit trail and document changes to
models and valuations?
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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
Key Technology Enablers
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Holistic
Does your analytics platform enable you to share assumptions across the
enterprise consistently?
© 2015 - Proprietary and Confidential Information of FINCAD
“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