Download - Transition to IMA: How to ensure a smooth ride? 21 1h July 2010 Solutions Analytics Consulting
Transition to IMA: How to ensure a smooth ride?
211h July 2010
Solutions
AnalyticsConsulting
Business Case
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Business Case -IMA Approach
IMA is not merely a regulatory compliance framework; by taking a broad business perspective on the Accord, a bank can capture a series of strategic opportunities
Strategic Opportunities in IMA Adoption
A. Reputation : Enhanced perception of the Bank as a low risk and regulatory compliant bank in order which improves/maintains the bank’s cost of funds and share price
B. Better Risk Management: A more precise risk weighting of assets
C. Capital Management: Internal efficiencies and disciplines in Risk reporting and monitoring would lead to changes in product mix and pricing.
D. Rating: Banks may decide to grow via acquisitions or divest a portion of their assets, in order to maximize the benefits of a more efficient capital management model
E. Competition - Driven by the Peer group
Potential Threats of IMA Adoption
A. Capital Increase: Banks expect a significant increase in capital requirements under IMA
B. Relatively Small HFT portfolio: For Banks with relatively small HFT portfolio compared to AFS, it may not make business sense for migrate to IMA from ‘cost benefit analysis ‘
IMA Framework
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Qualitative Requirements for IMA approval
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Independent Risk Control Unit
The bank must have a Risk Control Unit that is fully independent of business units that generate market risk exposures.
Documentation The bank must have well documented policies for Trading Book, Market Risk, ALM Procedures Also, the bank must maintain an MRM Dossier to record model specification, changes and
updates.
Integrity & Accuracy
The bank must be able to demonstrate that it has a conceptually sound risk management system that is implemented with integrity
The bank must have a proven track record of measuring risks with reasonable accuracy.
Skilled-Staff The bank must have a sufficient number of skilled staff using sophisticated methods in trading
area, market risk control, validation and internal audit.
Use-Test The risk estimates produced must be closely integrated with the risk management process. The risk measurement system should be used in conjunction with internal trading and exposure
limits.
Quantitative Requirements for IMA approval
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VaR parameters VaR must be computed on a daily basis using a 10-day holding period and confidence level
of 99%. No particular model prescribed, but the model in use should be able to capture all the
material risks
Data
Historical Time horizon for calculating VaR will be constrained to a minimum length of one year.
Banks must update their data sets no less frequently than once every three months
Stress and Back Testing
A rigorous Stress Testing exercise should be in place to supplement the risk analysis. The bank must conduct a rigorous Back Testing to carry out an ex-post comparison of the
risk measure generated by the model against actual daily changes in the P/L as well hypothetical changes.
Empirical Correlations
VaR models should ideally capture correlations across broad risk categories spanning Market Risk (Interest Rate, FX, equity Price, Commodity)
Models must accurately capture the unique risks associated with options with suitable models For banks using advanced derivative products, the models must capture the non-linear risks
like gamma, vega.Non-Linear Risks
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Typical Implementation Approach
1 -2 wks 4 - 6 wks 6 - 8 wks 24 -50 wks
Ke
y M
eth
od
olo
gie
s /
To
ols
Establish Project Scope
Understand:Bank structureProgramme structureKey decisions made
Project Planning/ Resource Management
Policy & Procedures
Configuration of Pricing/VaR Models
Phase 0Scoping StudyPhase 0Scoping Study
Phase 1Gap Analysis & Planning
Phase 1Gap Analysis & Planning
Phase 2Framework Design & Specification
Phase 2Framework Design & Specification
VaR and Stress Testing Models
Phase 3Implementation PhasePhase 3Implementation Phase
Define projects to fill the identified gaps
Business Transformation ( Training & Communication )
Back-Testing*
Model Refinement
Workshops & Training
Data Validation
Basel II Gap Analysis : Existing vs. Target State
* The implementation of the back testing program should begin at least six months before the bank decides to make an application to RBI for approval.
Construction/ Selection of Market Risk System
Detailed Documentation
Application to RBI
Market Risk Governance Under IMA
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Front Office/Trading
Unit
Front Office/Trading
Unit
This desk comprises the trading desk and their immediate supervisors
Middle Office/Risk
Control Unit
Middle Office/Risk
Control Unit
The unit is responsible for design and implementation of the bank’s risk models, Stress and Back-testing.
Model Construction
Unit
Model Construction
Unit
If the bank’s model are built in house, this unit should comprise of staff who are not involved in model validation or internal audit.
Model Validation Unit
Model Validation Unit
This unit comprises of staff who take care of validation and were not involved in construction at all.
Back officeBack office
This unit ensures the correct recording of transactions and funds transfers.
Internal AuditInternal Audit
This unit is responsible for carrying out an independent review of activities of trading unit and Risk Control Unit.
Market risk organizational structure
IMA guidelines make a strong emphasis on the Organizational Structure and Quality of Governance for Market Risk
As per the guidelines, BoD and senior management should be proactively involved in Market Risk Control.
A typical organizational design of the market risk function of a bank complying with Basel II framework for Market Risk would have the following ‘Independent’ entities.
Design and Methodology
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Current VaR Practices
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Currently, banks in India calculate capital charge for Market Risk using SMM and use different VaR models( mostly Historical Approach) for calculating Market Risk for Internal Purposes.
TYPES OF VaR Models
Historical
Monte-Carlo Simulation
Variance-Covariance
Non-parametric method that involves re-valuing portfolios against a set of historical scenarios assuming they are a good representation of all the possibilities between today and tomorrow.
Parametric Method similar to historical simulation, but instead of using historical scenarios, it generates them randomly assuming that portfolio returns follow a Normal Distribution
Parametric Method involves calculating VaR analytically by making assumptions about return distributions and by using variance- covariance
A typical Market Risk Distribution for a Sample Portfolio
Selection of VaR Methodologies
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Features Historical Approach Monte Carlo Simulation
Variance-Covariance
Distribution
Shape Actual Usually Normal Normal
Use Correlations No Yes Yes
VAR precision Poor with short window Good with many iterations Poor
Implementation
Ease of computation Easy Intensive Intermediate
Accuracy Yes Yes Poor for non-linear dependencies
Communicability Easy Difficult Easy
VaR analysis Easy More difficult Easy
Capture Non-Linear Risks Yes Yes No
Other s Historical events maybe irrelevant in the present context
Model risk Needs regular Updation of variance-covariance Matrix
Stress Testing
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TYPES OF STRESS TESTS
Scenario Building -Historical Analysis
Sensitivity Analysis
Mechanical-Search
This type uses scenarios from recent history, such as the 1987 equity crash, 2007 sub-prime crisis and simulate the effect on P/L of repeats of past historical events.
‘Reverse’ Stress Testing
This type uses pre-defined or regulator prescribed scenarios that have proven to be useful in practice. Example, change in equity price by x%, yield curve shift of y-basis points.
This type uses automated routines to generate over prospective changes in risk factors, and report the worst effect on P/L.
Reverse Stress-tests require a bank to assess scenarios and circumstances that would render its business model unviable, thereby identifying potential business vulnerabilities. It starts from an outcome of business failure and identifies circumstances where this might occur.
Pitfalls in Currently employed Stress Testing Methods
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Back TestingBack Testing
The Stress-Test procedures are very difficult to back-test as hypothetical stress scenarios cannot be “validated” based on actual events.
Stress Testing tests are unavoidably subjective because they depend on scenarios chosen by the stress tester. Hence, its value depends on the choice of scenarios and on the skills of the modeler.
Subjective NatureSubjective Nature
The results of stress tests are difficult to interpret because they give us no idea about the probabilities of the events.
Difficult InterpretationDifficult Interpretation
Methodology of stress testing is still in its infancy, and the approaches commonly used are open to objection. No. of risk factors that can be stressed simultaneously is capped.
Lack of SophisticationLack of Sophistication
VaR estimates which are probabilistic in nature cannot be integrated with loss estimates generated by Stress Tests under different scenarios.
IntegrationWith
VaR models
IntegrationWith
VaR models
Model Validation under IMA
Model validation consists of assessing five major components:
Governance & Oversight
Data integrity
Assumptions
Methodology
Back testing
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Validation must be carried out independently by internal and external auditors before RBI Validation
Documentation
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Documentation
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Scope of Application of the ModelDescription of ExposuresEstimation of Regulatory CapitalPolicies and OrganizationMarket Risk Measurement SystemStress Testing ProgrammeBack Testing ProgrammeTechnological Environment and Information Integrity ControlsLimits Structure/Information Systems/ Databases EmployedOperational Manual and Input Tables of the Market Risk CalculationInternal Audit ReportOther Independent AssessmentsFuture Developments
MR File ContentMR File Content
For obtaining RBI approval for the IM risk models of banks, the following documents are to be submitted:
Request for RBI approval of the IMA modelInternal audit report of the modelA Market Risk (MR) fileMR Model Dossier
MR file describes the internal model, the risk management control system and substantiates the compliance with the quantitative and qualitative requirements of these guidelines.
The MRM Dossier
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The content of this document is similar to the content of the MR file. However, the difference between the two lies in the fact that the MR file is submitted at the time of application to the RBI, whereas the MRM Dossier is maintained and updated on a regular basis. Apart form the various components of the MR file, the following information must also be contained in the MRM Dossier:
Full technical specifications of the model RBI approval for the model and subsequent changes, if any, made to the model along with the conditions subject to which the approval has been granted. Complete details and record of subsequent changes, if any (such as new products covered, modifications to the score of
the model, revision of sources of external data, modifications in the applications, organizational changes etc.) in the operation of the approved model.
Authors responsible for the contents, date updated Detailed Stress Testing and Back Testing Results Uses to which the VaR is put within the bank Weaknesses identified in the model
MRM DossierMRM Dossier
The purpose of the MRM Dossier is to keep a record of the details of the model and the changes/refinements, if any, made in from time to time.
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Snapshot of Dossier
MR File Volume: typically 200-300pages (A4 Word)
MRM Dossier Volume: typically 150-200 pages (A4 Word)
MRM Dossier
MRM DossierMR FileMR File
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Documentation for Market Risk policy
Typically 50-100 pages (A4 Word)
System/IT
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Risk Technology Architecture
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Market Risk Capital Management
Operational RiskCredit Risk
Compliance Tracker
Oracle /SQL / DB2
Risk Database / Data Warehou
se
Oracle /SQL / DB2
Risk Database / Data Warehou
se
Retail Operational Systems
Wholesale Operational Systems
Source Systems
Finance Systems
ETL Tool
Data Integrati
on(ETL)
ETL Tool
Data Integrati
on(ETL)
1 2 3 4
5
7
8
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Operational risk mgt solution
Operational risk modeling
Market Risk Analytics
ALM & Liquidity Risk
Counterparty Credit
Collateral & Limit Mgt
Credit Workflow
Rating Engine
Regulatory Capital Engine
Economic Capital Modeling
Budgeting and Forecast
ERM SolutionComponents
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Risk Technology architecture would be driven by the functional Risk architecture design and implementation at Group and Entity level. A generic risk technology architecture (illustrative) highlighting the key technology and systems components is presented below.
Risk Technology architecture would be driven by the functional Risk architecture design and implementation at Group and Entity level. A generic risk technology architecture (illustrative) highlighting the key technology and systems components is presented below.
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Timing of System Initiatives
Build Buy N/A
• Multiple systems are used to
source the risk data and then
run through series of data
transformations and
integration layers for Pillar
I /ECAP calculations.
• Additionally, the Risk
Technology Architecture
must be interfaced with
Finance Systems and the
impending ERM
components* to provide for
an enterprise wide risk
dashboard / reporting
mechanism
• With some systems already
bought/built/implemented
and or under implementation
(either bought or being built),
some interim adjustments /
upgrades may be made in
the architecture to ensure a
practical transition to the
target risk architecture.* The diagram outlined is indicative based on our understanding and may/may not
reflect the current or proposed architecture at the Bank.
Risk IT Architecture
Retail Risk Systems
Retail Risk Systems
Global Counterparty
Limit Management
(Fermat)
Global Counterparty
Limit Management
(Fermat)
Market Risk(Algorithmics)
Market Risk(Algorithmics)
Rating systems
Pillar II Risk Data Warehouse
Basel II Calculator
(Bancware) Enterprise wide Risk
Dashboard / Reporting
Operational Risk
Management(OpenPages)
Operational Risk
Management(OpenPages)
ETL (Data Transformations Management)
ETL (Data Transformations Management)
Credit Workflow(Fermat)
Credit Workflow(Fermat)
Risk Modelling System
Economic CapitalFinance (Capital
Budgeting)
Stress Testing
Pillar II Reporting
NEW (W)201X Q1
NEW (W)201X Q1
NEW (W)201X Q1
NEW (W)201X Q1
NO CHANGE
NO CHANGENO CHANGE UPGRADEUPGRADE
UPGRADE
Challenges for the Supervisor
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No clarity on data period for calculating ‘Stressed VaR’ makes it difficult to assess its value objectively .
Under Stress-Testing, scenario building depends on the choices made by the modeler and his skill-set which could make it difficult to compare its results with that of Banks of similar size.
For banks with spreadsheet based IMA models, it is difficult to maintain audit trails and controls
Back-Testing results could be meaningless if the portfolio of a bank changes drastically and frequently. In that case Back Testing has to be done more frequently depending upon the nature of the portfolio.
Staff-skill set needs to be diverse in order to review and validate different and eclectic models used by the Banks as no particular method is prescribed.
No clarity on data period for calculating ‘Stressed VaR’ makes it difficult to assess its value objectively .
Under Stress-Testing, scenario building depends on the choices made by the modeler and his skill-set which could make it difficult to compare its results with that of Banks of similar size.
For banks with spreadsheet based IMA models, it is difficult to maintain audit trails and controls
Back-Testing results could be meaningless if the portfolio of a bank changes drastically and frequently. In that case Back Testing has to be done more frequently depending upon the nature of the portfolio.
Staff-skill set needs to be diverse in order to review and validate different and eclectic models used by the Banks as no particular method is prescribed.
Confidentiality clauseThis document is confidential. No part of it may be circulated or reproduced outside without express approval of Aptivaa Consulting.© Aptivaa Consulting 2010.
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