Financial Risk Management Framwork & Basel Ii Icmap

Financial Risk Management
Framework
Javed H Siddiqi

Risk Management
“ Every experience you have is designed to make you stronger ”
Javed H. Siddiqi

Managing Risk Effectively: Three Critical Challenges
Management Challenges for the 21 st Century
GLOBALISM TECHNOLOGY CHANGE

What is Risk?
Risk, in traditional terms, is viewed as a ‘negative’. Webster’s
dictionary, for instance, defines risk as “exposing to danger or hazard”.
The Chinese give a much better description of risk
> The first is the symbol for “danger ”, while
>the second is the symbol for “opportunity ”, making risk a mix of danger and opportunity.

Risk Management Risk management is present in all aspects of life; It is about the everyday trade-off between an expected reward an a potential danger. We, in the business world, often associate risk with some variability in financial outcomes. However, the notion of risk is much larger. It is universal, in the sense that it refers to human behaviour in the decision making process. Risk management is an attempt to identify, to measure, to monitor and to manage uncertainty.

Risk Assessment
Assess your risk bearing capacity
How much risk can you tolerate?
How much risk protection can you afford?
How much risk are you willing to accept

Risk Management
Risk management integrates production, marketing & financial decisions
Risk management is a planning process where you assemble and assess information
Every management decision carries risk management implications

Risk Management Requires
Understanding of Your financial situation
Understanding sources of risk and potential risk
Understanding of risk management tools

Risk Management Includes :
Evaluation of alternative plans & risk management strategies
Implementation of the plan
Monitoring the plan
Developing probabilities to formalize risk assessment

Steps in the Risk Management Process
Determine the corporation’s objectives
Identify the risk exposures
Quantify the exposures
Assess the impact
Examine alternative risk management tools
Select appropriate risk management approach
Implement and monitor program

The Bottom Line: It All Boils Down to Capital
“ Capital ”
Assets less liabilities; owners’ equity; net worth
Support for (riskiness of) operations
Thus, supports profitability and solvency of firm
“ Capital Management ”
Determine need for and adequacy of capital
Plans for increasing or releasing capital
Strategy for efficient use of capital

Why Do We Care About Managing Capital?
Leads to solvency and profitability
Benefits of solidity and profitability
Higher company value
Happy claimholders
Better ratings
Less unfavorable regulatory treatment
Ability to price products competitively
Customer loyalty
Potentially lower costs

What Does Capital Management Entail? Capital Management Product Pricing Financial Risk Mgt. Setting Objectives Raising Capital Strategic Planning Liability Valuation Asset Allocation Risk Management

Capital Allocation and RAPM
The role of the capital in financial institutions and the different type of capital.
The key concepts and objective behind regulatory capital.
The main calculations principles in the Basel II the current Basel II Accord.
The definition and mechanics of economic capital.
The use of economic capital as a management tool for risk aggregation, risk-adjusted performance measurement and optimal decision making through capital allocation.

Role of Capital in Financial Institution
Absorb large unexpected losses
Protect depositors and other claim holders
Provide enough confidence to external investors and rating agencies on the financial heath and viability of the institution.

Type of Capital
Economic Capital (EC) or Risk Capital .
An estimate of the level of capital that a firm requires to operate its business .
Regulatory Capital (RC).
The capital that a bank is required to hold by regulators in order to operate .
Bank Capital (BC)
The actual physical capital held

Economic Capital
Economic capital acts as a buffer that provides protection against all the credit, market, operational and business risks faced by an institution.
EC is set at a confidence level that is less than 100% (e.g. 99.9%), since it would be too costly to operate at the 100% level.

Risk Measurement- Expected and Unexpected Loss
The Expected Loss (EL) and Unexpected Loss (UL) framework may be used to measure economic capital
Expected Loss: the mean loss due to a specific event or combination of events over a specified period
Unexpected Loss: loss that is not budgeted for (expected) and is absorbed by an attributed amount of economic capital
Losses so remote that capital is not provided to cover them. 500 Expected Loss, Reserves Economic Capital = Difference 2,000 0 Total Loss incurred at x% confidence level Determined by confidence level associated with targeted rating Probability Cost 2,500 EL UL

Financial Risk and Basel Javed H Siddiqi

BASEL-I Capital Calculation
Basel I Principles
Strengthen the stability of the international banking system
Create minimum risk-based capital adequacy requirements
Basel I Benefits
Relatively simple framework
Widely adopted
Increased banks’ capital
Credit Risk + Market Risk Capital Capital Adequacy Ratio RIWAC =

Basel I Regulatory Capital Rules Types of capital Basel I capital calculation Market risk Capital (Tier 3)
Short-term subordinated debt
Supplementary Capital (Tier 2)
Perpetual securities
Unrealised gains on investment securities
Hybrid capital instruments
Long-term subordinated debt with maturity > 5 years
Core Capital (Tier 1)
Stock issues
Disclosed reserves
Loan loss reserves to cushion future losses or for smoothing out income volatility
50% of total capital
Risk weights Traded Non-Traded Off-balance sheet assets Balance sheet assets Capital (Tiers 1, 2, 3) Risk-Weighted Assets and Contingents ≥ 8%

RIWAC Calculation RIWAC On-Balance Sheet x Counterparty Weighting Off-Balance Sheet Risk x Counterparty Weighting x Credit Conversion Factor = +

RIWAC Weightings 100% Non OECD Sovereigns 0% OECD 100% Corporates OECD Non OECD 100% Banks 20% On-Balance Sheet Risk N/A N/A N/A N/A N/A N/A Off Balance Sheet Risk Cont. liabilities 4% 10% 20% 20% 50% 100% 20% 50% 100% Secured LCs Issued Transactional Contingents Financial Guarantees

BASEL I- RIWAC Examples Corporate XYZ Bank Lends USD 100 M to UAE Corporate for 1 year Capital = USD 100 M X 100% (Risk Weight) X 8% (Capital Adequacy) = USD 8 M Banks XYZ Bank Lends USD 100 M to Barclays Bank for 2 years Capital = USD 100 M X 20% (Risk Weight) X 8% (Capital Adequacy) = USD 1.6 M Contingents XYZ confirms Sight L/C of USD 100 M issued by ABN AMRO Capital = USD 100 M X 20% (Risk Weight) X 20% (CCF) X 8% (Capital Adequacy) = USD 0.32 M

Basel I regulatory capital rules – Credit risk (1) On-balance sheet risk weights and Basel I capital calculation Off-balance sheet risk weights and Basel I capital calculation for non-trading assets Step 1: RWA = On BS exposure X Risk Weight Step 2: Capital = 8% X RWA
Corporate bonds, equity, real-estate
Less-developed countries’ debt
Claims on non-OECD banks
100
Residential mortgages
50
Claims on OECD banks
Govt. agency securities
Claims on municipalities
20
Cash & gold
Obligations on OECD and PAK treasuries
0 On-balance sheet asset category Risk weight (%)
Corporates and other counterparties
50
OECD banks and public sector entities
20
OECD governments
0 Off-balance sheet asset category Risk weight (%)
Documentary credits related to shipment of goods
20
Transaction-related contingencies – warranties, performance bonds
Undrawn commitments – Maturity > 1 year
50
General guarantees, standby letters of credit, banker’s acceptance, etc
100
Undrawn commitments – Maturity ≤ 1 year
0 Off-balance sheet non-trading assets Credit Conversion Factor (%) Step 1: Credit Equivalent Amount (CEA) = Notional amount X Credit Conversion Factor Step 2: RWA = CEA X Risk Weight Step 3: Capital = 8% X RWA

Basel I regulatory capital rules – Credit risk (2) Off-balance sheet risk weights and Basel I capital calculation for trading assets 10.0% 7.0% 6.0% 1.0% 0.0% Less than 1 year Commodity contracts Precious metals Equity derivatives FX and Gold Interest rates More than 5 years 1-5 years 1.5% 0.5% 7.5% 5.0% 10.0% 8.0% 8.0% 7.0% 15.0% 12.0% Credit Conversion Factor (%) Step 1: Current Exposure (CE) = Current marked-to-market value of asset Step 2: Potential Future Exposure (PFE) = Notional amount X Credit Conversion Factor Step 3: Credit Equivalent Amount (CEA) = CE + PFE Step 4: RWA = CEA X Risk Weight Step 5: Capital = 8% X RWA

BASEL I- Draw Backs Criticisms of Basel I Accord
Lack of risk sensitivity of capital requirements
One-size-fits-all’ approach to risk management
Limited attention to credit risk mitigation
Over emphasis on minimum capital requirements
Exclusive focus on financial risk
Consequences in the industry
Sub-optimal lending behavior
Increased divergence between regulatory capital and economic capital
Regulatory capital arbitrage through product innovation

Objectives “Basel II”
The objective of the New Basel Capital accord (“Basel II) is:
To promote safety and soundness in the financial system
To continue to enhance completive equality
To constitute a more comprehensive approach to addressing risks
To render capital adequacy more risk-sensitive
To provide incentives for banks to enhance their risk measurement capabilities

Comparison Introduces approaches for Credit risk and Operational risk in addition to Market risk introduced earlier. Operational risk not considered More risk sensitivity Broad brush structure Flexibility, menu of approaches. Provides incentives for better risk management One size fits all More emphasis on banks’ internal methodologies, supervisory review and market discipline Focus on a single risk measure Basel 2 Basel I

Economic Objectives
Efficiency : best use of capital across business lines, impetus for risk based pricing and operational cost savings
Stability : ensure capital protection consistent with shareholder value optimization

Economic Objectives
Growth sustainability : balanced Portfolio risk and return
Equity : level competitive playing field across(big and small) banks

Overview of Basel II Pillars The new Basel Accord is comprised of ‘three pillars’… Pillar I
Minimum Capital Requirements
Establishes minimum standards for management of capital on a more risk sensitive basis:
Credit Risk
Operational Risk
Market Risk
Pillar II
Supervisory Review Process
Increases the responsibilities and levels of discretion for supervisory reviews and controls covering:
Evaluate Bank’s Capital Adequacy Strategies
Certify Internal Models
Level of capital charge
Proactive monitoring of capital levels and ensuring remedial action
Pillar III Market Discipline Bank will be required to increase their information disclosure, especially on the measurement of credit and operational risks. Expands the content and improves the transparency of financial disclosures to the market.

Development of a revised capital adequacy framework Components of Basel II Pillar 1 Pillar 2 Pillar 3 The three pillars of Basel II and their principles Basel II
Continue to promote safety and soundness in the banking system
Ensure capital adequacy is sensitive to the level of risks borne by banks
Constitute a more comprehensive approach to addressing risks
Continue to enhance competitive equality
Objectives Supervisory review process
How will supervisory bodies assess, monitor and ensure capital adequacy?
Internal process for assessing capital in relation to risk profile
Supervisors to review and evaluate banks’ internal processes
Supervisors to require banks to hold capital in excess of minimum to cover other risks, e.g. strategic risk
Supervisors seek to intervene and ensure compliance
Market disclosure
What and how should banks disclose to external parties?
Effective disclosure of:
Banks’ risk profiles
Adequacy of capital positions
Specific qualitative and quantitative disclosures
Scope of application
Composition of capital
Risk exposure assessment
Capital adequacy
Minimum capital requirements
How is capital adequacy measured particularly for Advanced approaches?
Better align regulatory capital with economic risk
Evolutionary approach to assessing credit risk
Standardised (external factors)
Foundation Internal Ratings Based (IRB)
Advanced IRB
Evolutionary approach to operational risk
Basic indicator
Standardised
Adv. Measurement
Issue Principle

Overview of Basel II Approaches (Pillar I) Approaches that can be followed in determination of Regulatory Capital under Basel II Total Regulatory Capital Operational Risk Capital Credit Risk Capital Market Risk Capital Basic Indicator Approach Standardized Approach Advanced Measurement Approach (AMA) Standardized Approach Internal Ratings Based (IRB) Foundation Advanced Standard Model Internal Model Score Card Loss Distribution Internal Modeling

The Three Pillars
The First Pillar - Minimum Capital Requirements
The Second Pillar - Supervisory Review Process
The Third Pillar - Market Discipline

Pillar 1
Calculation of the total minimum capital requirements for credit, market and operational risk.
The minimum capital requirements are composed of three fundamental elements: a definition of regulatory capital, risk weighted assets and the minimum ratio of capital to risk weighted assets.

RISK BASED SUPERVISION

BASEL II : CAPITAL CHARGE

Credit Risk
The standardized approach
The Internal Ratings-Based Approach
Foundation
Advanced

CREDIT RISK WEIGHTS

Credit Exposure Classes
Sovereigns - countries, central banks and multilaterals with 0% risk
Banks and non-banks- banks, investment houses, securities firms
Retail -individuals/persons & their guarantees(credit card, personal loan, rem, small business) or pools of these loans with similar characteristics

Credit Exposure Classes
Sme - exposure to individual owner, partners and enterprises owned by group usually with government incentives or programs

Coverage And Compliance
110 signatory countries (ye 2003).
All banks, investment houses and securities firms, asset/fund management companies and bank owned/controlled insurance companies.

C & C
Banking areas affected : regulatory compliance, audits, risk management practices, accounting standards, financial products and services, human resources, it/systems

Standardized Approach

Internal Ratings Based

FIRB VS. AIRB

IRB
Borrower risk rating - inherent creditworthiness without considering facility type or security arrangements. Transformed into a PD
Facility risk rating -risk rating considering the various security arrangements or credit risk mitigation techniques(thus lower LGD values)

IRB
Collaterals
Netting
Guarantees and credit derivatives

LGD Valuations
FOUNDATION IRB
CI REAL ESTATE= 35%
RECEIVABLES FULLY SECURED LOANS=35%
OTHER PHYSICAL COLLATERALS=40%
UNSECURED LOANS=50%
FINANCIAL ASSETS (SCALED BY HAIRCUTS)= 0.5-15%
SUBORDINATED CLAIMS=75%
ADVANCED IRB
BANK OWN ESTIMATES

Credit Risk Mitigants
Collateral
Standard haircuts(issuer,rating, tenor, type)
Mark to market
Operational risks(eg. Legal)
Concentration risks

Credit Risk Mitigants
Netting
Master netting legal agreements(net positions)
Marked to market all
Transactions
Currency and maturity mismatches
Creditderivatives/guarantees
Counterparty/issuer risks
Derivatives documentation (legal)
Market risks

Credit Risk Impact

Credit Risk Impact
IRB estimated to reduce credit risk capital charges by 2-3% versus standardized approach. Another possible 10-20% capital charge reduction versus foundation approaches.

Key Basel Compliance Requirements
Reliable historical credit statistics: default rates, recoveries (e.G. Market valuation of collaterals), portfolio concentration data, financial statement analysis/ratio history and projections, exposure valuation)
Intensive credit risk analysis and portfolio modeling Skills
Integrated central exposure system with on line Analysis/processing functions

Key Compliance Requirements
Robust internal ratings
Appropriate use of credit risk/var models
Appropriate credit risk rating and modelling software

Market Risk Compliance
Timely and accurate daily mark to market accounting/data and valuation of fx and securities portfolio
Reliable and robust value at risk model Including historical simulation/ backtesting And stress testing results
Integrated on line market risk monitoring And control system
Well trained users(back, front and middle Office)

Ops Risk
The Basic Indicator Approach
The Standardised Approach
Advanced Measurement Approach

Ops Risk Impact

Operating Risk Compliance
High awareness level of operational risk and Control inherent in all business processes, their Likelihood and financial loss impact significance
Timely and reliable information /monitoring of key Operational risk indicators/events (transaction Volume, financials, system downtimes, control Exceptions, process errors etc) to form part of Operational event loss data base

Operating Risk Compliance
Establishing minimum risk control Benchmarks/standards and gaps versus actuals
Intensive operational risk & control training
Robust operational risk models (loss given event, Probability of loss, exposure indicators)

Pillar 2 – Supervisory Review
Intended not only to ensure that banks have adequate capital to support all the risks in their business, but also to encourage banks to develop and use better risk management techniques in monitoring and managing their risks.
Supervisors are expected to evaluate how well banks are assessing their capital needs relative to their risks and to intervene, where appropriate.

Pillar 2
Three main areas suited to treatment under Pillar 2:
Risks considered under Pillar 1 that are not fully captured by the Pillar 1 process (e.g. credit concentration risk)
Those factors not taken into account by the Pillar 1 process (e.g. interest rate risk in the banking book, business and strategic risk)
Factors external to the bank (e.g. business cycle effects).

Four Key Principles of Supervisory Review
Principle 1: Banks should have a process for assessing their overall capital adequacy in relation to their risk profile and a strategy for maintaining their capital levels.

Four Key Principles
Principle 2: Supervisors should review and evaluate banks’ internal capital adequacy assessments and strategies, as well as their ability to monitor and ensure their compliance with regulatory capital ratios. Supervisors should take appropriate supervisory action if they are not satisfied with the result of this process.

Four Key Principles
Principle 3: Supervisors should expect banks to operate above the minimum regulatory capital ratios and should have the ability to require banks to hold capital in excess of the minimum.

Four Key Principles
Principle 4: Supervisors should seek to intervene at an early stage to prevent capital from falling below the minimum levels required to support the risk characteristics of a particular bank and should require rapid remedial action if capital is not maintained or restored.

Pillar 3 Market Discipline
The purpose of Pillar 3 - market discipline is to complement the minimum capital requirements (Pillar 1) and the supervisory review process (Pillar 2).
Encourage market discipline by developing a set of disclosure requirements which will allow market participants to assess key pieces of information on the scope of application, capital, risk exposures, risk assessment processes, and hence the capital adequacy of the institution.

Banks approach to Basel II Transformation A Journey of Seven Steps… Phase I: Gap Analysis Phase II: Implementation Roadmap Phase III: Implementation Phase IV: Compliance And Certification Approach to Basel II: Recommended Seven Steps Supervisory Certification, Parallel Run and Go Live Basel II Program Initiation Gap Analysis Implementation Roadmap Organization, Policies And Processes Redesign Data Management & IT Applications Analytics- Models, Methodologies and Validation

Challenges
Establishing a sound credit risk Rating system
Enhancing risk management Infrastructure: var based Measurement using central data Repository and risk engines
Capital allocation by Business:higher returns to Compensate higher risks
Establishing a risk based culture

MINIMUM CAPITAL REQUREMENTS FOR BANKS (SBP Circular no 6 of 2005) 14% 12% 5 12% 10% 4 10% 9% 3 8% 8% 1 & 2 31 st Dec., 2006 and onwards 31 st Dec. 2005 Institutional Risk Assessment Framework (IRAF) Required CAR effective from IRAF Rating

Operational Risk and the New Capital Accord
Operational risk is now to be considered as a fully recognized risk category on the same footing as credit and market risk.
It is dealt with in every pillar of Accord, i.e., minimum capital requirements, supervisory review and disclosure requirements.
It is also recognized that the capital buffer related to credit risk under the current Accord implicitly covers other risks.

Operational risk Background Description
Three methods for calculating operational risk capital charges are available, representing a continuum of increasing sophistication and risk sensitivity:
(i) the Basic Indicator Approach (BIA)
(ii) The Standardised Approach (TSA) and
(iii) Advanced Measurement Approaches (AMA)
BIA is very straightforward and does not require any change to the business
TSA and AMA approaches are much more sophisticated, although there is still a debate in the industry as to whether TSA will be closer to BIA or to AMA in terms of its qualitative requirements
AMA approach is a step-change for many banks not only in terms of how they calculate capital charges, but also how they manage operational risk on a day-to-day basis
Available approaches Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. This definition includes legal risk, but excludes strategic and reputation risk

The Measurement methodologies
Basic Indicator Approach:
Capital Charge = alpha X gross income
* alpha is currently fixed as 15%
Standardized Approach:
Capital Charges = ∑beta X gross income
(gross income for business line = i=1,2,3, ….8)
Value of “Greeks” are supervisory imposed

The Measurement methodologies
Business Lines Beta Factors
Corporate Finance 18%
Trading & Sales 18%
Retail Banking 12%
Commercial Banking 15%
Payment and Settlement 18%
Agency Services 15%
Asset Management 12%
Retail Brokerage 12%

I. Definition Operational risk is the risk of loss resulting from inadequate or failed internal processes, people, and systems or from external events.
Categories of OR events
Execution, Delivery & Process Management (processing error, information transfer, data coding,...)
Clients, Products & Business Practices (clients misinformation, complaints and discounts due to errors, products mispecification...)
Internal fraud (thefts and frauds by employees)
External fraud (hold-up, thefts,..)
Employment practices & workplace safety (contract termination, disputes with employees...)
Damage to physical assets
Business disruption & system failures (IT break-down, hacking...)

I. Definition
The Specific Nature of Operational Risk
Embedded risk
Not a transaction-risk but a risk embedded in processes, people and systems and due to external events.
Inherent risk
A large part of operational risk is inherent to the business in which we are engaging and inherent to management processes.
Hidden risk
The costs due to OR are difficult to trace or anticipate since most are hidden in the accounting framework.
Leads to underestimation of the risk (e.g. information security).
Unstable risk
Not linearly linked to the size of the activities. Small activities can be very risky high risk, and vice versa.
OR can be very unstable and grow exponentially in a short period.
Reputation risk
A second order risk, leading to additional damage in the form of damage to reputation.

I. Definition Underlying causes of operational losses : processes - people - systems - or external events. Legal risk included , strategic and reputation risk excluded.
Appropriate manager per category of operational event :
Execution, Delivery & Process Management : ORM
Clients, Product & Business Practices : ORM
Internal fraud : Inspection / ORM
External fraud : Inspection
Employment practices & workplace safety : Security
Damage to physical assets : Security
Business disruption & system failures : Security

General Objective :
Define rules and procedures for banks to properly cover their different types of risks due to business activity.
Three Pillars
Pillar One : Capital Adequacy - formulas and calculations
Pillar Two : Supervisory Review Process - adjustment of supervision to individual risks profiles
Pillar Three : Market Discipline - information disclosure
II. Outlines of the Basle Reform

Regulatory Capital for OR introduced for the first time
Rule of thumb : OR capital = 12% of minimum capital requirement
Basic indicator approach ( BI ):
OR capital function of gross income (15%)
Gross income = interest margin + fees + other revenues
Only accessible to local banks
Standardised approach (  )
OR capital function of gross income per business line
Beta factor between 12% and 18% of gross income, estimated via QIS on a sample of 29 institutions.

Advanced Measurement Approach ( AMA ) in Basle II:
Banks are free to model their OR capital themselves
Strongly recommended for internationally active banks
Floor capital at 75% (so far) of the capital level under the Standardised Approach, and 9% of total regulatory capital
Submitted to quantitative and qualitative standards, such as:
incident reporting history of 5 years, minimum 3 years;
mapping of risks and losses to regulatory categories
independent ORM function;
implication of the senior management;
written policies and procedures;
active day-to-day OR management.

Advanced Measurement Approach ( AMA ) in Basle II:
Several types of models admitted by the Committee:
Loss Distribution Approach (LDA) : purely quantitative
Scorecard approach :mainly quantitative : assessment of risk level and quality of risk management based on different dimensions
Mix of the two : capital calculations based on incident data + adjustments to account for risk management quality

III. Modelling Operational Risk
Quantitative approach : LDA ( Loss Distribution Approach )
Frequency distribution of losses per business line : Poisson distr.
Severity distribution of losses per business line : logN distr.
Both distributions are combined by Monte Carlo simulations.

III. Modelling Operational Risk
LDA
Modeling of frequency and of severity distribution of losses, per business line
Internal data : to model to body of the distribution
External data : to model extreme events (tail of the distribution)
Frequency Loss amount Body region Tail region Internal data External data Cut-off mix 99.9% = Required Capital

III. Modeling Operational Risk
Paradox of the incident data collection :
Data collection is mandatory,
But external data essentially drive the capital amount.
Remaining issues on :
the cut-off mix
the relevant data to include (different processes in each firm)
Crucial data choice in the capital determination Data collection needed for active ORM reasons.

IV. Managing Operational Risk Dashboards - Dynamic risk analysis Key Risks /Key Performance Indicators Risk & Control Self-Assesment (RCSA) Internal Reporting : Mapping of losses Four Dimensions of Operational Risks

Dimension One : Incident Reporting
Incident reporting tool :
Free to define, often Access based
Full reporting tool, for management purposes
Internal control when encoding
Fields to include per event :
1. Date
2. Event localisation : BU, department, service
3. Event type : codification of Basle categories
4. Business line : codification of Basle categories
5. Comment : nature of the event
6. Gross Loss amount
7. Recovery amount : via insurance / other
8. Actions taken : preventive / corrective
9. Reporter coordinates.

First exploitation possibilities of an incident database
Summary statistics of the losses
! Matching the organisation chart rather than the Basle categories
Total losses, Min, Max, Frequency
“ Low Frequency, High Severity” events
Identification of the potential “uncapped” risks
Top loss analysis
Examples?
“ High Frequency, Low Severity” events
Recurrent, small, similar events
May signal a breach in control
Could be inherent to the activity (to be included in pricing)
Dimension One : Incident Reporting

Dimension Two : Dynamic Loss Analysis
Dashboards
Periodic reporting (monthly/quarterly) of KRI’s
Early warning: timely identification of changes in control level : change in the trend
Example

Dimension Three : Key Risks & Key Performance Indicators
People: turn-over, temporary staff, overtime, client complaints, absenteeism
Processing: outstanding confirmations, (status/duration of) reconciliation; failed & overdue settlements; claims & complaints; manual bookings; reversals
Accounting: volumes & lead-times suspense-accounts; reversals;
Systems: logs of downtimes; hacking-attempts; project-planning-overruns

Dimension Three : KRIs & KPIs
Headlines :
Regular KRI reporting for all businesses and functions
Green, Amber and Red thresholds for all KRI’s
Develop new/better KRI’s on on-going basis
Discuss all KRI reports in OR committee
Immediate management response to red and amber KRI’s
Trend analysis and local lessons learnt program

Dimension Four : RCSA

Identification
Incident reporting analysis
Check list from the key risks library
Prioritization list with the line management
Orientation questionnaires with selected people from the department.

RCSA performed by local management, with the support of ORM
RCSA processes for all key businesses and functions
High level management driven identification of key risk areas
Apply & document the analytic RCSA process
Report & discuss the outcomes of a RCSA in ORC
Implementation & progress-tracking of mitigating actions and key risk indicators (KRI)
Line management is responsible and key for the output

Dimension Four : RCSA Assessment : Impact / Probability Matrix Based on a risk analysis report which reflects all (residual) risks and controls. Note : each point on the graph represents a different event or potential risk. Ex. Misleading capture screen in equity brokerage Ex. Product misspecification

Mitigation of uncapped or significant risks via :
Better controls : process control / supervision / training,
Transfer : insurance policies / merge of activities,
Avoidance : activity suppression / outsourcing / automation.

Quantitative assessment of active ORM techniques
Principle : modify the parameters of the losses distribution, to include the impact of the active management.
Risk Adjusted Return on Capital (RAROC) adaptable to Operational Risk.
We define:
with EL and EC readily available.
Operational Income is assumed equal to 5% of total revenues.
V. Measuring the impact of ORM

V. Measuring the impact of ORM
Scenario :
AMA approach, and target RAROC of 18%.
Board Objective : ORM should reduce EL by 15%.
Minus x % in the number of events in Business Line “ i ”, for the event types “ j,k,l ”. Dashboard : Systematic reduction of events in BL “ i ”, event types “ j,k,l ” Minus x % in the number of events in BL “ i ”, minus y % in the severity of losses for event types: “Internal fraud” and “Processing errors”. Audit tracking : Application of audit recommendations in BL “ i ” Minus x % in frequency and minus y % in severity for event types “Clients, products and business practices”, Business line reorganization : New product review process for all BL Cut off the x top losses, all Business Lines Lessons learned : Analysis of largest losses in Business Line (BL) “ i ” Impact on the distributions Risk Management Action

V. Measuring the impact of ORM -22% -20% - - -18% - -19% - -15% -15% -14% -15% -11% -18% -15% -37% Expected Loss -14% -22% - - -10% - -9% - -10% -12% -5% -10% -3% -10% -11% -9% Unexpected Loss -10% -10% - - -4% - -4% - - - - - - - - - Severity -12% -22% - - -9% - -7% - -9% -12% -2% -10% -2% -10% -11% -8% Reg. Capital (by cell) -12% -12% - - -13% - -15% - -14% -15% -14% -15% - - - - Frequency -15.1% - -7.0% -4.1% -9.7% -5.9% -3.9% -9.6% Reg. Capital (by BL) BL Reorganization Audit Tracking Dashboards Lessons Learned -2 (2,2) -2 (2,1) -2 (1,2) -6.1% -2 (1,1) - (2,2) - (2,1) - (1,2) -8.2% - (1,1) - (2,2) - (2,1) - (1,2) -5.8% - (1,1) - (2,1) Reg. Capital (total) Number Induced changes -9.1% - (1,1) - - (2,2) (1,2)

V. Measuring the impact of ORM 0.75% 0.43% 0,51% 0.28% - BL2 – Retail Banking 243,922 140,041 165,387 91,112 - BL2 – Retail Banking 243,922 202,704 232,937 189,114 - TOTAL BL Reorganization Audit Tracking Dashboards Lessons Learned Default AMA Maximum acceptable cost (in % of total income) - 0.36% 0,39% 0.56% - BL1 – Asset Management/Private Banking 0.49% 0.41% 0,47% 0.38% - TOTAL - 62,663 67,550 98,003 - BL1 – Asset Management/Private Banking BL Reorganization Audit Tracking Dashboards Lessons Learned Default AMA Maximum acceptable cost (in currency units) 27.49% 26.55% 27.24% 26.36% 25.54% TOTAL 31.02% 27.37% 28.32% 25.94% 27.11% BL2 – Retail Banking 25.23% Audit Tracking 25.54% Dashboards 27.11% Lessons Learned 22.57% Default AMA 22.57% BL Reorganization BL1 – Asset Management/Private Banking Operational RAROC

VI. Conclusion
ORM Goals – at board level :
Decrease the likelihood of a catastrophic event
Cost - benefit analysis of controls
Compliance with regulatory requirements
Lower economic & regulatory capital
ORM Goals and Ways – at business unit level :
Consolidated incident reporting
Involvement of line management
Active Management of operational risks
Set-up and use of dashboards
Implementation of RCSA

Market Risk and Basel II It is the risk that the value of on and off-balance sheet positions of a financial institution will be adversely affected by movements in market rates or prices such as interest rates , foreign exchange rates , equity prices , credit spreads and/or commodity prices resulting in a loss to earnings and capital.

Types of financial risk Financial Risks Liquidity Risk Operational Risk Regulatory Risk Human Factor Risk Market Risk Equity Risk Interest Rate Risk Currency Risk Commodity Risk Trading Risk Gap Risk Credit Risk Portfolio Concentration Risk Transaction Risk Counterparty Risk Issuer Risk

Market Risk under Basel II
Standardized Approach
Building Block Approach : Capital charge captured separately for each risk and then summed. Trading book used for general and specific risk in interest and equities markets. Both trading and banking books are used for general risk in currency and commodities markets.
Internal Model
VAR modeling: On daily basis and 99th percentile one-tailed confidence interval is to be used, 10days holding period.

Convergence of Economies
Easy and faster flow of information
Skill Enhancement
Increasing Market activity
Why the focus on Market Risk Management ? Leading to
Increased Volatility
Need for measuring and managing Market Risks
Regulatory focus
Profiting from Risk

Value-at-Risk Value-at-Risk is a measure of Market Risk, which measures the maximum loss in the market value of a portfolio with a given confidence VaR is denominated in units of a currency or as a percentage of portfolio holdings For e.g.., a set of portfolio having a current value of say Rs.100,000- can be described to have a daily value at risk of Rs. 5000- at a 99% confidence level, which means there is a 1/100 chance of the loss exceeding Rs. 5000/- considering no great paradigm shifts in the underlying factors. It is a probability of occurrence and hence is a statistical measure of risk exposure Measure, Monitor & Manage – Value at Risk

Variance- covariance Matrix Multiple Portfolios Yields Duration Incremental VaR Stop Loss Portfolio Optimization VaR Features of RMD VaR Model Facility of multiple methods and portfolios in single model Return Analysis for aiding in trade-off For Identifying and isolating Risky and safe securities For picking up securities which gel well in the portfolio For aiding in cutting losses during volatile periods Helps in optimizing portfolio in the given set of constraints

Value at Risk-VAR
Value at risk (VAR) is a probabilistic method of measuring the potentional loss in portfolio value over a given time period and confidence level.
The VAR measure used by regulators for market risk is the loss on the trading book that can be expected to occur over a 10-day period 1% of the time
The value at risk is $1 million means that the bank is 99% confident that there will not be a loss greater than $1 million over the next 10 days.

Value at Risk-VAR
VAR (x%) = Z x% σ
VAR(x%)=the x% probability value at risk
Z x% = the critical Z-value
σ = the standard deviation of daily return's on a percentage basis
VAR (x%) dollar basis =
VAR (x%) decimal basis X asset value

Example: Percentage and dollar VAR
If the asset has a daily standard deviation of returns equal to 1.4 percent and the asset has a current value of $5.3 million calculate the VAR(5%) on both a percentage and dollar basis .
Critical Z-value for a VAR(5%)= -1.65, VAR(10%)=-1.28, VAR(1%)=-2.32
VAR(5%) = -1.65( σ ) = -1.65(.014) = -2.31%
VAR (x%)dollar basis= VAR (x%) decimal basis X asset value
VAR (x%)dollar basis= -.0231X5,300,000 = $-122,430
Interpretation:
there is a 5% probability that on any given day, the loss in value on this particular asset will equal or exceed 2.31% or $122,430

Time conversions for VAR
VAR(x%)= VAR(x%) 1-day √J
Daily VAR: 1 day
Weekly VAR: 5 days
Monthly VAR: 20 days
Semiannual VAR: 125 days
Annual VAR: 250 days

Converting daily VAR to other time bases:
Assume that a risk manager has calculated the daily VAR(10%) dollar basis of a particular assets to be $12,500.
VAR(10%) 5-days(weekly) = 12,500 √5= 27,951
VAR(10%) 20-days(monthy) = 12,500 √20= 55,902
VAR(10%) 125-days = 12,500 √125= 139,754
VAR(10%) 250-days = 12,500 √250= 197,642

Understanding of Asset & Liability Management (ALCO)
The process of making decision about the composition of assets and liabilities and their risk assessment is known as asset /liability management.
The decisions are usually made by the asset/liability management committee (ALCO) that is responsible for the overall financial direction of the bank.

Classification of Assets and Liabilities
Rate-sensitive assets (RSAs)
Rate-sensitive liabilities (RSLs)
Those assets and Liabilities whose interest return or costs vary with interest rate changes over given time horizon referred to as rate sensitive assets/liabilities.
Non rate-sensitive (NRS)
Those assets and Liabilities whose interest return or costs do not vary with interest rate movement over the same time horizon referred to as non-rate sensitive

Gap and Relative Ratio
Gap= RSA – RSL
Relative gap ratio= Gap/Total assets
Interest–sensitivity ratio=RSA/RSL

Interest–sensitivity
A financial institution at given time may be asset or liability sensitive.
Asset sensitive e.g. RSA(100B)-RSL(50B)
Positive gap or Interest-sensitivity ratio > 1
Bank will experience an increase in their net interest income when interest rate increase and a decrease in their net interest income when interest rate fall.
Liability sensitive e.g. RSA(50B)-RSL(100B)
Negative gap or Interest-sensitivity ratio < 1
Bank will experience an decrease in their net interest income when interest rate increase and a increase in their net interest income when interest rate fall.

Gap, Interest Rate Changes, and Net Interest Income No Change Decrease RSA=RSL Zero No Change Increase RSA=RSL Zero Increase Decrease RSA<RSL Negative Decrease Increase RSA<RSL Negative Decrease Decrease RSA>RSL Positive Increase Increase RSA>RSL Positive Change in Net Interest Income Change in Interest Rates Gap

Managing Interest Rate Risk Rs/$ Gap
Aggressive asset/liability management
The aggregative asset/liability management focuses on increasing the net interest income through altering the portfolio of the institution.
Defensive asset/liability management
The goal of defensive asset/liability management is to insulate the net interest income from changes in interest rate

Duration Gap Analysis
The duration gap is the difference between the duration of a bank’s assets and liabilities.
It is a measure of interest rate sensitivity that helps to explain how changes in interest rate affect the market value of a bank’s assets and liabilities, and, in turn, its net worth.
NW=A-L
∆ NW= ∆A- ∆L

Measurement of the Duration Gap
Balance Sheet Duration
Assets Rs Duration (Yrs) Liabilities Rs Duration (Yrs)
Cash 100 0.00 Deposit 1 Yr 600 1.00
Loans 400 1.25 Deposit 5 Yr 300 5.00
T. Liabilities 900 2.33
Mort 500 7.00 Equity 100
Loans 1,000 4.00 1,000 DGAP (duration gap)=D a -WD L
DGAP (duration gap)=4.0 – (0.9)(2.33) = 4.00-2.10= 1.90 Years
D a = Average duration of assets
D L =Average duration of liabilities
W=Ratio of total liabilities to total assets
Suppose that current interest rate are 11% and are expected to increase by 100 basis points(1%)
%age change in the Net Worth=% ∆Net Worth= (-1.90)(1/1.11) = -1.7%
Amt change in the Net Worth= ∆Net Worth= (-1.90)(1/1.11) x TA= -1.7%X1000= -17

Duration Gap, Interest Rate and Changes in Net Worth No Change Decrease Zero No Change Increase Zero Decrease Decrease Negative Increase Increase Negative Increase Decrease Positive Decrease Increase Positive Change in Net Worth Change in interest Rate Duration Gap

History

Credit Risk Management

Credit Risk Credit risk refers to the risk that a counter party or borrower may default on contractual obligations or agreements

Standardized Approach (Credit Risk)
The Banks are required to use rating from External Credit Rating Agencies (ECAIS ). (Long Term)
100% Unrated Unrated Unrated Unrated 150% CCC+ and below CCC+ and below 7 6 150% 100% 100% 50% 20% Risk Weight (Corporate) B+ B B- B+ B B- 5,6 5 BB+ BB BB- BB+ BB BB- 4 4 BBB+ BBB BBB- BBB+ BBB BBB- 3 3 A+ A A- A+ A A- 2 2 AAA AA+ AA AA- AAA AA+ AA AA- 0,1 1 JCR-VIS PACRA ECA Scores SBP Rating Grade

Short-Term Rating Grade Mapping and Risk Weight 4 3 2 1 External grade (short term claim on banks and corporate) 150% Other Other S4 100% A-3 A-3 S3 50% A-2 A-2 S2 20% A-1 A-1 S1 Risk Weight JCR-VIS PACRA SBP Rating Grade

Methodology Calculate the Risk Weighted Assets
Solicited Rating
Unsolicited Rating
Banks may use unsolicited ratings (if solicited rating is not available) based on the policy approved by the BOD.

Short-Term Rating
Short term rating may only be used for short term claim.
Short term issue specific rating cannot be used to risk-weight any other claim.
e.g. If there are two short term claims on the same counterparty.
Claim-1 is rated as S2
Claim-2 is unrated
100% 50% Risk -weight Claim-2 unrated Claim-1 rated as S2

Short-Term Rating (Continue)
e.g. If there are two short term claims on the same counterparty.
Claim-1 is rated as S4
Claim-2 is unrated
150% 150% Risk -weight Claim-2 unrated Claim-1 rated as S4

Ratings and ECAIs
Rating Disclosure
Banks must disclose the ECAI it is using for each type of claim.
Banks are not allowed to “cherry pick” the assessments provided by different ECAIs

Basel I v/s Basel II
Basel: No Risk Differentiation
Capital Adequacy Ratio = Regulatory Capital / RWAs (Credit + Market)
8 % = Regulatory Capital / RWAs
RWAs (Credit Risk) = Risk Weight * Total Credit Outstanding Amount
RWAs = 100 % * 100 M = 100 M
8 % = Regulatory Capital / 100 M
Basel II: Risk Sensitive Framework
RWA (PSO) = Risk Weight * Total Outstanding Amount
= 20 % * 10 M = 2 M
RWA (ABC Textile) = 100 % * 10 M = 10 M
Total RWAs = 2 M + 10 M =12 M

Credit Risk Mitigation (CRM)
Where a transaction is secured by eligible collateral.
Meets the eligibility criteria and Minimum requirements.
Banks are allowed to reduce their exposure under that particular transaction by taking into account the risk mitigating effect of the collateral.

Adjustment for Collateral:
There are two approaches :
Simple Approach
Comprehensive Approach

Simple Approach (S.A)
Under the S. A. the risk weight of the counterparty is replaced by the risk weight of the collateral for the part of the exposure covered by the collateral.
For the exposure not covered by the collateral, the risk weight of the counterparty is used.
Collateral must be revalued at least every six months.
Collateral must be pledged for at least the life of the exposure.

Comprehensive Approach (C.A)
Under the comprehensive approach, banks adjust the size of their exposure upward to allow for possible increases.
And adjust the value of collateral downwards to allow for possible decreases in the value of the collateral.
A new exposure equal to the excess of the adjusted exposure over the adjusted value of the collateral.
counterparty's risk weight is applied to the new exposure.

e.g. Suppose that an Rs 80 M exposure to a particular counterparty is secured by collateral worth Rs 70 M. The collateral consists of bonds issued by an A-rated company. The counterparty has a rating of B+. The risk weight for the counterparty is 150% and the risk weight for the collateral is 50%.
The risk-weighted assets applicable to the exposure using the simple approach is therefore:
0.5 X 70 + 1.50 X 10 = 50 million
Risk-adjusted assets = 50 M
Comprehensive Approach: Assume that the adjustment to exposure to allow for possible future increases in the exposure is +10% and the adjustment to the collateral to allow for possible future decreases in its value is -15%. The new exposure is:
1.1 X 80 -0.85 X 70 = 28.5 million
A risk weight of 150% is applied to this exposure :
Risk-adjusted assets = 28.5 X 1.5 =42.75 M

Credit risk Basel II approaches to Credit Risk Standardised Approach Foundation Advanced Internal Ratings Based (IRB) Approaches Evolutionary approaches to measuring Credit Risk under Basel II
RWA based on externally provided:
Probability of Default (PD)
Exposure At Default (EAD)
Loss Given Default (LGD)
RWA based on internal models for:
RWA based on externally provided:
RWA based on internal models for
Limited recognition of credit risk mitigation & supervisory treatment of collateral and guarantees
Limited recognition of credit risk mitigation & supervisory treatment of collateral and guarantees
Internal estimation of parameters for credit risk mitigation – guarantees, collateral, credit derivatives
Basel II provides a ‘tailored’ or ‘evolutionary’ approach to banks that is sensitive to their credit risk profiles Increasing complexity and data requirement Decreasing regulatory capital requirement

Credit Risk – Linkages to Credit Process Transaction Credit Risk Attributes Exposure at Default Loss Given Default Probability of Default Exposure Term Economic loss or severity of loss in the event of default Likelihood of borrower default over the time horizon Expected amount of loan when default occurs Expected tenor based on pre-payment, amortization, etc. CREDIT POLICY RISK RATING / UNDERWRITING COLLATERAL / WORKOUT LIMIT POLICY / MANAGEMENT MATURITY GUIDELINES INDUSTRY / REGION LIMITS BORROWER LENDING LIMITS Portfolio Credit Risk Attributes Relationship to other assets within the portfolio Exposure size relative to the portfolio Default Correlation Relative Concentration

The causes of credit risk
The underlying causes of the credit risk include the performance health of counterparties or borrowers.
Unanticipated changes in economic fundamentals.
Changes in regulatory measures
Changes in fiscal and monetary policies and in political conditions.

Risk Management
Risk Management activities are taking place simultaneously
RM performed by Senior management and Board of Directors Middle management or unit devoted to risk reviews On-line risk performed by individual who on behalf of bank take calculated risk and manages it at their best, eg front office or loan originators. Strategic Macro Micro Level

Best Practices for Credit Risk Management 1. Rethinking the credit process 2. Deploy Best Practices framework 3. Design Credit Risk Assessment Process 4. Architecture for Internal Rating 5. Measure, Monitor & Manage Portfolio Credit Risk 6. Scientific approach for Loan pricing 7. Adopt RAROC as a common language 8. Explore quantitative models for default prediction 9. Use Hedging techniques 10. Create Credit culture

Increased reliance on objective risk assessment
Align “Risk strategy” & “Business Strategy”
Credit process differentiated on the basis of risk, not size
Investment in workflow automation / back-end processes
Active Credit Portfolio Management
1. Rethinking the credit process

Credit & Credit Risk Policies should be comprehensive
Set Limits On Different Parameters
Credit organisation - Independent set of people for Credit function & Risk function / Credit function & Client Relations
Ability to Calculate a Probability of Default based on the Internal Score assigned
Separate Internal Models for each borrower category and mapping of scales to a common scale
2. Deploy Best Practices framework

RMD provides well structured “ready to use” “value statements” to fairly capture and mirror the Rating officer’s risk assessment under each specific risk factor as part of the Internal Rating Model 3. Design Credit Risk Assessment Process Credit Risk Industry Risk Business Risk Management Risk Financial Risk Industry Characteristics Industry Financials Market Position Operating Efficiency Track Record Credibility Payment Record Others Existing Fin. Position Future Financial Position Financial Flexibility Accounting Quality
External factors
Scored centrally once in a year
Internal factors
Scored for each borrowing entity by the concerned credit officer

Credit Rating System consists of all of the methods, processes, controls and data collection and IT systems that support the assessment of credit risk, the assignment of internal risk ratings and the quantification of default and loss estimates. The New Basle Capital Accord
Appropriate rating system for each asset class
Multiple methodologies allowed within each asset class (large corporate , SME)
Each borrower must be assigned a rating
Two dimensional rating system
Risk of borrower default
Transaction specific factors (For banks using advanced approach, facility rating must exclusively reflect LGD)
Minimum of nine borrower grades for non-defaulted borrowers and three for those that have defaulted
CORPORATE/ BANK/ SOVEREIGN EXPOSURES
Each retail exposure must be assigned to a particular pool
The pools should provide for meaningful
differentiation of risk, grouping of sufficiently homogenous exposures and allow for accurate and consistent estimation of loss characteristics at pool level
RETAIL EXPOSURES 4. Architecture for Internal Rating

ONE DIMENSIONAL R RMD’s modified TWO DIMENSIONAL approach Rating reflects Expected Loss CONCEPTUALLY SOUND INTERNAL RATING MODEL – CAPTURES PD, LGD SEPARATELY Differs from the two dimensional system portrayed above in that it records LGD rather than EL as the second grade. The benefit of this approach is that rater’s LGD judgment can be evaluated and refined over time by comparing them to loss experience. The Facility grade explicitly measures LGD. The rater would assign a facility to one of several LGD grades based on the likely recovery rates associated with various types of collateral, guarantees or other factors of the facility structure. 4. Architecture for Internal Rating…contd.

What is a Rating System?
A rating system is one by which borrowers/facilities are systematically assigned to (grouped into) rating grades according to the credit risk characteristics ( rating criteria or risk factors ) of the borrowers/facilities
Rating grades 1 6 5 4 3 2 7 defaul t

Types of Rating System: Hybrid Rating System
Rating systems that uses both expert judgements and statistical modelling techniques - the most commonly-used rating systems in industry
Classic expert judgement-based system Pure model-based system Expert judgement-based system with quantitative guidelines Model-based system with judgemental overrides Expert-derived models Constrained judgement Spectrum of Rating Systems Hybrid system - the most commonly-used in the industry

Types of Rating System: An Example RISK FACTORS SCORE RELATIVE IMPORTANCE Subjective factors 1. Management 32% Strong 100 Weak 0 2. Entry barrier 25% High 100 Low 0 Objective factors 3. Gearing 34.5% <=50% 100 > 50% 0 4. Earnings growth 8.5% >= 10% 100 < 10% 0 Risk factors & scores determined by judgements Relative importance determined by models

Types of Rating System: An Example
The range of scores would lie between “0” (i.e. weak management, low entry barrier, gearing >50% and earnings growth <10%) to “100” (i.e. strong management, high entry barrier, gearing <=50% and earnings growth >=10%)
Assume the Bank maps score ranges to rating grades :
e.g. if a borrower has a strong management, the industry has low entry barrier, the gearing is 80%, and earnings growth is 30%, then it would have credit score: 100  32% + 0  25% + 0  34.5% + 100  8.5% = 40.5 and the borrower would be assigned to rating grade 5
Rating grades 6 5 4 3 2 7 (95,100] (70,95] (60,70] (50,60] (40,50] (20,40] [0,20] 1 Score ranges

FIRB Approach for corporate, bank & sovereign exposures:
Bank estimates PD for each borrower rating
LGD, EAD and M are prescribed by the SBP
(supervisory estimates)
AIRB Approach for corporate, bank & sovereign exposures:
Bank estimates PD for each borrower rating
it also estimates LGD for each facility rating
it also estimates EAD for each facility type
it also calculates M according to rules prescribed by the SBP
For retail exposures:
Bank estimates PD, LGD and EAD for each pool
Quantification of a Rating System

For FIRB or AIRB Approach for Corporate/Commercial/SME, bank/FI & sovereign exposures , 3 methods can be used to estimate the PD of a borrower rating
1. Internal default experience
2. Mapping to external data
3. Statistical default models
Quantification of a Rating System: PD of Corporate/Commercial/SME, Bank/FI & Sovereign Exposures

1. Internal default experience:
e.g. in the past 5 years, annual default rates of borrowers assigned to rating grade 4 were 10%, 12%, 9%, 8% and 11% respectively. PD of rating grade 4 for this year can be estimated as the simple average of these default rates, i.e.:
(10% + 12% + 9% + 8% + 11%)  5 = 10%

2. Mapping to external data :
e.g. By comparing the rating criteria of its internal rating system with those of the Moody’s, Bank concludes that 50% of the borrowers assigned to its rating grade 2 would have Moody’s ratings “Baa1”, 25% “A3” and 25% “Ba1”. In the past 5 years, average annual default rates of these Moody’s ratings were 3%, 2% and 4% respectively. The Bank’s rating grade 2 can be estimated as:
50%  3% + 25%  2% + 25%  4% = 3%
There are many types of mapping methodologies

3. Statistical default models :
e.g. Bank uses a model-based rating system , under which PD is estimated for each borrower. There are 3 borrowers assigned to rating grade 3, with PD estimated to be 4.5%, 5% and 5.5% respectively by the model. PD of rating grade 3 can be estimated as the simple average of the individual PDs of these borrowers, i.e.:
(4.5% + 5% + 5.5%)  3 = 5%
5% will be used for all the 3 borrowers for CAR purpose, regardless of the individual PDs generated from the model
Quantification of a Rating System: PD of Corporate/Commercial/SME, Bank & Sovereign Exposures

What is Validation?
Basel definition: “encompasses a range of processes and activities that contribute to an assessment of whether ratings adequately differentiate risk, and whether estimates of risk components appropriately characterise the relevant aspects of risk”
Bank’s responsibility to demonstrate its rating system meets minimum requirements
Review of a Bank’s validation process a major part of the IRB recognition process

Six Principles of the Validation
Six Principles of the Validation of the Basel Accord Implementation
(i) Validation is fundamentally about assessing the predictive ability of a bank’s risk estimates and the use of ratings in credit processes
(ii) The bank has primary responsibility for validation
(iii) Validation is an iterative process
(iv) There is no single validation method
(v) Validation should encompass both quantitative and qualitative elements
(vi) Validation processes and outcomes should be subject to independent review

Basel Approach to Validation (1)
Closely aligned with the 6 principles
Bank conducts its own internal validation of the rating system, estimates of risk components & the risk ratings generation processes
Internal validation clearly documented & shared with Regulator
Individuals involved in validation must have necessary skills & knowledge and independence
No universal validation tool

No industry “best practice” standard on validation
Quantitative techniques very diverse, portfolio specific, and still evolving
Setting prescriptive quantitative standards & benchmarks for IRB systems could stifle innovation
Principles-based approaches by other supervisors
Views of external consultants & industry experts

Qualitative and Quantitative elements.
Qual. - processes, procedures & controls
Corporate governance & oversight, independence, transparency, accountability, use of internal ratings, internal & external audit, use of external vendor models
Quant. - generally accepted techniques
Data quality, accuracy of PDs, LGDs & EADs, model logic & conceptual soundness, estimation & validation techniques, back-testing, benchmarking

Corporate Governance & Oversight
Board & senior management involvement
Understanding of Basel /SBP requirements
Understanding & approval of key aspects of IRB system
Ensures adequate resources and clearly defines responsibilities
Ensures adequate training
Integrates IRB systems with policies, procedures, systems, controls
Tracks differences between policies & actual practice (e.g. exceptions/overrides)
Quarterly MIS on rating system performance & regular internal review
Receives regular reports on internal ratings

Independent Rating Approval Process
General rule that approval of ratings & transactions should be separate from sales & marketing
Independent & separate functional reporting lines for rating “assignors” & rating “approvers” (e.g. credit officers, with well-defined performance measures)
Where ratings are assigned & approved within sales & marketing
mitigate the inherent conflict of interest with compensating controls (e.g. limited credit limits, independent post-approval review of ratings, more frequent internal audit coverage)
Where rating assignment or approval process is automated, verify accuracy & completeness of data inputs

Annual Review
Reviews conducted internally or by external experts
Functional independence
Should encompass all aspects of the process generating the risk estimates & usage
Compliance with established policies & procedures
Quantification process & accuracy of risk component estimates
Model development, use & validation
Adequacy of data systems & controls
Adequacy of staff skills & experience
Identify weakness, make recommendations & take corrective actions
Significant findings reported to senior management & the Board
Independent Review of IRB System & Risk Quantification

Transparency & Accountability
Transparency
Enable third parties to understand the design, operations & accuracy of a rating system & to evaluate whether it is performing as intended
An ongoing requirement: update documentation when there are changes
Achieved through documentation
Expert judgement-based vs. Model-based rating system
Accountability
Identify individuals or parties responsible for rating accuracy & rating system performance
Inventory of models & accountability chart of roles of parties
Establish performance standards
Senior individual to take responsibility for overall performance

The IRS & risk estimates should have substantial influence on decision-making & actions:
Credit approval & pricing,, individual & portfolio limit setting
Portfolio monitoring & determining provisioning
Analysis & reporting of credit risk information
Modelling & management of economic capital
Assessment of total credit risk capital requirements
Formulating business strategies & assessment of risk appetite
Assessment of profitability & performance, and determining performance-related remuneration
Other aspects (e.g. Banks’ infrastructure such as IT, skills & resources and organisational structure)
Use of Internal Ratings

Data Quality
Accuracy, completeness & appropriateness
Data architecture Storage, retrieval & deletion Data processing Data collection IT infrastructure Reconciliation IRB data A/C data External & pooled data Use of statistical techniques Staff competency Management oversight & control Data quality assessment programme & internal audit

Quantitative Requirements
Accuracy of PD, LGD, EAD
Discriminatory power and calibration
Benchmarking
Stress testing

Validation of a Rating System: Back-testing
Back-testing is the direct comparison between the risk component estimates with the realised figures , e.g. PD against default rate of a borrower grade (or pool for retail)
In practice, estimates will never be exactly the same as realised figures. The question is whether the deviation is acceptable, especially when the estimates are smaller than the realised figures (i.e. underestimation )
In general, statistical hypothesis testing can be applied:
Null hypothesis (H0): The estimate of the risk component is correct
Alternative hypothesis (H1): The risk component is underestimated
To use the hypothesis testing technique, a confidence level needs to be set and a probability distribution of the risk component needs to be defined.

Validation of a Rating System: Benchmarking
Benchmarking is the comparison of a Bank’s risk component estimates with those of a third party such as estimates by rating agencies
For PD, external benchmarks are generally most useful where backtesting is difficult
For LGD and EAD, as well as PD of small-sized borrowers (e.g. individuals and SMEs), external benchmarks may not be available
LGD and EAD depend heavily on individual Banks’ recovery and credit monitoring policies , and therefore it is possible for there to be big differences of internal estimates from the benchmarks, even for the same type of facilities

Validation of a Rating System: Stability Analysis
Even if a rating system performs well under certain situations or for certain types of borrowers/facilities, it may not do so in other situations or with other types of borrowers/facilities
Stability analysis examines whether a rating system and/or the risk component estimates remain valid under different situations or for different types of borrowers/facilities. It involves asking questions like:
Would the back-testing results remain satisfactory during economic boom as well as recession?
How would distribution of borrowers/facilities amongst rating grades and estimates of risk components change if certain assumptions are modified (e.g. discount rates in workout LGD)?
What would be the risk component estimates if only a sub-sample of data are used in quantification?

Validation of a Rating System: Discriminatory Power
Discriminatory power is about the “rank order” of borrowers. It assesses the ability of a rating system to differentiate “bad” borrowers (i.e. those going to default) from “good” borrowers (i.e. those not going to default).
Many quantitative techniques can be used to assess discriminatory power :
Accuracy Ratio
Receiver Operating Characteristic Measure
Pietra Index
Bayesian Error Rate
Conditional Information Entropy Ratio
Information Value
Brier Score
Divergence

Generally speaking, all these techniques are to measure the difference between the distribution of the “good” borrowers and that of the “bad” borrowers in relation to risk characteristics, e.g. credit scores, rating grades, income
Validation of a Rating System: Discriminatory Power Frequency Rating score “ Bad” borrowers “ Good” borrowers

Validation of a Rating System: Discriminatory Power
For a perfect rating system, the distribution of “bad” borrowers would not overlap with that of “good” borrowers
Discriminatory power analysis can be applied to borrower ratings of corporate, bank and sovereign exposures
For retail exposures, discriminatory power can be assessed for individual rating criteria that are used in segmentation
As with back-testing, it is difficult to set a “passing mark” for a rating system’s discriminatory power

‘ CREDIT CAPITAL’ The portfolio approach to credit risk management integrates the key credit risk components of assets on a portfolio basis, thus facilitating better understanding of the portfolio credit risk. The insight gained from this can be extremely beneficial both for proactive credit portfolio management and credit-related decision making. 1. I t is based on a rating (internal rating of banks/ external ratings) based methodology. 2. Being based on a loss distribution (CVaR) approach, it easily forms a part of the Integrated risk management framework. 5. Measure, Monitor & Manage Portfolio Credit Risk

PORTFOLIO CREDIT VaR Expected (EL) Priced into the product (risk-based pricing) Unexpected (UL) Covered by capital reserves (economic capital) Probability Loss (L) Credit Capital models the loss to the value of the portfolio due to changes in credit quality over a time frame

ARE CORRELATIONS IMPORTANT 99.99% 99.67% 99.35% 99.03% 98.71% 98.39% 98.07% 97.75% 97.43% 97.11% 96.79% 96.47% 96.15% 95.83% 95.51% 95.19% Correlation Probability of Default Confidence level Large impact of correlations RELATIVE CONTRIBUTION OF CORRELATIONS AND PROBABILITY OF DEFAULT IN CREDIT VaR CREDIT VaR Source: S&P 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Corr(X,Y)=ρ xy =Cov(X,Y)/std(X)std(Y) 2.07 1.99 4.33 2.21 1.48 0.22 1.67 1.30 1.78 0.67 0.05 1.31 2.39 Utility 1.99 3.85 5.63 4.78 3.40 1.08 2.82 2.61 3.29 1.03 -0.28 2.56 3.56 Trans 4.33 5.63 16.72 -1.14 3.51 0.50 4.63 5.21 3.85 -0.04 -0.44 2.49 7.04 Telecom 2.21 4.78 -1.14 13.15 9.39 0.46 3.83 0.91 7.32 6.27 -0.20 6.03 2.40 Real Est. 1.48 3.40 3.51 9.39 4.07 0.10 2.45 2.12 3.64 1.88 -1.63 2.01 2.06 Leisure 0.22 1.08 0.50 0.46 0.10 96.00 0.47 0.41 0.41 -0.03 0.75 0.52 0.67 Insur 1.67 2.82 4.63 3.83 2.45 0.47 3.01 2.34 2.78 0.73 0.75 1.69 3.11 High tech 1.30 2.61 5.21 0.91 2.12 0.41 2.34 3.50 2.09 0.52 0.94 1.60 3.65 Chem 1.78 3.29 3.85 7.32 3.64 0.41 2.78 2.09 3.81 1.54 -0.49 2.36 2.68 Build 0.67 1.03 -0.04 6.27 1.88 -0.03 0.73 0.52 1.54 1.39 -0.50 0.83 0.67 Finan 0.05 -0.28 -0.44 0.20 -1.63 0.75 0.75 0.94 -0.49 -0.50 4.74 -1.41 1.57 Energ 1.31 2.56 2.49 6.03 2.01 0.52 1.69 1.60 2.36 0.83 -1.41 2.51 1.84 Cons 2.39 3.56 7.04 2.40 2.06 0.67 3.11 3.65 2.68 0.67 1.57 1.84 4.81 Auto Utility Trans Tele R.E. Leisure Insur Hi tech Chem Build Finan Energ Cons Auto 3-Year Default Correlations

RMD’s approach ‘CREDIT CAPITAL’ Overall Architecture STEP 1 From the historical correlation data of industries, the firm-to-firm correlations are found. STEP 2 Calculate asset value thresholds for entire transition matrix. This is done assuming that given current rating, the asset values have to move up/down by certain amounts (which can be read off a Standard Normal distribution) for it to be upgraded /downgraded. Step 3 Large no. of Simulations (Monte Carlo) of the asset value thresholds preserving the correlation structure using Cholesky Decomposition is carried out. Asset value thresholds are converted to simulated ratings for the portfolio for each of the simulation runs. STEP 4 Using the forward yield curve (rating wise) and recovery data suitable valuation of each of the instruments in the portfolio is done for each simulation run. The distribution of portfolio values is subtracted from the original value to generate the loss distribution. Average variability explained by each industry Industry Correlation Step 1 Tenor of Evaluation, Current Rating Correlations Transition rates Step 2 Return Thresholds Simulated Credit Scenarios Step 3 Monte Carlo simulation Migration Portfolio Loss Distribution Spot & Forward Curve for each grade Recovery Rates Valuation Step 4 Exposure Default

Credit Risk - Raroc

What is RAROC ? The concept of RAROC (Risk adjusted Return on Capital) is at the heart of Integrated Risk Management. 7. Adopt RAROC as a common language
Revenues
Expenses
Expected Losses
+ Return on economic capital
+ transfer values / prices
Capital required for
Credit Risk
Market Risk
Operational Risk
Risk Adjusted Return Risk Adjusted Capital or Economic Capital RAROC

Will the loan default? What will be the exposure at default ? How much will be recovered ? no yes Risk of Default Risk of Exposure Risk of Recovery The 3 components of Credit Risk Loss = 0 Average (expected) Loss
Country risk
Quality of the USR
Maturity
Transfer Risk (per product)
Commitment level
Current Exposure
Unused part of the line
Product Loan Equivalent (LEF)
Available pledges on assets
Recovery on unsecured assets

R isk A djusted R eturn O n C apital
RAROC = Revenues - Expected Loss
Economic capital
with : EL = EDF . EAD . Severity
EDF = expected default frequency (depending on the risk class / on the ration)
EAD = exposure at default
= outstanding . LEF . (1 - pledges)
LGD = Severity = loss given default (depending on the type of counterparty)
RAROC - Definition & Hypotheses

EL = expected losses, very likely, based on historical data
RAROC calculated per transaction / client / group of clients
Revenues :
Credit
Non Credit
RAREV = R isk A djusted Rev enues = Revenues - EL
Minimum required for a non-destroying value loan :
RAREV > 0
-> impact on credit pricing
RAROC - Definition & Hypotheses

Economic capital : own funds needed to cover the unexpected losses of a transaction, as they are assessed by the banking institution. Economic Capital =  (EDF) .  . 6,3 . LGD . (1-tax) . EAD where :  (EDF) = (edf. (1-edf)) 1/2  = default correlation between assets of the same risk class 6,3 = stress factor for a confidence interval of 99.95% (1-tax) = accounting for fiscal deductibility of losses RAROC - Definition & Hypotheses

RAROC = (12 %) x 65 % + (RFR + 0,5%) x 35 % = 14.9% = RFR + 10% (1 - 40%)
ROE = 12 %
RFR = 5 %
Tier I / Tier II = 65% / 35 %
cost of Tier II : RFR + 0,5%
taxation rate = 40 %
The transaction revenues need to be sufficient to cover the funding costs, ie., to remunerate the economic capital properly. RAROC - Hurdle Rate

Raroc - Leverages
Profitability
Credit Margin
Non Credit revenues
Risk
Risk class
Credit type
Maturity
Country of credit
Recovery rate
Pledges

Investment loan : 1 MM EUR
Bullet repayment
Credit margin : 2%
Pledges : 400,000 eur
No other recoveries
EDF : 1,5%
Maturity : 1 an
EL = 1.5% . 60% . 1000 000 = 9 000 EUR
Eco K =  (EDF) .  . 6,3 . Severity . (1-tax) . EAD
= (1,5% . 98,5%) 1/2 . 2,7% . 6,32 . 60% . 60%. 1MM
= 74 670 EUR
RAROC = 20 000 - 9 000 = RFR + 14.7%
74 670
Raroc - Numerical Example

Quantification of the funding cost of the bank per transaction, per product and per client type.
Management tool of capital, a scarce and an expensive ressource.
Management by objectives of the banking network’s agents
Uses of Raroc

Alignement of regulatory and economic capital
Validation of internal models -> détermination of risk classes and of corresponding EDF’s.
Basle II RAROC
Measurement and management of economic capital
Key role of the EDF’s in Raroc:
Raroc = (Revenues - EL) / K
EL = EDF . EAD . Severity
K=  (EDF) .  .6,3. sev.(1-tax).EAD
Raroc in Basle II

Direct link between the risk class of a debtor and his capital requirement.
Fine-tuning of the capital requirement based on the bank’s activities and on its risk profile (pillar 2).
Basle II RAROC
Raroc capital seen as both regulatory and economic.
Raroc as a global management and optimisation tool for the bank’s activities.
Raroc in Basle II

RAROC 22% EVA 310 Risk-adjusted Net income 1750 Capital Charge 1440 Risk-adjusted After tax income 1.75% Average Lending assets 100 000 Total capital 8000 Cost of capital 18% Risk-adjusted Net income 2.20% Net Tax 0.45% Total capital 8.0 % Average Lending assets 100 000 Risk-adjusted income 5.60 % Costs 3.40 % Credit Risk Capital 4.40 % Market Risk Capital 1.60 % Operational Risk Capital 2.00 % Income 6.10 % Expected Loss 0.50 % RAROC Profitability Tree – an illustration

More than 10 years old, but still a flexible and efficient management tool in a modern regulatory environment.
Based on numerous parameters, it is adaptable to new banking products and activities.
Best suited for :
the assessment of the credit risk capital of a bank;
internal credit risk management;
external credit pricing tool .
Raroc - Summary

Corporate predictor Model is a quantitative model to predict default risk dynamically
Model is constructed by using the hybrid approach of combining Factor model & Structural model (market based measure)
The inputs used include: Financial ratios, default statistics, Capital Structure & Equity Prices.
The present coverage include listed & ECAIs rated companies
The product development work related to private firm model & portfolio management model is in process
The model is validated internally
.
Derivation of Asset value & volatility
Calculated from Equity Value , volatility for each company-year
Solving for firm Asset Value & Asset Volatility simultaneously from 2 eqns. relating it to equity value and volatility
Calculate Distance to Default
Calculate default point (Debt liabilities for given horizon value)
Simulate the asset value and Volatility at horizon
Calculate Default probability (EDF)
Relating distance to default to actual default experience
Use QRM & Transition Matrix
Calculate Default probability based on Financials
Arrive at a combined measure of Default using both
8. Explore quantitative models for default prediction

Interest Rate Risk Spread Risk Default Risk Credit Default Swap Credit Spread Swap Total Return Swap Basket Credit Swap Securi Securitization tization Credit Portfolio Risks Different Hedging Techniques . . . as we go along, the extensive use of credit derivatives would become imminent 9. Use Hedging techniques

Credit Risk: Loan Portfolio and Concentration Risk
The Portfolio and Individual Securities are prone to two Type of Risks.
1. Systematic Risk
2. Unsystematic Risk
The Unsystematic Risk can be eliminated with Diversification.

Models of Loan Concentration Risk
1. Migration Analysis.
Migration analysis uses a loan migration matrix (transition matrix), which provide probabilities that the credit quality of a loan will migrate from one quality class to another quality class over a period of time, usually one year.
2. Concentration Limits.
The concentration limit is the maximum permitted loan amount to that can be granted to an individual borrower in a given sector, expressed as percentage of capital:
3. Subjective Model.
e.g. We have already lent too much to this borrower.

Concentration Limits
Concentration Limit = Maximum loss as a percentage of capital X 1/Loss Rate
e.g. A bank wants to limit its losses in a particular sector to 5% of its capital and loss rate for this sector is 60%.
Concentration Limit = 0.05 X (1/0.6)
= 8.33%

INTERNAL EXPOSURE LIMIT PER PARTY 2% of tier-1 4:5 2.5% of tier-1 4:4 5% of tier-1 4:3 10% of tier-1 4:2 15% of tier-1 4:1 Risk Rated “4” 2.5% of tier-1 3:4 5% of tier-1 3:4 10% of tier-1 3:3 15% of tier-1 3:2 22% of tier-1 3:1 Risk Rated “3” 5% of tier-1 2:5 10% of tier-1 2:3 15% of tier-1 1:2 20% of tier-1 2:2 25% of tier-1 2:1 Risk Rated “2” 10% of tier-1 1:5 15% of tier-1 1:4 20% of tier-1 1:3 25% of tier-1 1:2 30% of tier-1 1:1 Risk Rated “1” Risk Rated “5” Risk Rated “4” Risk Rated “3” Risk Rated “2” Risk Rated “1” Risk Rating of the Industry

INTERNAL EXPOSURE LIMIT PER GROUP 2% of Tier -1 Capital 4:5 2.5% of Tier -1 Capital 4:4 5% of Tier -1 Capital 4:3 10% of Tier -1 Capital 4:2 20% of Tier -1 Capital 4:1 Risk Rated “4” 2.5% of Tier -1 Capital 3:5 5% of Tier -1 Capital 3:4 10% of Tier -1 Capital 3:3 20% of Tier -1 Capital 3:2 30% of Tier -1 Capital 3:1 Risk Rated “3” 5% of Tier -1 Capital 2:5 10% of Tier -1 Capital 2:4 20% of Tier -1 Capital 2:3 30% of Tier -1 Capital 2:2 45% of Tier -1 Capital 2:1 Risk Rated “2” 10% of Tier -1 Capital 1:5 20% of Tier -1 Capital 1:4 30% of Tier -1 Capital 1:3 45% of Tier -1 Capital 1:2 50% of Tier -1 Capital 1:1 Risk Rated “1” Risk Rating (Group) Risk Rating “5” Risk Rating “4” Risk Rating “3” Risk Rating “2” Risk Rating “1” Risk Rating Industry

Migration Analysis
Loan Migration Matrix
0.04 0.80 0.13 0.03 3 0.02 0.03 0.83 0.12 2 0.01 0.04 0.10 0.85 1 D 3 2 1 Risk Grade at End of Year Risk Grade at Beginning of Year

Sample Credit Rating Transition Matrix ( Probability of migrating to another rating within one year as a percentage) Credit Rating One year in the future 18.60 39.97 24.86 12.34 2.06 1.85 0.25 0.06 CCC 5.58 10.13 63.89 2.31 8.29 9.25 0.36 0.21 B 1.32 4.14 8.05 74.68 5.53 3.29 2.91 0.08 BB 0.07 0.16 0.32 6.51 84.21 5.00 1.89 1.84 BBB 0.03 0.06 0.13 1.48 7.40 89.05 1.59 0.27 A 0.02 0.05 0.13 1.11 2.16 7.47 88.23 0.84 AA 0.02 0.02 0.10 0.12 0.63 0.45 10.93 87.74 AAA Default CCC B BB BBB A AA AAA C U R R E N T CREDIT R A T I N G

“ Credit culture” refers to an implicit understanding among bank personnel that certain standards of underwriting and loan management must be maintained.
Strong incentives for the individual most responsible for negotiating with the borrower to assess risk properly
Sophisticated modelling and analysis introduce pressure for architecuture involving finer distinctions of risk
Strong review process aim to identify and discipline among relationship managers
10. Create Credit culture

Effective Management of Risk benefits the bank..
Efficient allocation of capital to exploit different risk / reward pattern across business
Better Product Pricing
Early warning signals on potential events impacting business
Reduced earnings Volatility
Increased Shareholder Value
To Summarise…. No Gain! No Risk …

Thanks for your attention . . .

Financial Risk Management Framwork & Basel Ii Icmap

by jhsiddiqi2003

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