Book 3. Operational Risk

FRM Part 2

OR 19. Range of Practices and Issues in Economic Capital Frameworks

Presented by: Sudhanshu

Module 1. Risk Measures and Risk Aggregation

Module 2. Validation, Dependency, Counterparty Credit Risk and Interest Rate Risk

Module 3. BIS Recommendations, Constraints and Opportunities, and Best Practices and Concerns

Module 1. Risk Measures and Risk Aggregation

Topic 1. Risk Measures Overview

Topic 2. Common Risk Measures

Topic 3. Practical Challenges

Topic 4. Risk Aggregation Overview

Topic 5. Risk Aggregation Challenges: Standardization and Measurement Issues

Topic 6. Five Common Risk Aggregation Methodologies

Topic 7. Variance-Covariance Approach

Topic 1. Risk Measures Overview

• Objective: To quantify risk for a given bank.

• Ideal Risk Measure Characteristics:

  • Intuitive and easy to understand

  • Stable and easy to compute

  • Coherent (satisfies monotonicity, subadditivity, positive homogeneity, and translation invariance)

  • Interpretable in economic terms

• Key Challenge: No single measure perfectly captures all necessary elements.​​

Topic 2. Common Risk Measures

• ​​Standard Deviation

  • Challenge 1: Not stable; depends on assumptions about the loss distribution.

  • Challenge 2: Not coherent; violates the monotonicity condition.

  • Challenge 3: Simple, but not very meaningful for risk decomposition.

• ​Value at Risk (VaR) - The most commonly used measure

  • Challenge 1: Not stable; depends on assumptions about the loss distribution.

  • Challenge 2: Not coherent; violates the subadditivity condition. This can cause problems with internal capital allocation and limit setting.

• Expected Shortfall (ES)

  • Challenge 1: May or may not be stable, depending on the loss distribution.

  • Challenge 2: Not easy to interpret, and the link to the bank's desired target rating is unclear.

• Spectral and Distorted Risk Measures

  • Challenge 1: Not intuitive or easily understood, and rarely used in practice.

  • Challenge 2: May or may not be stable, depending on the loss distribution.​

• Banks often use multiple risk measures for different purposes.

• VaR is commonly used for measuring absolute risk and is easier to communicate to senior management.

• ES is increasingly used for capital allocation due to its stability.

• Regulatory Challenge: Regulators do not have a clear preference for one measure. If a bank uses different measures for internal and external purposes, a logical connection must exist. This presents an analytical challenge for regulators to compare a bank's internal and regulatory capital amounts.

Topic 3. Practical Challenges

Practice Questions: Q1

Q1. Which of the following risk measures is the least commonly used measure in the practice of risk management?
A. Value at risk.

B. Standard deviation.
C. Expected shortfall.
D. Spectral risk measures.

Practice Questions: Q1 Answer

Explanation: D is correct.

Spectral and distorted risk measures are the least used of the four measures and are mainly of academic interest only.

Topic 4. Risk Aggregation Overview

• Risk Type Identification Challenges: Risk aggregation requires identifying individual risk types and making aggregation choices, but classification by risk types (market, credit, operational, business) may be approximate and error-prone.
• Definition Inconsistencies: Risk type definitions may differ across banks or even within the same bank, creating complications in the aggregation process and reducing comparability.
• Oversimplified Portfolio Classification: Portfolios often contain multiple simultaneous risk types but are typically represented by a single risk type for classification purposes, potentially resulting in inaccurate risk measurements.
• Aggregation Bias Risk: Simplistic risk type distinctions may bias the overall aggregation process by failing to capture the true complexity of overlapping risk exposures within portfolios.
• Multiple Aggregation Approaches: Banks use different methodologies including risk-type silos across the entire bank, business unit silos, or combined approaches, with no universally accepted standard method.
• Method-Specific Advantages: Each aggregation approach offers specific advantages, explaining why no single unanimously accepted methodology has emerged across the banking industry.

Topic 5. Risk Aggregation Challenges: Standardization and Measurement Issues

• Comparable Units Requirement: Before aggregating risk types into a single measure, they must be expressed in comparable units across three critical dimensions: risk metric, confidence level, and time horizon.
  • Risk Metric and Subadditivity: Risk quantification relies on specific metrics that must satisfy the subadditivity condition to ensure accurate aggregation across different risk types.
  • Confidence Level Inconsistencies: Different risk types have varying loss distribution shapes, creating differences in confidence intervals and adding complexity when aggregating risks with inconsistent confidence levels.
  • Time Horizon Challenges: Selecting appropriate risk measurement time horizons presents significant difficulties, as combining risk measures from different time horizons creates problems regardless of measurement methods and leads to inaccurate risk type comparisons.
• Portfolio Diversification Limitations: The common belief that combining portfolios reduces risk per investment unit versus weighted averages doesn't hold with VaR, which doesn't necessarily satisfy subadditivity conditions.
• Covariance Assumption Flaws: The assumption that covariance fully captures risk dependencies can be false, as risk interactions may sometimes result in higher combined risk rather than lower, highlighting additional computational challenges in risk aggregation.

1. Simple Summation: Adding together individual capital components.

   • Challenge: Does not differentiate between risk types, assumes equal weighting, and ignores diversification benefits and interactions between risks.

2. Constant Diversification: Same process as simple summation except that it subtracts a fixed diversification percentage from the overall amount.

   • Challenge: Similar to simple summation, but subtracts a fixed diversification percentage. It still suffers from the same fundamental challenges.

3. Variance-Covariance Matrix (Commonly used): Summarizes the interdependencies across risk types and provides a flexible framework for recognizing diversification benefits.

   • Challenge: Inter-risk correlations are difficult and costly to obtain, and the method does not adequately capture nonlinearities or skewed distributions. Often relies on expert judgment due to poor data quality.

4. Copulas:  Combines marginal probability distributions into a joint probability distribution through copula functions.

   • Challenge: Demanding input requirements, very difficult to validate parameterization, and building a joint distribution is complex.

5. Full Modeling/Simulation: Simulate the impact of common risk drivers on all risk types and construct the joint distribution of losses.

   • Challenge: The most demanding method in terms of inputs, information technology, and time. Can provide a false sense of security.

Topic 6. Five Common Risk Aggregation Methologies

• Variance-Covariance Method Limitations: Banks commonly use variance-covariance approaches but frequently rely on expert judgment to complete matrix items due to unavailable or poor-quality bank-specific data.
• Conservative Bias in Correlations: Banks often employ "conservative" variance-covariance matrices with correlations that are approximate and intentionally biased upward to account for uncertainty.
• Dimensionality Reduction Trade-offs: To minimize expert judgment requirements, banks may limit matrix dimensionality and aggregate risk categories, inadvertently embedding correlation assumptions while reducing homogeneity within categories.
• Aggregation Challenges: Risk category aggregations make quantification more difficult as each category becomes less homogeneous and harder to accurately measure and model.
• Sophistication vs. Overconfidence: More sophisticated methodologies can create false confidence in output accuracy, requiring careful consideration of potential misleading results.
• Robustness and Error Checks: Banks must implement robustness checks and estimates of specification and measurement error to prevent overreliance on sophisticated model outputs.

Topic 7. Variance-Covariance Approach

Practice Questions: Q2

Q2. Which of the following aggregation methodologies is characterized by great difficulty in validating parameterization and building a joint distribution?
A. Copulas.
B. Constant diversification.
C. Variance-covariance matrix.
D. Full modeling/simulation.

Practice Questions: Q2 Answer

Explanation: A is correct.

Copulas have two notable disadvantages:

1. parameterization is very difficult to validate, and
2. building a joint distribution is very difficult.

Module 2. Validation, Dependency, Counterparty Credit Risk and Interest Rate Risk

Topic 1. Validation of Models

Topic 2. Six Qualitative Validation Processes

Topic 3. Six Quantitative Validation Processes

Topic 4. Model Validation Limitations and Regulatory Concerns

Topic 5. Dependency Modeling in Credit Risk: Overview

Topic 6. Credit Risk Model Assumptions and Validity Challenges

Topic 7. Equity Price Models and Regulatory Approach Challenges

Topic 8. Evaluating Counterparty Credit Risk

Topic 9. Assessing Interest Rate Risk in the Banking Book

Topic 1. Validation of Models

• Model Validation Definition: Validation serves as "proof" that a model functions as intended, though its effectiveness varies depending on the specific testing objective and model characteristics.
• Risk Sensitivity vs. Accuracy Testing: While validation is useful for testing model risk sensitivity, it proves less effective for validating the accuracy of high quantiles in loss distributions.
• Economic Capital vs. IRB Model Validation: Economic capital model validation differs from IRB model validation because economic capital models produce distributions rather than single predicted forecasts that can be directly compared to actual outcomes.
• VaR Model Similarities: Economic capital models share significant similarities with VaR models, despite operating with longer time horizons, higher confidence levels, and facing greater data scarcity challenges.
• Distribution-Based Output Challenges: The distributional nature of economic capital model outputs creates unique validation complexities compared to models that generate point estimates or single forecasts.

Topic 2. Six Qualitative Validation Processes

1. Use Test: Regulators rely more on models used for internal purposes.

   • Challenge: Regulators need a detailed understanding of how the model's properties are being used.

2. Qualitative Review: Examining documentation, discussing with developers, and testing algorithms.

   • Challenge: Ensuring the model works in theory, considers correct risk drivers, and has accurate mathematics.

3. ​Systems Implementation: User acceptance testing and code checks before deployment.

4. Management Oversight: Senior management must be involved in interpreting model outputs for business decisions.

   • Challenge: Ensuring management understands the model's use and output interpretation.

5. Data Quality Checks: Processes to ensure data used is complete, accurate, and relevant.

6. Examination of Assumptions: Reviewing assumptions (e.g., correlations, recovery rates) and performing sensitivity testing.

1. Validation of Inputs & Parameters: Validation of those parameters not included in the IRB approach, such as correlations.

   • Challenge: Every model is based on underlying assumptions, so simply checking inputs is not fully effective, especially in complex models.

2. Model Replication: Attempts to replicate the model results obtained by the bank.​

   • ​Challenge: Rarely sufficient for validation. Simply re-running algorithms to get the same results is not enough.

3. Benchmarking & Hypothetical Portfolio Testing: ​Involves determining whether the model produces results comparable to a standard model or comparing models on a set of reference portfolios.

   • Challenge: Compares models but provides little assurance the model reflects reality.

4. ​Backtesting: Considers how well the model forecasts the distribution of outcomes—comparison of outcomes to forecasts.
   • ​​Challenge: Only effective for models whose outputs can be compared to a quantifiable metric. Not yet a major part of banks' validation practices for economic capital.

5. Profit and Loss Attribution: Involves regular analysis of profit and loss—comparison between causes of actual profit and loss versus the model’s risk drivers.

   • Challenge: Not widely used except for market risk pricing models.

6. Stress Testing: Stressing the model and comparing its outputs to stress losses.

Topic 3. Six Qualitative Validation Processes

Practice Questions: Q3

Q3. Which of the following model validation processes is specifically characterized by the limitation that it provides little comfort that the model actually reflects reality?
A. Backtesting.
B. Benchmarking.
C. Stress testing.
D. Qualitative review.

Practice Questions: Q3 Answer

Explanation: B is correct.

With benchmarking and hypothetical portfolio testing, the process has its limitations because it can only compare one model against another and may provide little comfort that the model actually reflects “reality.” All that the process is able to do is provide broad comparisons confirming that input parameters or model outputs are broadly comparable.

Topic 4. Model Validation Limitations and Regulatory Concerns

• Selective Validation Effectiveness: Validation processes may be highly effective for risk sensitivity testing but prove ineffective for assessing overall absolute accuracy of models.
• Conceptual Soundness Challenges: Validating the conceptual soundness of capital models is difficult because model development requires assumptions that may not be testable, making absolute certainty impossible.
• Theory vs. Practice Gap: Even when underlying model assumptions appear reasonable and logical, they may not hold true in practical applications, creating validation uncertainties.
• Weak Industry Practices: From a regulatory perspective, industry validation practices are particularly weak for total bank capital adequacy and overall model calibration assessments.
• High Quantile Validation Difficulties: Validation projects face significant challenges when evaluating high quantiles of loss distributions over extended time periods, compounded by data scarcity and technical issues like tail estimation.
• Management Awareness Imperative: Senior management and model users must understand model limitations and the risks of using incompletely validated models for critical business decisions.

Topic 5. Dependency Modeling in Credit Risk: Overview

• Critical Modeling Challenge: Modeling dependency structures between borrowers is crucial yet challenging, requiring consideration of both linear and nonlinear dependency relationships between obligors.
• Three Primary Modeling Approaches: Dependencies can be modeled using credit risk portfolio models, models using copulas, and models based on the asymptotic single-risk factor (ASRF) model.
• ASRF Implementation Options: Banks may use their own correlation estimates or employ multiple systematic risk factors to address concentrations, raising questions about calibration methods and addressing infinite granularity assumptions.
• IRB Framework Application: ASRF can be used to compute capital requirements for credit risk under the Internal Ratings-Based (IRB) framework, making correlation accuracy critical.
• Regulatory Scrutiny Requirements: Regulators may need to test accuracy and strength of bank correlation estimates given heavy reliance on model assumptions and significant impact on economic capital calculations.
• Reliability Concerns: Multiple issues exist regarding challenges in developing reliable dependency assumptions used in credit risk portfolio models, affecting overall model credibility.

Topic 6. Credit Risk Model Assumptions and Validity Challenges

• Questioned Core Assumptions: Past validity concerns include ASRF Gaussian copula approach, normal distribution for default-driving variables, correlation stability over time, and joint assumptions of correct default probabilities with doubly-stochastic processes.
• Time-Clustering Limitations: Some models using standard assumptions struggle to explain observed time-clustering of defaults in certain markets, raising doubts about model adequacy.
• PD-LGD Correlation Gaps: Insufficient integration of correlation between probability of default and loss given default, coupled with inadequate LGD variability modeling, may underestimate required economic capital.
• Source Identification Problems: Inadequate correlation modeling creates challenges in identifying different sources of correlations and clustering of defaults and losses across portfolios.
• Business Cycle Impact: Rating changes are greatly influenced by business cycles and explained by different models during expansionary versus recessionary periods, affecting calibration approaches.
• Sample Period Sensitivity: The sample period and approach used to calibrate dependency structures significantly impact whether correlation estimates are over or underestimated.

Topic 7. Equity Price Models and Regulatory Approach Challenges

• Asset Return Approximation Issues: Models assume unobservable asset returns can be approximated by equity price changes but fail to consider that relationships between asset returns and equity prices are unobservable and potentially nonlinear.
• Irrelevant Information Problem: Using equity prices to estimate credit default probability is problematic because prices may include information irrelevant for credit risk purposes, resulting in correlation estimate inaccuracies.
• Limited Historical Data: Regulatory-type approaches face challenges with limited historical data for correlation estimation, and assumptions used may be inconsistent with underlying Basel II credit risk model assumptions.
• Concentration Risk Management: Basel II risk weight model usage requires concentration risk accounting through other measures and management methods, requiring regulatory evaluation of such approaches.
• Misspecification Risks: Key challenge involves using misspecified or incorrectly calibrated correlations and normal distributions that don't replicate asset return distribution details, leading to large portfolio credit risk measurement errors.
• Economic Capital Accuracy: Correlation and distribution misspecification may result in significant errors in measuring both portfolio credit risk and required economic capital calculations.

Topic 8. Evaluating Counterparty Credit Risk

• Challenges in Evaluating Counterparty Credit Risk

  • Multi-System Data Integration: Task requires obtaining data from multiple systems and measuring exposures from enormous numbers of transactions, including many with embedded optionality across wide-ranging time periods.
  • Complex Risk Management: Banks must monitor collateral and netting arrangements while categorizing exposures across numerous counterparties, necessitating well-developed processes and trained staff to handle these operational challenges.

• Market Risk-Related Challenges to Counterparty EAD Estimation

  • Counterparty Credit Exposure Challenges: Requires simulating market risk factors and revaluating counterparty positions under risk factor shocks, similar to VaR models, but faces two key challenges when adapting VaR technology.

  • Netting: Unlike VaR models, which net positions across a portfolio, counterparty credit risk requires calculation at the netting set level, significantly increasing computational effort.

  • Holding Periods: VaR is usually for a short-term holding period, but counterparty credit exposure must be measured over much longer time horizons. Therefore, market risk factors need to be simulated over much longer time periods than in VaR calculations, and the revaluation of the potential exposure in the future must be done for the entire portfolio at certain points in the future.

Topic 8. Evaluating Counterparty Credit Risk

• Credit Risk-Related Challenges to PD and LGD Estimation

  • Standalone Counterparty Assessment: Material transactions with counterparties having no other bank exposures require calculating specific probability of default (PD) and loss given default (LGD) for both counterparty and transaction.
  • Hedge Fund Information Gaps: Measurement challenges arise with hedge funds due to limited information on underlying fund volatility, leverage levels, and investment strategies employed.
  • Transaction-Specific LGD Requirements: Even for counterparties with existing bank credit exposures, banks must calculate specific LGD for each individual transaction.
• ​Operational Risk-Related Challenges in Managing Counterparty Credit Risk
  • ​Resource and System Requirements: Managing counterparty credit risk requires specialized computer systems and trained personnel to handle complex transactions including daily limit monitoring, mark-to-market valuations, collateral management, and intraday liquidity extensions.
  • Measurement Error Risks: Complicated transaction processes increase the likelihood of measurement errors, particularly in daily monitoring, valuation, and collateral management activities.
  • Operational Risk Quantification Challenges: Quantifying operational risks proves especially difficult for new or rapidly growing businesses, new products or processes, intraday credit extensions, and infrequent but severe events.

Topic 8. Evaluating Counterparty Credit Risk

• Differences in Risk Profiles Between Margined and Non-Margined Counterparties

  • Forecasting Period Differences: Margined counterparties require short forecasting periods while non-margined counterparties typically need much longer time horizons for risk modeling.
  • Aggregation Challenges: Risk aggregation between margined and non-margined counterparties is difficult because standard procedures use single time periods for all positions, conflicting with the different forecasting requirements.
• ​Aggregation Challenges
  • ​Firm-Wide Complexity: Challenges increase significantly when moving from measuring single counterparty credit risk to firm-wide credit risk measurement for economic capital purposes.
  • Mixed Activity Aggregation: When counterparties have both derivatives and securities financing activities, existing systems may be unable to handle proper aggregation across these different activity types.
  • Multi-Risk Integration: Additional aggregation challenges arise when combining high-level credit risk measures with market risk and operational risk measures to calculate economic capital.
  • Detailed Component Breakdown: Breaking down counterparty credit risk into detailed components (similar to market risk approaches) faces computational complexities and enormous data requirements that are generally cost-prohibitive for frequent analysis.
  • System Infrastructure Limitations: Many banks face challenges from outdated systems unable to handle required computational and data processing demands.

Topic 9. Assessing Interest Rate Risk in the Banking Book

• Long holding periods and embedded optionality in banking book items create complex modeling challenges due to indeterminate cash flows on both assets and liabilities that are difficult to predict.
• Optionality in the Banking Book:
  • Major Measurement Challenge: Nonlinear risk emerges from long-term fixed-income obligations with embedded borrower prepayment options and embedded options in non-maturity deposits.
  • Asset Side Prepayment Risk: Prepayment risk options in mortgages, mortgage-backed securities, and consumer loans create uncertain cash flows, making interest rate risk measurement difficult.
  • Liability Side Dual Options: Non-maturity deposits contain two embedded options - banks can determine depositor interest rates and amendment timing, while depositors can withdraw entire balances without penalty.
  • Complex Option Interactions: The interaction between bank rate-setting options and depositor withdrawal options creates significant valuation and interest rate sensitivity measurement problems.
  • Advanced Modeling Requirements: Sufficiently modeling optionality exposures requires very complex stochastic-path evaluation techniques to capture the full range of potential outcomes.

Topic 9. Assessing Interest Rate Risk in the Banking Book

• Banks' Pricing Behavior
  • Complex Modeling Requirements: Measuring interest rate risk requires models to analyze persistence of various non-maturity banking products and determine bank interest rates considering market conditions, customer relationships, commercial power, and optimal policies.
  • Credit Risk Integration Challenge: Determining bank interest rates requires pricing credit risk across different products, necessitating pricing rules that link credit spreads to macroeconomic conditions and ensuring interest rate stress scenarios account for dependencies between interest rate and credit risk factors.
• ​Choice of Stress Scenarios: The drawbacks of using simple interest rate shocks pose interest rate measurement challenges because the shocks:​

  • Are not based on probabilities and, therefore, are dificult to integrate into

    economic capital models based on VaR.

  • Are not necessarily sensitive to the current rate or economic environment.

  • Do not take into account changes in the slope or curvature of the yield curve.

  • Do not allow for an integrated analysis of interest rate and credit risks on banking book items.

Module 3. BIS Recommendations, Constraints and Opportunities, and Best Practices and Concerns

Topic 1. BIS Recommendations for Supervisors

Topic 2. Economic Capital Constraints and Opportunities

Topic 3. Governance of Economic Capital - Best Practices

Topic 4. Governance of Economic Capital - Key Concerns

Topic 1. BIS Recommendations for Supervisors

• There are 10 Bank for International Settlements (BIS) recommendations:
  • Economic Capital Model Usage: Banks must demonstrate how economic capital models integrate into corporate decision-making and risk acceptance processes, with boards understanding differences between gross standalone and net diversified enterprise-wide risk.
  • Senior Management Commitment: Economic capital processes require significant senior management commitment, understanding of importance in corporate planning, and ensuring strong supporting infrastructure.
  • Transparency and Integration: Economic capital results must be traceable and understandable for usefulness, requiring reliable absolute estimates and flexibility for firm-wide stress testing.
  • Risk Identification: Crucial starting point requiring thorough risk measurement processes to ensure proper risk drivers, positions, and exposures are captured, minimizing variance between actual and measured risk through sensitivity analysis, stress testing, or scenario analysis for hard-to-quantify risks.
  • Risk Measure Selection: Banks must understand strengths and weaknesses of chosen risk measures, recognizing no single economic capital risk measure is universally preferred or perfect.

Topic 1. BIS Recommendations for Supervisors

• Risk Aggregation Quality: Aggregation reliability depends on measurement component quality and risk interrelationships, requiring consistent parameters and methodologies that mirror bank's business composition and risk profile.
• Comprehensive Validation: Economic capital model validation must be thorough with corroborating evidence proving models work as intended within agreed confidence intervals and time periods to absorb unexpected losses.
• Credit Risk Dependencies: Banks must assess appropriateness of dependency structures in credit portfolios, addressing model limitations through supplementary approaches like sensitivity or scenario analysis.
• Counterparty Credit Risk Tradeoffs: Banks must consider tradeoffs between available measurement methods, using additional approaches like stress testing to cover all exposures and properly aggregate before gaining comprehensive perspective.
• Banking Book Interest Rate Risk: Financial instruments with embedded options require close examination for risk control, with tradeoffs between earnings-based versus economic value-based measurement models requiring careful consideration.

Topic 2. Economic Capital Constraints and Benefits

• Credit Portfolio Management

  • Constraints Imposed: ​(1) Credit quality of each borrower is determined in a portfolio context, not on a standalone basis; (2) A loan’s incremental risk contribution is used to determine the concentration of the loan portfolio.

  • Opportunities Offered: ​(1) The process allows one to determine appropriate hedging strategies to use in reducing portfolio concentration; (2) Credit portfolio management becomes a means for protecting against risk deterioration.

• ​Risk-Based Pricing
  • ​Constraints Imposed: (1) Pricing decisions are based on expected risk-adjusted return on capital (RAROC), so deals will be rejected if they are lower than a specific RAROC. The proposed interest rate is determined by the amount of economic capital allocated to the deal; (2) Pricing decisions include: cost of funding, expected loss, allocated economic capital, and additional return required by shareholders. Therefore, a minimum interest rate is determined that will increase shareholder value.
  • Opportunities Offered: Can be used to maximize the bank’s profitability. For example, some pricing decisions may need to be overridden because certain customer relationships are more profitable (at a lower price/interest rate) or desirable from a reputational point of view. Of course, such overrides are not taken lightly and require upper management approval, as well as rigorous subsequent monitoring

Practice Questions: Q4

Q4. Which of the following categories of BIS recommendations specifically refers to the need to consider using additional methods, such as stress testing, to help cover all exposures?
A. Risk aggregation.

B. Counterparty credit risk.
C. Dependency modeling in credit risk.
D. Interest rate risk in the banking book.

Practice Questions: Q4 Answer

Explanation: B is correct.

There are tradeoffs to be considered when deciding between the available methods of measuring counterparty credit risk. Additional methods, such as stress testing, need to be used to help cover all exposures.

Topic 2. Economic Capital Constraints and Benefits

• Customer Profitability Analysis

  • Constraints Imposed: ​(1) The analysis is complicated in that many risks need to be aggregated at the customer level. (2) Customers need to be segmented in terms of ranges of (net) return per unit of risk; the underlying information is difficult to measure and allocate.

  • Opportunities Offered: ​(1) Assuming that the measurement obstacles have been overcome, the analysis can be easily used to determine unprofitable or only slightly profitable customers. Such customers could be dropped and economic capital allocated to the more profitable customers. (2) Economic capital is used in maximizing the risk-return tradeoff (through relative risk-adjusted profitability analysis of customers).

• Management Incentives
  • ​Constraints Imposed: Studies show that compensation schemes are a minor consideration in terms of the actual uses of economic capital measures at the business unit level.
  • Opportunities Offered: It is suggested that management incentives is the issue that motivates bank managers to participate in the technical aspects of the economic capital allocation process.

Practice Questions: Q5

Q5. The use of which of the following items is meant more for protecting against risk deterioration?
A. Risk based pricing.
B. Management incentives.
C. Credit portfolio management.
D. Customer profitability analysis.

Practice Questions: Q5 Answer

Explanation: C is correct.

Credit portfolio management is used as a means to protect against risk deterioration. In contrast, risk based pricing is used to maximize the bank’s profitability; customer profitability analysis is used to determine unprofitable or only slightly profitable customers; and management incentives are used to motivate managers to participate in the technical aspects of the economic capital allocation process.

Topic 3. Governance of Economic Capital – Best Practices

• Senior Management Leadership: Successful economic capital framework implementation depends critically on senior management involvement and experience, as they serve as primary drivers for framework adoption.
• Business Unit Structure and Expertise: Banks employ either centralized approaches (assigning responsibilities to single functions like Treasury) or decentralized approaches (sharing between finance and risk functions), with varying capital allocation responsibilities and reallocation flexibility.
• Measurement and Disclosure Timing: Most banks compute economic capital monthly or quarterly, with Basel II Pillar 3 encouraging disclosure of information about capital allocation to risks.
• Formal Governance Framework: Policies and procedures for owning, developing, validating, and monitoring economic capital models promote consistent application across the bank, with model owners typically overseeing the entire framework.

Topic 4. Governance of Economic Capital – Key Concerns

• Senior Management Buy-In: Management commitment level determines meaningfulness of economic capital processes, requiring senior leadership to understand importance of applying economic capital measures for strategic planning purposes.
• Integrated Stress Testing Role: While many banks apply stress tests, using more integrated stress testing allows better assessment of stress scenario impacts on specific economic capital measures.
• Absolute vs. Relative Risk Measurement: Correctly interpreting economic capital requires measuring either absolute capital levels or relative risk basis, with considerations for diversification assumptions, management involvement, and risk capture methods.
• Multi-Factor Capital Determination: Economic capital should not be the sole determinant of required capital, as most banks align with external credit ratings while balancing shareholder profitability desires (lower capital) against rating agency solvency requirements (higher capital).
• Available Capital Resource Definition: No standard definition exists for available capital among banks, with most institutions adjusting Tier 1 capital to determine available capital resources.
• Model Transparency Requirements: Economic capital models prove more useful for senior managers when transparent, with increased documentation improving validity for business decision-making applications.