Risk Adjusted Performances, Capital Management and Capital Allocation Decision Making To order this title, and for more information, click here
By Francesco Saita, Professor of Financial Markets and Institutions and Director of the M.Sc. in Finance at Bocconi University, Milan, Italy, where he is
also the Vice Director of Newfin Research Center on Financial Innovation.
Description While the highly technical measurement techniques and methodologies of Value at Risk have attracted huge interest, much less attention
has been focused on how Value at Risk and the risk-adjusted performance measures such as RAROC or economic profit/EVA?? can be effectively
used to improve a bank!|s decision making processes. Academic books are typically concerned primarily with measurement techniques, and
devote only a small section to describing the applications, usually without discussing the problems that changing organizational processes
in banks may have on business units!| behaviour. Practitioners!| books are often based on a single experience, presenting the approach
that has been pursued by a single bank, but often do not adequately evaluate that approach. In actual practice, the choice of how to
use Value at Risk and risk-adjusted performance measures has no single optimal solution, but requires effective decision making that
can identify the solution that is consistent with the bank!|s style of management and coordination mechanisms, and often with characteristics
of individual business units as well. In this book, Francesco Saita of Bocconi University argues that even though risk measurement techniques
have greatly improved in recent years for market, credit and now also operational risk, capital management and capital allocation decisions
are far from becoming purely technical and mechanical. On one hand, decisions about capital management must consider handling different
capital constraints (e.g. regulatory vs. economic capital ) and face remarkable difficulties in providing a measure of !?aggregated!?
Value at Risk (i.e. a measure that considers the overall value at risk of the bank after diversification across risk types). On the other
hand, the aim of using capital more efficiently through capital allocation cannot be achieved only through a sort of centralized asset
allocation process, but rather by designing a Value at Risk limit system and a risk-adjusted performance measurement system that are
designed to provide the right incentives to individual business units. This connection between sophisticated and cutting edge risk measurement
techniques and practical bank decision making about capital management and capital allocation make this book unique and provide readers
with a depth of academic and theoretical expertise combined with practical and real-world understanding of bank structure, organizational
constraints, and decisionmaking processes.
Audience
Primary audience: Graduate students in master's or Ph.D. programs in finance/banking; bankers and risk managers involved in capital allocation
and portfolio management.
Course titles: advanced topics in financial/banking risk management, portfolio management, mathematics of
investment, commercial bank management.
Contents (dedication)
Preface
Chapter 1
Value at Risk, Capital Management and Capital Allocation
1.1. An Introduction to Value at Risk
1.2.
Capital management and capital allocation. The structure of the book.
Chapter 2
What Is !?Capital!? Management?
2.1. Regulatory
Capital and the Evolution towards Basel II
2.1.1. The 1988 Basel I Accord and the 1996 Amendment
2.1.2. The Concept of Regulatory Capital
2.2. An Overview of the Basel II Capital Accord
2.2.1. Pillar 1: Minimum Capital Requirements. The Main Changes Introduced by Basel
II
Box 2-1. The Impact of the Basel II Accord on the Level of Minimum Regulatory Capital Requirements
2.2.2. Pillar 2: Supervisory Review
Process
2.2.3. Pillar 3: Market Discipline
2.2.4. The Debate about Basel II Adoption and Implementation
2.3. Bank!|s Estimates of
Required Capital and the Different Notions of Bank Capital
2.3.1. Book Value of Capital and the Impact of IAS/IFRS
2.3.2. Market Capitalization
and the Double Perspective of Bank Managers
2.3.3. The Impact of Alternative Notions of Capital on Capital Management and Allocation
2.4. Summary
2.5. Further Readings
Chapter 3
Market Risk
3.1. The Variance-Covariance Approach
3.1.1. A Simplified Example
3.1.2.
The Choice of the Relevant Random Variables
3.1.3. Mapping Exposures
Box 3-1. Mapping Equity Positions Through Beta: An Example
3.1.4.
VaR for a Portfolio
Box 3-2. Calculating VaR for a Three-Stock Portfolio
Box 3-3. Why Mapping Is Important
3.1.5. Estimating Volatility
and Correlation: Simple Moving Averages
3.1.6. Estimating Volatility and Correlation: Exponentially Weighted Moving Averages and GARCH
Models
3.1.7. VaR Estimates and the Relevance of the Time Horizon
3.1.8. Implied Volatilities and Correlations
Box 3-4. Deriving Implied
Volatility from Option Prices
3.2. Simulation Approaches: Historical and Monte Carlo Simulation
3.2.1. Historical Simulation
3.2.2.
The Hybrid Approach
3.2.3. Monte Carlo Simulations
3.2.4. Filtered Historical Simulations
3.3. Value at Risk for Option Positions
3.3.1.
The Problems in Option VaR Measurement
3.3.2. Potential Solutions for Option VaR Measurement
3.4. Extreme Value Theory and Copulas
3.4.1.
Extreme Value Theory
3.4.2. Copulas
3.5. Expected Shortfall and the Problem of VaR Non-Subadditivity
3.6. Backtesting Market Risk
Models
3.6.1. Which Series Should Be Considered? Actual vs. Theoretical Portfolio Returns
3.6.2. Backtesting VaR Forecasts: Unconditional
Accuracy and Independence
3.7. Internal VaR Models and Market Risk Capital Requirements
3.8. Stress Testing
3.9. Summary
3.10. Further
Readings
Chapter 4
Credit Risk
4.1. Defining Credit Risk. Expected and Unexpected Losses
4.2. Agency Ratings
4.2.1. External Rating
Assignment
4.2.2. Transition Matrixes and Cumulative and Marginal Default Probabilities
4.3. Quantitative Techniques for Stand-alone
Credit Risk Evaluation: Moody!|s/KMV EDF and External Scoring Systems
4.3.1. Merton!|s (1974) Model and Moody!|s/KMV Expected Default
Frequency
Box 4-1. Deriving the Theoretical Credit Spread for Risky Bonds in the Merton (1974) Model
4.3.2. Credit Scoring Systems
4.4.
Capital Requirements for Credit Risk under Basel II
4.4.1. The Standardized Approach
4.4.2. Foundation and Advanced Internal Rating Based
approaches
4.5. Internal Ratings
4.5.1. Internal Rating Assignment Process
4.5.2. Rating Quantification and the Definition of Default
4.5.3. Point-in-Time versus Through-the-Cycle Internal Ratings
4.6. Estimating Loss Given Default
4.7. Estimating Exposure at Default
4.8. The Interaction between Basel II and International Accounting Standards
4.9. Alternative Approaches to Credit Portfolio Risk Modelling
4.9.1. CreditMetrics??
4.9.2. Moody!|s/KMV PortfolioManager??
4.9.3. Credit Portfolio View
4.9.4. CreditRisk+
4.10. A comparison of
main credit portfolio models
Box 4-2. Industry Practices Concerning Credit Portfolio Models
Box 4-3. How Close Are Results Obtained from
Credit Risk Portfolio Models?
4.11. Summary
4.12. Further Readings
Chapter 5
Operational Risk and Business Risk
5.1. Capital Requirements
for Operational Risk Measurement: the Three Approaches Proposed by Basel II
5.1.1. The Basic Indicator Approach (BIA)
5.1.2. The Standardized
Approach (SA)
5.1.3. The Advanced Measurement Approach
5.2. The Objectives of Operational Risk Management
5.3. Quantifying Operational
Risk: Building the Data Sources
5.3.1. Operational Risk Mapping and the Identification of Key Risk Indicators
5.3.2. Building an Internal
Loss Database
5.3.3. External Loss Databases
5.3.4. Scenario Analysis
5.4. Quantifying Operational Risk: from Loss Frequency and Severity
to Operational Risk Capital
5.4.1. Modelling Severity Based on Internal Loss Data
5.4.2. Integrating Internal Severity Data with External
Data and Scenario Analysis
5.4.3. Estimating Operational Loss Frequency
5.4.4. Estimating Correlation or Dependence among Operational
Events
5.4.5. Deriving Operational Risk Capital Estimates Through Simulation
5.4.6. Is Risk Measurement the Final Step?
5.5. Case Study:
U.S. Banks!| Progress on Measuring Operational Risk (by Patrick de Fontnouvelle and Victoria Garrity, Supervision, Regulation and Credit
Department,
Federal Reserve Bank of Boston)
5.6. The Role of Business Risk and Earnings at Risk Measures
5.7. Measuring Business Risk
in Practice: Defining an Earnings at Risk Measure
5.8. From Earnings at Risk to Capital at Risk
5.9. Summary
5.10. Further Readings
Chapter 6
Risk Capital Aggregation
6.1. The Need for Harmonization: Time Horizon, Confidence Level and the Notion of Capital
6.2.
Risk Aggregation Techniques
6.2.1. Choosing the Components to be Aggregated: Business Units versus Risk Types
6.2.2. Alternative Risk
Aggregation Methodologies
6.3. Estimating Parameters for Risk Aggregation
Box 6-1 Some Examples of Linear Correlation Coefficients Estimates
from Existing Studies and Their Implications on Aggregated Risk Capital
6.4. Case Study: Capital Aggregation within Fortis (by Luc Henrard,
Chief Risk Officer, Fortis, and Ruben Olieslagers, Director, Central Risk Management, Fortis)
6.5. A Synthetic Comparison of Alternative
Risk Aggregation Techniques
6.6. Summary
6.7. Further Readings
Chapter 7
Value at Risk and Risk Control for Market and Credit Risk
7.1. Defining VaR-based Limits for Market Risk: Identifying Risk-Taking Centers
Box 7-1 Clarifying VaR Measurement Limitations: Deutsche
Bank!|s Example
7.2. Managing VaR Limits for Market Risk: the Links between Daily VaR and Annual Potential Losses
7.2.1. Translating
Actual Daily VaR Values into an Ex-Post Yearly VaR Equivalent
Box 7-2 Daily VaR Fluctuations and Their Implications for Ex-Post Yearly
VaR Equivalent: An Example Based on Real Data
7.2.2. Translating Yearly Ex-Ante Acceptable Loss into a Daily VaR Equivalent
7.2.3.
The Case of Variable VaR Limits and the Role of Cumulated Losses
7.3. Managing VaR-Based Trading Limits
7.4. Identifying Risk Contributions
and Internal Hedges: VaRDelta, Component VaR and Incremental VaR
Box 7-3 A Variant for the Calculation of Component CaR
7.5. Managing
Risk and Pricing Limits for Credit Risk
7.5.1. Setting Loan Autonomy Limits: From Notional Size to Expected Loss
7.5.2. Setting Loan
Pricing Limits
7.5.3. Case 1: Large Borrower Applying for a Loan to an Investment Bank
7.5.4. Case 2: SME Applying for a Loan to a Smaller
Retail-Oriented Bank
7.6. Summary
7.7. Further Readings
Chapter 8
Risk-Adjusted Performance Measurement
8.1.Business Areas, Business
Units and The Double Role of Risk-Adjusted Performance Measures
8.2. Not Only Capital at Risk: Profit Matters Too
8.2.1. Transfer Prices
8.2.2. Cost Attribution and Its Impact on RAP Measures
8.3. Capital Investment versus Capital Allocation
8.4. Choosing the Measure
of Capital at Risk: (a) Allocated vs. Utilized Capital
8.5. Choosing the Measure of Capital at Risk: (b) Diversified vs. Undiversified
Capital
8.5.1.A Comparison of Alternative Diversified CaR Measures
8.5.2. Criteria for Choosing Between Diversified or Undiversified
CaR
8.6. Choosing the Risk-Adjusted Performance Measure: EVA vs. RAROC
8.7. Variants and Potential Extensions
8.7.1. Differentiated
Target Returns
8.7.2. Alternative RAP Measures
8.7.3. Expected Shortfall and Performance Measurement
8.8. Risk-Adjusted Performances
and Managers!| Performance Evaluation
8.9. Summary
8.10. Further Readings
Chapter 9
Risk-adjusted performance targets, capital allocation
and the budgeting process
9.1. From the Banks Cost of Equity Capital to Performance Targets for the Bank
9.1.1. Estimating the cost
of equity capital
9.1.2. Defining the Target Rate of Return
9.2. Should Business Units Target Returns Be Different?
9.2.1. The Potential
Impacts of a Single Hurdle Rate
9.2.2. Estimating Betas for Different Businesses
9.2.3. Applying Different Costs of Capital: Identifying
the Driver
9.3. Capital Allocation and the Planning and Budgeting Process
9.3.1. Why Should Capital Allocation Be Linked to the Planning
Process?
9.3.2. Why Should Capital Allocation Not Be Linked to the Planning Process?
9.4. Capital Allocation Process at UniCredit Group
(by Elio Berti, head of Capital Allocation, CFO Department, UniCredit)
9.5. Summary
9.6. Further Readings
9.7. Final Remarks
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