Validation of Risk Management Models for Financial Institutions: Theory and Practice

Cover
Half-title
Title
Copyright
Contents
List of Figures
List of Tables
List of Contributors
Foreword
Acknowledgments
1 Common Elements in Validation of Risk Models Used in Financial Institutions
1.1 Mincer-Zarnowitz Regressions
References
2 Validating Bank Holding Companies' Value-at-Risk Models for Market Risk
2.1 Introduction
2.2 VaR Models
2.3 Conceptual Soundness
2.4 Sensitivity Analysis
2.5 Confidence Intervals for VaR
2.6 Backtesting
2.7 Results of the Backtests
2.8 Benchmarking
2.9 Conclusion
References
3 A Conditional Testing Approach for Value-at-Risk Model Performance Evaluation
3.1 Introduction
3.2 The General Framework
3.2.1 Conditional Backtesting
3.2.2 Conditional Volatility Test
3.3 Test Design
3.3.1 Specific Risk
3.3.2 Historical Price Variation
3.3.3 Concentration
3.3.4 Market Stress/Adverse Environment
3.3.5 Events
3.4 Summary
4 Beyond Exceedance-Based Backtesting of Value-at-Risk Models: Methods for Backtesting the Entire Forecasting Distribution Using Probability Integral Transform
4.1 Introduction
4.2 Data
4.3 Graphics of the Exceedance Count and Distribution of PITs
4.4 Quantifying Deviations from Uniformity of Distribution of PITs
4.5 Misspecification Tests Based on Exceptions
4.6 Misspecification Tests Based on the Distribution of PITs
4.7 Conclusion
References
5 Evaluation of Value-at-Risk Models: An Empirical Likelihood Approach
5.1 Introduction
5.2 PIT-Based Backtesting
5.2.1 Tests Based on the Distribution of PITs
5.3 Empirical Study
5.3.1 Tests Based on the Probabilities Implied by the PITs
5.4 Conclusions and Final Remarks
References
6 Evaluating Banks' Value-at-Risk Models during the COVID-19 Crisis
6.1 Introduction
6.2 Data and Summary Statistics
6.3 Were VaR models Missing Relevant Factors?
6.4 Which Factors Were Associated with Contemporaneous Backtesting Exceptions?
6.5 Comparing Linear and Logistic Regressions
References
7 Performance Monitoring for Supervisory Stress-Testing Models
7.1 Introduction
7.2 Literature Review
7.3 Performance Monitoring
7.4 Performance Monitoring Tools
7.4.1 Output Sensitivity Analysis
7.4.1.1 Scenario Sensitivity Analysis
7.4.1.2 Portfolio Sensitivity Analysis
7.4.1.3 Parameter Sensitivity Analysis
7.4.1.4 Date Sensitivity Analysis
7.4.2 Output Benchmarking
7.4.2.1 Output Backtesting
7.5 Extant Performance Monitoring of DFAST Stress-Testing Output
7.6 Conclusion
References
8 Counterparty Credit Risk
8.1 Introduction
8.2 Definitions and Terminology
8.2.1 Expected Credit Loss
Credit Valuation Adjustment (CVA)
8.3 Measurement, Pricing and Stress Testing
8.3.1 Calculation of CVA
8.3.2 Stress Testing
8.4 The Experiences of 1998 and 2008
8.5 The Capitalization of CCR
8.6 Validation of CCR Models
8.6.1 Generator of Future Market Scenarios
8.6.2 Pricing Models
8.6.3 Credit Exposure Calculator
8.6.4 CVA Calculator
8.6.5 Economic and Regulatory Capital Calculators
8.7 The Cost of Hedging the CVA
8.8 A Few Words on Backtesting and Stress Testing
8.9 Summary and Conclusions
References
9 Validation of Retail Credit Risk Models
9.1 Introduction
9.2 Importance of Retail Credit and Retail Credit Risk
9.3 Evolution of the Retail Credit Risk Model Framework
9.3.1 Static Credit and Behavioral Scoring Model
9.3.2 Multi-Period Loss Forecasting Models
9.3.2.1 Aggregate or Segmented Pool-Level Modelling Approaches
Net Charge-Off Model
Static Roll-Rate Model
Vintage Loss Forecasting Model
9.3.2.2 Loan-Level Model
PD Model
1. Definition of Default
2. Hazard/Survival Model
3. Cox Proportional Hazard Model
4. Panel Multinomial Logistic Model
5. Landmarking Approach
6. Status Transition Model
7. Exposure at Default (EAD) Model
8 Loss Given Default (LGD) Model
9.4 Issues in Retail Credit Risk Model Validation
9.4.1 Model Development and Role of Independent Validation
9.4.2 Models' Purpose and Use
9.4.2 Evaluation of Conceptual Soundness
A. Statistical Modeling Framework
B. Data and Sampling
C. Variable Selection and Segmentation
9.4.3 Outcome Analysis and BackTesting
9.4.4 Sensitivity Analysis and Benchmarking
9.4.5 Ongoing Monitoring
9.4.6 Future Challenges: Machine Learning and Validation
9.5 Conclusions
References
10 Issues in the Validation of Wholesale Credit Risk Models
10.1 Introduction
10.2 Wholesale Credit Risk Models
10.2.1 Wholesale Lending
10.2.2 Internal Risk Rating Systems
10.2.3 Wholesale Loss Modeling Overview
10.2.3.1 Accrual Loans
10.2.3.2 FVO Loans
10.2.3.3 Other Wholesale Loss Modeling Approaches
10.2.4 C&I Loss Forecasting Models for Stress Tests
10.2.4.1 Stressed PD Modeling Approaches
10.2.4.2 Stressed LGD Modeling Approaches
10.2.5 CRE Loss Forecasting Models for Stress Tests
10.2.6 FVO Portfolio Loss Modeling
10.2.6.1 Fair Value Loss
10.2.6.2 Computing Fair Value of a Loan
10.2.7 The Core Components of an Effective Validation Framework
10.3 Conclusions
References
11 Case Studies in Wholesale Risk Model Validation
11.1 Introduction
11.2 Validation of Use
11.2.1 Use Validation: AIRB Regulatory Capital Models
11.2.2 Use Validation: CCAR/DFAST Models
11.2.3 Use Validation: Summary and Conclusions
11.3 Validation of Data (Internal and External)
11.3.1 Data Validation: AIRB Regulatory Capital Models
11.3.2 Data Validation: CCAR/DFAST Models
11.3.3 Data Validation: Summary and Conclusions
11.4 Validation of Assumptions and Methodologies
11.4.1 Validation of Assumptions and Methodologies: AIRB Regulatory Capital Models
11.4.2 Validation of Assumptions and Methodologies: CCAR/DFAST Models
11.4.3 Validation of Assumptions and Methodologies: Summary and Conclusions
11.5 Validation of Model Performance
11.5.1 Validation of Model Performance: AIRB Regulatory Capital Models
11.5.2 Validation of Model Performance: CCAR/DFAST Models
11.5.2.1 Federal Reserve SR 15-18 Guidance on Assessing Model Performance
11.5.3 Model Performance Validation: Summary and Conclusions
11.5.4 Outcomes Analysis
11.5.4.1 Outcomes Analysis: AIRB Regulatory Capital Models
11.5.4.2 Outcomes Analysis: CCAR/DFAST Models
11.6 Model Validation Report
11.6.1 Model Validation Report: AIRB Regulatory Capital Models
11.6.2 Validation Report: CCAR/DFAST Models
11.6.3 Model Validation Report: Summary and Conclusions
11.7 Vendor Model Validation and Partial Model Validation
11.7.1 Partial Model Validation
References
12 Validation of Models Used by Banks to Estimate Their Allowance for Loan and Lease Losses
12.1 Introduction
12.2 Pre-2020 Accounting for ALLL
12.2.1 Reserves for Non-impaired Loans
12.2.2 Reserves for Impaired Loans
12.2.3 Reserves for Purchased Credit-Impaired Loans
12.3 The Financial Crisis and Criticisms of the Incurred Loss Methodology
12.4 The New Current Expected Credit Loss (CECL) Methodology
12.5 Potential Modeling and Validation Concerns surrounding CECL
12.5.1 Issues from Extending the Loss Measurement Window to Contractual Life
12.5.2 Issues from Incorporating Reasonable and Supportable Forecasts of the Future
12.5.3 Other Issues from Changes Brought in by CECL
12.6 General Model Validation Concerns of ALLL Models
12.6.1 Data Issues
12.6.2 Modeling Issues
12.6.3 Documentation Issues
12.6.4 Performance Testing Issues
12.6.5 Other Issues
12.7 Conclusions
Appendix A Description of HUD data and Analysis
Appendix B An Example on Maturation Effect and CECL Loss Computations
Appendix C An Example on Discounting of Cash Flows and Losses
References
13 Operational Risk
13.1 Introduction
13.2 Loss Distribution Approach (LDA)
13.2.1 LDA and the 99.9th Quantile
13.2.2 Using the LDA Appropriately
13.3 Regression Modeling
13.3.1 Dates
13.3.2 Large Loss Events
13.3.3 Small Sample Size
13.3.4 Using Regression Analysis Appropriately
13.4 Model Risk
13.4.1 Backtesting
13.4.2 Sensitivity Analysis
13.4.3 Benchmarking
13.5 Conclusion
References
14 Statistical Decisioning Tools for Model Risk Management
14.1 Introduction
14.2 Risk Modeling
14.3 Utility Analysis
14.4 Empirical Application
14.4.1 Home Mortgage Data
14.4.2 Disparity Analysis
14.4.3 Model Estimation Results
14.5 Model Evaluation
14.5.1 Comparison Metrics
14.5.2 Quadratic Reward Specification
14.5.3 Utility Comparisons
14.6 Discussion
References
15 Validation of Risk Aggregation in Economic Capital Models
15.1 Introduction
15.1.1 Literature Review
15.1.2 Validation of Economic Capital Models
15.2 Data and Descriptive Statistics
15.2.1 Variables for Risk Types and Hypothetical Bank Construction
15.2.2 Graphical Analysis
15.3 Empirical Methodology and Results
15.3.1 Benchmarking: Alternative Copula Models
15.3.1.1 Statistical Assessment Criteria
15.3.2 VaR Estimation and Backtesting Analysis
15.3.2.1 VaR Estimation
15.3.2.2 Backtesting Analysis
15.3.3 VaR Stability
15.3.4 Stress Testing
15.4 Conclusion
Appendix A: Mapping between Y9.C and Bloomberg variables
Appendix B: Mergers and Acquisition list
References
16 Model Validation of Interest Rate Risk (Banking Book) Models
16.1 Introduction
16.2 Earnings at Risk
16.3 Economic Value of Equity
16.4 Duration of Equity
16.5 Governance of ALM
16.6 Residential Mortgages
16.7 Commercial Loans
16.8 Credit Cards
16.9 Other Retail Loans
16.10 Wholesale Liabilities
16.11 Certificates of Deposit
16.12 Non-maturity Deposits
16.13 Investment Portfolio
16.14 Term Structure Modeling
16.15 Summary of Model Validation for ALM
17 Validation of Risk Management Models in Investment Management
17.1 Introduction
17.2 What Makes Validation of Investment Management Models Different?
17.3 Asset Management Models That May Be Validated Using Methodologies for Similar Models Used for the Bank's Own Assets
17.4 Conclusion
Index