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