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Frequently Used Abbreviations Foreword Preface Acknowledgments Section I: An Approach to Fixed Income Investment Risk Management Chapter 1: An Investment Risk Management Paradigm 1.1 Introduction 1.2 Elements of Risk Management 1.3 BlackRocks Investment and Risk Management Approach 1.4 Introduction to the BlackRock Investment Risk Paradigm Chapter 2: Parametric Approaches to Risk Management 2.1 Introduction  2.2 Measuring Interest Rate Exposure: Analytical Approaches 2.2.1 Macaulay and Modified Duration, and Convexity 2.2.2 Option-Adjusted Framework: OAV, OAS, OAD, OAC 2.2.3 Dynamic Nature of Local Risk Measures: Duration and Convexity Drift 2.2.4 Interest Rate Scenario Analysis 2.3 Measuring Interest Rate Exposure: Empirical Approaches 2.3.1 Coupon Curve Duration 2.3.2 Empirical (Implied) Duration 2.4 Measuring Yield Curve Exposure 2.4.1 Key Rate Durations 2.5 Measuring and Managing Volatility Related Risks 2.5.1 Volatility Duration 2.5.2 Option Usage in Portfolio Management 2.6 Measuring Credit Risk 2.6.1 Spread Duration 2.6.2 Duration Times Spread (DxS) 2.7 Measuring Mortgage-Related Risks 2.7.1 Prepayment Duration 2.7.2 Mortgage/Treasury Basis Duration 2.8 Measuring Impact of Time Chapter 3: Modeling Yield Curve Dynamics 3.1 Probability Distributions of Systematic Risk Factors 3.2 Principal Component Analysis: Theory and Applications 3.2.1. Introduction 3.2.2 Principal Components Analysis 3.2.3 The First Principal Component and the Term Structure of Volatility 3.2.4 Example: Historical Steepeners and Flatteners of the U.S. Treasury Curve 3.3 Probability Distributions of Interest Rate Shocks Chapter 4: Portfolio Risk: Estimation and Decomposition 4.1 Introduction 4.2 Portfolio Volatility and Factor Structure 4.3 Covariance Matrix Estimation 4.3.1 Weighting of Historical Data 4.3.1.1 Exponential Decay Weighting 4.3.1.2 Alternative Weighting Schemes and Stress Scenarios 4.3.1.3 Enhancing Volatility Responsiveness Dynamically 4.3.2. Asynchronicity 4.3.2.1 Overlapping Covariance Matrix 4.3.2.2 Newey-West Estimation 4.3.3. Factor Model Structure: Generalizations 4.3.3.1. Optimization of the Error-Bias Tradeoff 4.3.3.2. Misspecification and Omitted Covariation 4.3.4 Covariance Matrix Estimation: Summary and Recommendations 4.4 Ex-Ante Risk and Value-at-Risk (VaR) Methodologies 4.4.1 VaR Estimation Approaches 4.4.2. Enhanced HVaR 4.4.2.1. EHVaR Systematic Risk Methodology 4.4.2.2 EHVaR Idiosyncratic Risk Methodology 4.4.3. VaR Estimation: Summary 4.5 Introduction to Risk Decomposition 4.6. Alternative Approaches to Risk Decomposition 4.6.1 A Comparison of the Different Approaches 4.7. Risk Decomposition Using Contribution to Risk 4.7.1 Security-level Contributions and Aggregations 4.7.2 Factor-level Contributions and Aggregations 4.7.3. Decomposing Contribution to Risk into Atomic Contributions 4.7.4 Decomposing Contribution to Risk into Exposure, Volatility and Correlation 4.7.5 Decomposing Contribution to Risk using Analysis of Variance 4.8. Risk Decomposition Through Time 4.9. Risk Decomposition: Summary Chapter 5: Market-Driven Scenarios: An Approach for Plausible Scenario Construction 5.1 Introduction 5.2 Implied Stress Testing Framework 5.2.1 Market-Driven Scenario Framework 5.2.2 Scenario Likelihood 5.2.3 From Likelihood to Probability 5.2.4 Decomposing the Scenario Z-Score 5.2.5 Specifying a Covariance Matrix 5.3 Developing Useful Scenarios 5.3.1 Scenario Definition 5.4 A Market-Driven Scenario Example: Brexit 5.4.1 Describing Different Brexit Scenario Outcomes 5.4.2 Identifying Key Policy Shocks in Soft Brexit Scenario 5.5 Conclusion Chapter 6: A Framework to Quantify and Price Geopolitical Risks 6.1 Introduction 6.2 Setting the Scene 6.2.1 Short and Sharp 6.2.2 Shades of Gray 6.3 BlackRocks Framework for Analyzing Geopolitical Risks 6.4 Global Trade Deep Dive 6.4.1 Calibrating the Shocks 6.5 What is Already Priced in? 6.5.1 Is It Priced In? 6.5.2 Adjusted Impacts 6.5.3 Assessing Likelihood 6.5.4 Takeaways 6.6 Taking Action 6.6.1 Key Drivers 6.6.2 BGRI-Specic Assets 6.6.3 The Path Forward 6.7 Caveats and Cautions Chapter 7: Liquidity Risk Management 7.1 Introduction 7.2 A Brief History of Liquidity Risk Management 7.3 A Fund Liquidity Risk Framework 7.4 Asset Liquidity 7.4.1 Importance of Data Modeling for Liquidity Risk Management 7.4.2 Asset Liquidity: Days-to-Liquidate 7.4.3 Asset Liquidity: Corporate Bond Transaction Costs (T-Cost) 7.5 Redemption Risk 7.5.1 Managing Redemptions and Outflow Risk 7.6 Liquidity Stress Testing 7.7 Extraordinary Measures 7.8 Fixed Income Data Availability Limitations 7.8.1 Modeling Asset Liquidity 7.8.2 Modeling Redemption-at-Risk 7.8.3 Modeling Liquidity Optimization 7.9 Conclusion Chapter 8: Using Portfolio Optimization Techniques to Manage Risk 8.1 Risk Measurement Versus Risk Management 8.2 Typical Fixed Income Hedges 8.3 Parametric Hedging Techniques 8.4 Generalized Approach to Hedging 8.4.1 Hedging as Constrained Portfolio Optimization 8.4.2 Mathematical Formulation 8.4.2.1 Exposure Hedging 8.4.2.2 Managing a Portfolio to a Benchmark 8.4.2.3 Stress Scenario Hedging 8.4.3 Examples of Optimized Risk Management Strategies 8.4.3.1 Achieving an ESG Tilt While Managing a Fixed Income Portfolio Relative to a Benchmark  8.4.3.2 Hedging Stress Scenario Exposure 8.5 Advanced Portfolio Optimization and Risk Management Techniques 8.5.1 Risk Budgeting/Parity 8.5.2 Going Beyond a Single Fund / Single Period in Portfolio Risk Management 8.5.2.1 Multi-Fund Portfolio Construction and Risk Management 8.5.2.2 Multi-Period Portfolio Construction and Risk Management  8.5.2.3 Risk Management Using Scenario Optimization 8.5.3 Example: Risk Budgeting for Factor-Based Investing Chapter 9: Risk Governance 9.1 Introduction 9.2 Risk Scan Standard Framework 9.3 Risk and Performance Target Framework  9.4 Governance Chapter 10: Risk - Return Awareness Intrinsic Value Unless Special Conditions Hold 17.2.2 Cash and Cash Flow are the Only Robust Sources of Liquidity 17.2.3 Complexity and Opacity Matter More Than You Think 17.2.4 Collateralization Can Be a Two-Edged Sword 17.2.5 Liquidity Is a Common Risk Factor 17.3 Investors in Securitized Products Need to Look Past the Data to the Underlying Behavior of the Assets 17.4 Certification is Useless During Systemic Events 17.5 Market Risk Can Change Dramatically 17.6 The Changing Nature of Market Risk 17.7 By the Time a Crisis Strikes, its too Late to Start Preparing 17.8 Conclusion Chapter 18: Reflections on Buy-Side Risk Management After (or Between) the Storms 18.1 Introduction 18.2 Risk Management Requires Institutional Buy-In 18.3 The Alignment and Management of Institutional Interests 18.4 Getting Risk Takers to Think Like Risk Managers 18.5 Independent Risk Management Organizations 18.6 Clearly Define Fiduciary Obligations 18.7 Bottom-Up Risk Management 18.8 Risk Models Require Constant Vigilance 18.9 Risk Management Does Not Mean Risk Avoidance Chapter 19: Lessons Worth Considering from the COVID-19 Crisis 19.1 Introduction 19.2 Background 19.3 Core Principles Underpinning Recommendations 19.4 March 2020: Capital Markets Highlights and Official Sector Intervention 19.5 COVID-19 Lessons: What Worked and What Needs to be Addressed 19.6 Recommendations to Enhance the Resilience of Capital Markets 19.6.1 Recommendations Regarding Bank Regulations 19.6.2 Recommendations Regarding Market Structure 19.6.2.1 Treasuries 19.6.2.2 Short-Term Markets 19.6.2.3 Fixed Income Markets 19.6.2.4 Central Clearing Counterparties (CCPs) 19.6.2.5 Equities 19.6.2.6 Indices 19.6.2.7 Data 19.6.3 Recommendations Regarding Asset Management 19.7 Concerns with Macroprudential Controls 19.8 Conclusion 19.9 PostScript About the Author(s) Index

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