60,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: Wiley Professional Banking and Finance Series /Wiley Frontiers in Finance
- Sprache: Englisch
An irreplaceable roadmap to modern risk management from renowned experts on the subject Edited by a co-founder and the former Chief Risk Officer of BlackRock--the world's largest asset manager--BlackRock's Guide to Fixed-Income Risk Management delivers an insightful blueprint to the implementation of a comprehensive investment risk management framework for buy-side firms. Leveraging the unprecedented academic and professional experience of current and former senior leaders in BlackRock's risk and portfolio management functions, as well as trading, financial modeling, and analytics experts, the book serves a practitioner's guide to investment risk management, leveraging BlackRock's risk management framework. The included chapters combine to provide chief investment officers, risk managers, portfolio managers, researchers, and compliance professionals an approach to investment risk management well-suited for today's and tomorrow's markets. The book also presents: * Critical elements that underpin a strong risk management program and culture * Fixed income risk management concepts and theories that can be applied to other asset classes * Lessons learned from financial crises and the COVID-19 Pandemic Ideal for undergraduate students and students and scholars of business, finance, and risk management, BlackRock's Guide to Fixed-Income Risk Management is a one-of-a-kind combination of modern theory with proven, practical risk management strategies.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 844
Veröffentlichungsjahr: 2023
Ähnliche
Table of Contents
Cover
Table of Contents
Title Page
Copyright
Frequently Used Abbreviations
Foreword
Preface
ORGANIZATION OF THE BOOK
SECTION I: AN APPROACH TO FIXED‐INCOME INVESTMENT RISK MANAGEMENT
SECTION II: FIXED‐INCOME RISK MANAGEMENT—THEN AND NOW
SECTION III: LESSONS FROM THE CREDIT CRISIS AND CORONAVIRUS PANDEMIC
NOTE
Acknowledgments
NOTE
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 BLACKROCK'S INVESTMENT AND RISK MANAGEMENT APPROACH
1.4 INTRODUCTION TO THE BLACKROCK INVESTMENT RISK MANAGEMENT PARADIGM
NOTES
CHAPTER 2: Parametric Approaches to Risk Management
2.1 INTRODUCTION
2.2 MEASURING INTEREST RATE EXPOSURE: ANALYTICAL APPROACHES
2.3 MEASURING INTEREST RATE EXPOSURE: EMPIRICAL APPROACHES
2.4 MEASURING YIELD CURVE EXPOSURE
2.5 MEASURING AND MANAGING VOLATILITY RELATED RISKS
2.6 MEASURING CREDIT RISK
2.7 MEASURING MORTGAGE‐RELATED RISKS
2.8 MEASURING IMPACT OF TIME
NOTES
CHAPTER 3: Modeling Yield Curve Dynamics
3.1 PROBABILITY DISTRIBUTIONS OF SYSTEMATIC RISK FACTORS
3.2 PRINCIPAL COMPONENT ANALYSIS: THEORY AND APPLICATIONS
3.3 PROBABILITY DISTRIBUTIONS OF INTEREST RATE SHOCKS
NOTES
CHAPTER 4: Portfolio Risk: Estimation and Decomposition
4.1 INTRODUCTION
4.2 PORTFOLIO VOLATILITY AND FACTOR STRUCTURE
4.3 COVARIANCE MATRIX ESTIMATION
4.4 EX ANTE RISK AND VAR METHODOLOGIES
4.5 INTRODUCTION TO RISK DECOMPOSITION
4.6 ALTERNATIVE APPROACHES TO RISK DECOMPOSITION
4.7 RISK DECOMPOSITION USING CTR
4.8 RISK DECOMPOSITION THROUGH TIME
4.9 RISK DECOMPOSITION: SUMMARY
APPENDIX A. EHVAR: IDIOSYNCRATIC RISK ESTIMATION
APPENDIX B. EHVAR: AGGREGATION
NOTES
CHAPTER 5: Market‐Driven Scenarios: An Approach for Plausible Scenario Construction
5.1 INTRODUCTION
5.2 IMPLIED STRESS TESTING FRAMEWORK
5.3 DEVELOPING USEFUL SCENARIOS
5.4 A MARKET‐DRIVEN SCENARIO EXAMPLE: BREXIT
5.5 CONCLUSION
APPENDIX: DECOMPOSITION OF SCENARIO Z‐SCORE
NOTES
CHAPTER 6: A Framework to Quantify and Price Geopolitical Risks
6.1 INTRODUCTION
6.2 SETTING THE SCENE
6.3 BLACKROCK'S FRAMEWORK FOR ANALYZING GEOPOLITICAL RISKS
6.4 GLOBAL TRADE DEEP DIVE
6.5 WHAT IS ALREADY PRICED IN?
6.6 TAKING ACTION
6.7 CAVEATS AND CAUTIONS
NOTES
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
7.5 REDEMPTION RISK
7.6 LIQUIDITY STRESS TESTING
7.7 EXTRAORDINARY MEASURES
7.8 FIXED‐INCOME DATA AVAILABILITY LIMITATIONS
7.9 CONCLUSION
NOTES
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
8.5 ADVANCED PORTFOLIO OPTIMIZATION AND RISK MANAGEMENT TECHNIQUES
NOTES
CHAPTER 9: Risk Governance
9.1 INTRODUCTION
9.2 RISK SCAN STANDARD FRAMEWORK
9.3 RISK AND PERFORMANCE TARGET (RPT) FRAMEWORK
9.4 GOVERNANCE
NOTES
CHAPTER 10: Risk‐Return Awareness and Behavioral Finance
10.1 INTRODUCTION
10.2 PORTFOLIO AND RISK MANAGER PARTNERSHIP
10.3 BEHAVIORAL RISK MANAGEMENT FOR FIXED INCOME
10.4 DECISION‐MAKING ANALYTICS
10.5 INVESTMENT PROCESS
10.6 CONCLUSION
NOTES
CHAPTER 11: Performance Attribution
11.1 INTRODUCTION
11.2 BRINSON ATTRIBUTION AND BEYOND
11.3 FACTOR‐BASED ATTRIBUTION
11.4 EQUITY FUNDAMENTAL FACTOR‐BASED ATTRIBUTION
NOTES
CHAPTER 12: Performance Analysis
12.1 INTRODUCTION
12.2 PERFORMANCE GOVERNANCE
12.3 PERFORMANCE METRICS
12.4 CONCLUSION
NOTES
CHAPTER 13: Evolving the Risk Management Paradigm
13.1 INTRODUCTION
13.2 TRADITIONAL BUY‐SIDE RISK MANAGEMENT FRAMEWORK
13.3 EVOLVING THE IRMP: IN PURSUIT OF INVESTMENT RISK MANAGEMENT AT SCALE
13.4 RISK GOVERNANCE
13.5 SUPPORTING RISK GOVERNANCE THROUGH TECHNOLOGY
13.6 IMPLEMENTING A RISK GOVERNANCE FRAMEWORK THROUGH ALADDIN
13.7 ALADDIN'S RISK RADAR EXAMPLE
13.8 CONCLUSION
NOTES
SECTION II: Fixed‐Income Risk Management—Then and Now
CHAPTER 14: The Modernization of the Bond Market
14.1 CHARTING THE EVOLUTION OF BOND MARKETS
14.2 THE DEVELOPMENT OF AN INDEX‐BASED ECOSYSTEM
14.3 IMPLICATIONS FOR INVESTING, PORTFOLIO MANAGEMENT, AND RISK MANAGEMENT
14.4 THE FUTURE STATE OF PORTFOLIO CONSTRUCTION
14.5 CONCLUSION
NOTES
CHAPTER 15: The LIBOR Transition
15.1 INTRODUCTION
15.2 IMPLICATIONS TO PORTFOLIO AND RISK MANAGEMENT
15.3 SHIFT FROM LIBOR TO SOFR
15.4 RISK MANAGEMENT IMPACT AND COORDINATION
15.5 REFLECTIONS ON A BENCHMARK REFORMS
NOTES
CHAPTER 16: Derivatives Reform: The Rise of Swap Execution Facilities and Central Counterparties
16.1 THE CALL FOR CHANGE: 2008 GLOBAL FINANCIAL CRISIS
16.2 THE VALUE OF DERIVATIVES IN FIXED‐INCOME PORTFOLIOS
16.3 TRADING FIXED‐INCOME DERIVATIVES: THE RISE OF SEFs
16.4 CLEARING FIXED‐INCOME DERIVATIVES: THE RISE OF CCPs
16.5 CCP RISK MITIGATION TECHNIQUES
16.6 THE CALL FOR CHANGE: MARKET PARTICIPANTS ASK FOR STRONGER CCPs
16.7 CONCLUSION
NOTES
SECTION III: Lessons from the Credit Crisis and Coronavirus Pandemic
CHAPTER 17: Risk Management Lessons Worth Remembering from the Credit Crisis of 2007–2009
17.1 INTRODUCTION
17.2 THE PARAMOUNT IMPORTANCE OF LIQUIDITY
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, IT'S TOO LATE TO START PREPARING
17.8 CONCLUSION
NOTES
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
NOTES
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
19.7 CONCERNS WITH MACROPRUDENTIAL CONTROLS
19.8 CONCLUSION
19.9 POSTSCRIPT
NOTES
Bibliography
About the Website
About the Editor
About the Contributors
Index
End User License Agreement
List of Tables
Chapter 2
TABLE 2.1 Duration Comparison Report for a Sample Portfolio (as of 1/31/2020...
TABLE 2.2 Effect of Parallel Shock Size on OAD Estimates (as of 1/31/2020)
TABLE 2.3 Duration Drifts of Selected Securities in Various Interest Rate En...
TABLE 2.4 Interest Rate Scenario Analysis Report for a Sample Portfolio (as ...
TABLE 2.5 Parametric Risk Measures for Generic 30‐Year MBSs (as of 1/31/2020...
TABLE 2.6 Key Rate Duration (KRD) Report for a Sample Portfolio (as of 1/31/...
TABLE 2.7 HROR Scenario Analysis for the Sample Portfolio (as of 1/31/2020)...
TABLE 2.8 HROR Scenario Analysis of a 1.47% 6M x 10‐Year Right‐to‐Receive Sw...
TABLE 2.9 Interest Rate Shocks Exceeding Given Thresholds: Cumulative Probab...
Chapter 3
TABLE 3.1 Correlations and Volatilities of US Key Rates from Aladdin Daily D...
TABLE 3.2 Correlations and Volatilities of US Key Rates from Aladdin Daily D...
TABLE 3.3 Principal Components Analysis of Spot Curve Movements (as of 1/31/...
TABLE 3.4 Principal Components Analysis of Spot Curve Movements (as of 3/16/...
Chapter 4
TABLE 4.1 Factor Model Taxonomy
TABLE 4.2 Portfolio Risk Estimation Use Cases: Modeling Priorities
TABLE 4.3 Portfolio Risk Forecasts: Estimation Choices
Chapter 6
TABLE 6.1 A History of Geopolitical Crises
TABLE 6.2 A What‐If Scenario
TABLE 6.3 More Shocks
Chapter 7
TABLE 7.1 Liquidity Risk Management Elements
TABLE 7.2 Improved Transaction Data Availability
TABLE 7.3 Key Elements of a Typical Redemption “Waterfall” for US Retail Fun...
TABLE 7.4 Examples of Extraordinary Measures
Chapter 8
TABLE 8.1 Bond ETF Strategies
TABLE 8.2 Inputs for Inflation Rise Stress Test Scenario
TABLE 8.3 Portfolio Holdings Following Optimization
TABLE 8.4 Volatility and Correlation Among Factors
TABLE 8.5 Portfolio Risk, Weight, and Risk Concentrations for All Strategies...
Chapter 10
TABLE 10.1 Framework for Evaluating Behavioral Aspects of Investment Process...
TABLE 10.2 Ego‐Protective Barriers to Learning from Mistakes
Chapter 11
TABLE 11.1 Security Return Breakdown by Factors
TABLE 11.2 Active Portfolio Return Contribution by Security
TABLE 11.3 Active Portfolio Return Contribution by Sector
TABLE 11.4 Five‐Stock Portfolio with Apple as the Benchmark
TABLE 11.5 Market‐Value Factor‐Based Attribution
TABLE 11.6 Beta‐Adjusted Factor‐Based Attribution
List of Illustrations
Chapter 2
EXHIBIT 2.1 Examples of Price Dependencies on Changes in Interest Rates (as ...
EXHIBIT 2.2 Examples of Price Dependencies on Changes in Interest Rates (as ...
EXHIBIT 2.3 Coupon Curves of Generic 30‐Year MBS (as of 1/31/2020)
EXHIBIT 2.4 OAS Curves of Generic 30‐Year MBS (as of 1/31/2020)
EXHIBIT 2.5 Key Rate Duration (KRD) Profiles of Selected Instruments (as of ...
EXHIBIT 2.6 Difference Between the Classical Triangular Shock and the Wave S...
EXHIBIT 2.6a Sample Triangular Shock at the 7‐Year Point
EXHIBIT 2.6b Sample Wave Shocks and Their Difference
EXHIBIT 2.7 Difference in Implied Forward Rates
EXHIBIT 2.7a Triangular Shock Implied Forward Rates
EXHIBIT 2.7b Wave Shock Implied Forward Rates
EXHIBIT 2.8 At‐the‐Money Implied Rate Volatility Surface (in Normal BP)
EXHIBIT 2.9 Long Straddle Payout Versus Long Strangle Payout
EXHIBIT 2.10 Gamma Versus Delta
EXHIBIT 2.11 Adding Skew
Chapter 3
EXHIBIT 3.1 Annualized Principal Components Spot Curve Shocks (as of 1/31/20...
EXHIBIT 3.2 Transition from One Coordinate System to Another: Interdependent...
EXHIBIT 3.3 The First Principal Component Shock and TSOV (as of 1/31/20)
EXHIBIT 3.4 Relationship Between Changes in the Level and the Slope of the U...
EXHIBIT 3.5 Relationship Between Changes in the Level and the Slope of the U...
EXHIBIT 3.6 Interest Rate Shocks of the Same Shape
Chapter 4
EXHIBIT 4.1 Multi‐Asset Portfolio Daily Returns: Normal Versus Empirical...
EXHIBIT 4.2 Multi‐Asset Portfolio Daily Return Time Series: January 2001–Nov...
EXHIBIT 4.3 Impact of Half‐Life on Multi‐Asset Portfolio Risk Estimates...
EXHIBIT 4.4 Cross-Sectional Scaling
EXHIBIT 4.5 Analytical VaR
EXHIBIT 4.6 Historical VaR
EXHIBIT 4.7 Enhanced Historical VaR
EXHIBIT 4.8 Enhanced Historical VaR with Cross‐Sectional Adjustments
EXHIBIT 4.9 Marginal Contribution to Risk
EXHIBIT 4.10 Risk Decomposition Measures for a Sample Portfolio
EXHIBIT 4.11 Sector Contributions
EXHIBIT 4.12 Hierarchy of Factor Blocks
EXHIBIT 4.13 Factor Block Contribution
EXHIBIT 4.14 Atomic Contributions
EXHIBIT 4.15 Security‐Level X‐Sigma‐Rho Decomposition
EXHIBIT 4.16 Factor Block ANOVA Report
EXHIBIT 4.17 Economy–Exposure Risk Decomposition
EXHIBIT 4.18 Risk Time‐Series for HY Index
EXHIBIT 4.19 High‐Yield Skewed‐
t
(dark gray line) Versus Normal (light gray ...
EXHIBIT 4.20 95% and 99% VaR and ES Idiosyncratic Tail Risk Multipliers at D...
Chapter 5
EXHIBIT 5.1 “Risk Ruler” of Scenario Z‐Scores
EXHIBIT 5.2 From Scenario Z‐Scores to a Likelihood Measure
EXHIBIT 5.3 Empirical Distribution of Scenario Z‐Scores
EXHIBIT 5.4 Empirical Exceedance Probability—Two‐Factor Example
EXHIBIT 5.5 Changes in the Probability of Risk‐Off Scenario A Through Time...
EXHIBIT 5.6 Robustness of Scenario Plausibility
EXHIBIT 5.7 Soft Brexit Scenario Policy Variable Selection
EXHIBIT 5.8 Plausibility Ruler with Brexit Scenarios
EXHIBIT 5.9 Soft Brexit Scenario Z‐Score
EXHIBIT 5.10 Soft Brexit Perturbation Vector
EXHIBIT 5.11 Soft Brexit P&L
Chapter 6
EXHIBIT 6.1 Shock Waves
EXHIBIT 6.2 Pricing In and Out
EXHIBIT 6.3 Risks and Impacts
EXHIBIT 6.4 Stress Test
EXHIBIT 6.5 Underwater
EXHIBIT 6.6 Tied to Trade Tensions
Chapter 7
EXHIBIT 7.1 Annual US Corporate Bond Issuance
EXHIBIT 7.2 US High‐Yield and Investment‐Grade Corporate Bond Market Turnove...
EXHIBIT 7.3 An Effective Fund Liquidity Risk Management Framework
EXHIBIT 7.4 US Investment‐Grade Trading Volumes
EXHIBIT 7.5 HY and IG Capped and Uncapped Trade Volumes in 2019
EXHIBIT 7.6 Fixed‐Income Bond Latent Liquidity
EXHIBIT 7.7 Liquidity Optimization—Balancing Risk Profile, Liquidation Time,...
Chapter 8
EXHIBIT 8.1 Applying Hedges to Isolate Risk
EXHIBIT 8.2 Illustration of the Risk Versus Transaction Cost Trade‐Off
EXHIBIT 8.3 Portfolio Summary Screen
EXHIBIT 8.4 Portfolio Optimization Results
EXHIBIT 8.5 ESG Versus Active Risk for a 150 Trade Rebalance and for a Rebal...
EXHIBIT 8.6 Stress Test Loss Reduction
EXHIBIT 8.7 Weight Allocation for Different Strategies
EXHIBIT 8.8 Risk Allocation for Different Strategies
Chapter 9
EXHIBIT 9.1 An Example of Risk Zone Ranges
EXHIBIT 9.2 An Example of RPT
Chapter 10
EXHIBIT 10.1 Prospect Theory Value Function
EXHIBIT 10.2 Case Studies on the Disposition Bias
EXHIBIT 10.3 The Endowment Effect in Investment Grade Credit Selection
EXHIBIT 10.4 Network Configurations for Evaluating Decentralization
Chapter 11
EXHIBIT 11.1 Market Value Brinson Attribution
EXHIBIT 11.2 Market Value Brinson Attribution
EXHIBIT 11.3 Sample Portfolio and Benchmark Weights
EXHIBIT 11.4 Brinson Attribution in Sample Portfolio
EXHIBIT 11.5 Beta‐adjusted Attribution
EXHIBIT 11.6 Beta‐adjusted Attribution
EXHIBIT 11.7 Brinson Attribution
EXHIBIT 11.8 Beta‐adjusted Attribution
EXHIBIT 11.9 Brinson Attribution
EXHIBIT 11.10 Beta‐adjusted Attribution
EXHIBIT 11.11 Factor-Based Attribution. This Exhibit 11.11 comprises of Tabl...
EXHIBIT 11.12 Market‐Value Factor‐Based Attribution
EXHIBIT 11.13 Beta‐Adjusted Factor‐Based Attribution
Chapter 13
EXHIBIT 13.1 An Example of Task Detail
EXHIBIT 13.2 An Example of Exception Classification
Chapter 14
EXHIBIT 14.1 US High‐Yield and Investment‐Grade Corporate Bond Market Turnov...
EXHIBIT 14.2 Investment‐Grade and High‐Yield Dealer Positioning Versus Marke...
EXHIBIT 14.3 Share of the Top‐Decile Most‐Traded Bonds as Percentage of Tota...
EXHIBIT 14.4 US Investment‐Grade and High‐Yield Markets—Average Trade Size E...
EXHIBIT 14.5 Evolution of Market Structure
EXHIBIT 14.6 Growth in Electronic Trading
EXHIBIT 14.7 MarketAxess US Corporate Trading Volumes as Percentage of TRACE...
EXHIBIT 14.8 MarketAxess Open Trading Volumes as Percentage of Total Volume...
EXHIBIT 14.9 US Fixed‐Income ETF Average Daily Volume (ADV)
EXHIBIT 14.10 US Fixed‐Income ETF Average Daily Volume (ADV) by Sector
EXHIBIT 14.11 High‐Yield (HY) and Investment‐Grade (IG) Corporate ETFs Tradi...
EXHIBIT 14.12 Portfolio Trade Example
Chapter 15
EXHIBIT 15.1 Alternative Reference Rates
EXHIBIT 15.2 Linear Swaps
EXHIBIT 15.3 3M LIBOR Versus SOFR Versus SOFR Plus Spread Parent Leg Duratio...
EXHIBIT 15.4 LIBOR vs SOFR vs SOFR Plus Spread Floating Leg Durations
Chapter 16
EXHIBIT 16.1 BIS OTC FI Swaps
EXHIBIT 16.2 SEF Execution (Q3 2022)
EXHIBIT 16.3 Bilateral Derivatives Market
EXHIBIT 16.4 CCP Role in a Trade
EXHIBIT 16.5 CCP Default Waterfall
EXHIBIT 16.6 Default Fund Member Contributions (Q4 2021)
Chapter 17
EXHIBIT 17.1 Yale Endowment Asset Allocation
EXHIBIT 17.2 Fixed‐Income Monthly Return Moments, August 31, 1999–September ...
EXHIBIT 17.3 AIG and the Two‐Edged Sword of Collateral
EXHIBIT 17.4 A$/¥ Carry Trade Cumulative Returns and Bid–Ask Spread
EXHIBIT 17.5 Growth in Issuance of Securitized Assets
EXHIBIT 17.6 CWHL 2007‐HY7 Pool and Loan Group Characteristics
EXHIBIT 17.7 CWHL 2007‐HY7 Collateral Performance
EXHIBIT 17.8 Deteriorating Quality of Underlying Assets
EXHIBIT 17.9 Pre–Credit Crisis AAA Ratings by Security Type
EXHIBIT 17.10 Risk Appetite Index Components
EXHIBIT 17.11 Short‐Term Risk Appetite Index, 3‐Month Horizon
EXHIBIT 17.12 Active Risk for Constant Exposure Portfolio, January 2005–Octo...
EXHIBIT 17.13 Average Monthly CPR, January 2009–September 2009
Chapter 19
EXHIBIT 19.1 Mutual Funds in the United States: Just the Tip of the Iceberg...
EXHIBIT 19.2 Selected Official Sector Programs Announced in March and April,...
EXHIBIT 19.3 Take‐Up of Federal Reserve Facilities
EXHIBIT 19.4 US Futures Commission Merchant (FCM) Required Customer Funds: F...
EXHIBIT 19.5 US FCM Required Customer Funds
EXHIBIT 19.6 Largest US High‐Yield Bond ETF Versus Cboe Volatility Index
EXHIBIT 19.7 Divergence Between Investment‐Grade ETF Price and NAV
EXHIBIT 19.8 March 2020 Weekly Liquidity Levels in LVNAV MMFs (in Aggregate ...
EXHIBIT 19.9 High‐Yield Bond Flows: Aggregate Outflows and Average Percentag...
EXHIBIT 19.10 Fixed‐Income Index Rebalancing Decisions, Month‐End March 2020...
Guide
Cover Page
Title Page
Copyright
Praise Page
Frequently Used Abbreviations
Foreword
Preface
Acknowledgments
Table of Contents
Begin Reading
Bibliography
About the Website
About the Editor
About the Contributors
Index
Wiley End User License Agreement
Pages
iii
iv
v
xvii
xviii
xix
xx
xxi
xxii
xxiii
xxiv
xxv
xxvi
xxvii
xxviii
xxix
xxxi
xxxii
xxxiii
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
279
280
281
282
283
284
285
286
287
288
289
290
291
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
373
374
375
376
377
378
379
380
381
382
383
385
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
BlackRock's Guide to Fixed‐Income Risk Management
BENNETT W. GOLUB, Editor
Copyright © 2024 by BlackRock, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per‐copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750‐8400, fax (978) 750‐4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748‐6011, fax (201) 748‐6008, or online at http://www.wiley.com/go/permission.
Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762‐2974, outside the United States at (317) 572‐3993 or fax (317) 572‐4002.
Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com.
Library of Congress Cataloging‐in‐Publication Data:
Names: Golub, Bennett W., author. | John Wiley & Sons, publisher.
Title: BlackRock's guide to fixed-income risk management / Bennett W. Golub.
Description: Hoboken, New Jersey : Wiley, [2023] | Includes index.
Identifiers: LCCN 2023007920 (print) | LCCN 2023007921 (ebook) | ISBN 9781119884873 (hardback) | ISBN 9781119884897 (adobe pdf) | ISBN 9781119884880 (epub)
Subjects: LCSH: Financial risk management. | Fixed-income securities—Risk management. | Investments—Risk management.
Classification: LCC HD61 .G644 2023 (print) | LCC HD61 (ebook) | DDC 658.15/5—dc23/eng/20230505
LC record available at https://lccn.loc.gov/2023007920
LC ebook record available at https://lccn.loc.gov/2023007921
Cover Design: Wiley and BlackRock
Cover Image: © Liyao Xie/Getty Images
“And, of course, stability isn't nearly so spectacular as instability.”∼Aldous Huxley, Brave New World
Frequently Used Abbreviations
ABS
Asset‐Backed Securities
ADV
Average Daily Volume
ANOVA
Analysis of Variance
AP
Authorized Participant
APG
Aladdin Product Group
ARRC
Alternative Reference Rates Committee
ARRs
Alternative Reference Rates
ATM
At‐the‐Money or Automated Teller Machine
ATR
Alpha Target Ratio
AUM
Assets Under Management
AVaR
Analytical VaR
BGRI
BlackRock Geopolitical Risk Indicator
BLK
BlackRock
BoE
Bank of England
BP
Basis Point
BSRMF
Buy Side Risk Managers Forum
BWIC
Bids Wanted in Competition
CCD
Coupon Curve Duration
CCost
Cost of Carry
CCP
Central Counterparty
CDF
Cumulative Distribution Function
CDS
Credit Default Swap
CDX
Credit Default Swap Index
CIO
Chief Investment Officer
CLO
Collateralized Loan Obligation
CM
Clearing Member
CMBS
Commercial Mortgage‐Backed Securities
CMRA
Capital Market Risk Advisors
CP
Commercial Paper
CPR
Conditional Prepayment Rate
CRO
Chief Risk Officer
CSA
Collateral Support Agreement
CTF
Collective Trust Fund
CTR
Contribution to Risk
CVaR
Conditional VaR
DxS
Duration Times Spread
EHVaR
Enhanced HVaR
EM
Emerging Markets
EMH
Efficient Market Hypothesis
ERM
Exchange Rate Mechanism
ES
Expected Shortfall
ESG
Environmental, Social, and Governance
€STR
Euro Short‐Term Rate
ETD
Exchange‐Traded Derivative
ETF
Exchange‐Traded Fund
ETP
Exchange‐Traded Product
EWMA
Exponentially Weighted Moving Average
FCA
Financial Conduct Authority
FCM
Futures Commission Merchant
FINRA
Financial Industry Regulatory Authority
FSB
Financial Stability Board
FSOC
Financial Stability Oversight Council
FX
Foreign Exchange
GDP
Gross Domestic Product
GFC
Global Financial Crisis
GNMAII MBS
Ginnie Mae Mortgage‐Backed Security
HF
Hedge Fund
HRaR
Historical Redemption‐at‐Risk
HROR
Horizon Rate of Return
HVaR
Historical VaR
HY
High Yield
IBORs
Interbank Offered Rates
ICTR
Incremental Contribution to Risk
IG
Investment Grade
IM
Initial Margin
IMA
Investment Management Agreement
IOSCO
International Organization of Securities Commissions
IR
Information Ratio
IRMP
Investment Risk Management Paradigm
ISDA
International Swaps and Derivatives Association
ITM
In‐the‐Money
ITRM
Idiosyncratic Tail Risk Multiplier
KRBC
Key Rate Bucket Convexities
KRD
Key Rate Duration
LCR
Liquidity Coverage Ratio
LDI
Liability‐Driven Investment
LIBOR
London Interbank Offered Rate
LTV
Loan to Value
LVNAV
Low Volatility Net Asset Value
MBS
Mortgage‐Backed Securities
MCTR
Marginal Contribution to Risk
MD
Mahalanobis Distance
MDS
Market‐Driven Scenario
MiFID II
Markets in Financial Instruments Directive
MMF
Money Market Fund
MPO
Multi‐Period Optimization
MRAC
Market Risk Advisory Committee
MSQE
Mean‐Squared Error
MTB
Mortgage/Treasury Basis
MWCB
Market Wide Circuit Breaker
NAV
Net Asset Value
NBFI
Non‐Bank Financial Intermediation
OAC
Option‐Adjusted Convexity
OAD
Option‐Adjusted Duration
OAS
Option‐Adjusted Spread
OAV
Option‐Adjusted Value
OCC
Office of the Comptroller of the Currency
OIS
Overnight Index Swaps
OLS
Ordinary Least Squares
OTC
Over‐the‐Counter
OTM
Out‐of‐the‐Money
OTR
On‐the‐Run
OTS
Office of Thrift Supervision
OWIC
Offers Wanted in Competition
P&L
Profit and Loss
PCA
Principal Components Analysis
PEPP
Pandemic Emergency Purchase Programme
PFMI
Principles for Financial Market Infrastructure
PQD
Public Quantitative Disclosure
PROC
Portfolio Risk Oversight Committee
PRV
Purchase Redemption Value
PTF
Proprietary Trading Firm
REITs
Real Estate Investment Trusts
RFQ
Request for Quote
RFR
Risk‐Free Rates
RPT
Risk and Performance Targets
RQA
Risk & Quantitative Analysis
SAR
Standalone Risk
SARON
Swiss Average Rate Overnight
SEC
Securities and Exchange Commission
SEF
Swap Execution Facility
SOCP
Second Order Conic Programming
SOFR
Secured Overnight Financing Rate
SONIA
Sterling Overnight Index Average
SPACs
Special Purpose Acquisition Companies
STRM
Systematic Tail Risk Multiplier
T‐cost
Transaction Cost
TONA
Tokyo Overnight Average Rate
TRACE
Trade Reporting and Compliance Engine
TRS
Total Return Swaps
TSOV
Term Structure of Volatility
TSY
Treasury
UCITS
Undertakings for Collective Investment in Transferable Securities
UMBS
Uniform Mortgage‐Backed Securities
UST
US Treasury
VaR
Value at Risk
VM
Variation Margin
WFH
Work from Home
WoW
Week-over-Week
ZV0
Zero Volatility Spread
Foreword
Back in the distant days of the mid‐1980s I began my career focused on equity markets. Over the next two decades, the abundance of price and volume data, along with the relatively limited set of variables required to describe equity risk, led to the development of fundamental and statistical equity risk models that are now ubiquitous across the asset management industry.
In due course it became obvious to me that in order to understand investment risk in all its forms, some knowledge of fixed‐income markets and products was also necessary. Many years before my association with BlackRock, I came across a book, Risk Management: Approaches for Fixed Income Markets (2000) by Bennett W. Golub and Leo M. Tilman. I remember reading it with some fascination. The statistical constructs were, at least in part, similar to those used in equity markets. However, the markets themselves were very different. Nonetheless, the rigor of the discussions and the practicality of the contents were incredibly useful to me back then.
Some years later Ben Golub and I became colleagues when BlackRock acquired Merrill Lynch Investment Management, where I was then the head of its Risk & Quantitative Analysis group in London. This brought me for the first time into contact with BlackRock's fixed‐income analytics and risk management teams. My equities background combined with BlackRock's fixed‐income expertise made for a good match, and I was appointed the co‐head with Ben Golub of a merged Risk & Quantitative Analysis group.
Over the subsequent 16 years, much has taken place in both markets and risk management. The contrasts between equities and fixed‐income investing were, in many respects, palpable. Some of the characteristics of fixed‐income securities became clearer to me, such as the limited trading and even more limited price data, the lags in the availability of the limited amount of pricing data, the astounding number of individual securities, and the nonstandard terms and conditions of securities. Real‐world risk management meant making necessary compromises from the equity ideal.
Currently, changes and reforms are moving fixed‐income markets somewhat closer to the more idealized equity markets, making certain types of analyses that were routine in the equity markets partially available for fixed‐income securities.
The last 16 years have also contained some major market events, including the Global Financial Crisis and the Coronavirus pandemic, that challenged the notion that even exchange markets operate efficiently all the time. Some of the later chapters in this book that focus on financial crises contain lessons that we learned that are genuinely worth remembering.
This book introduces the notion of an Investment Risk Management Paradigm (IRMP). This is particularly useful as a reminder of the need to enforce consistent levels of rigor across all of the firm's investment processes. Having a formal notion of this standardization has been extremely useful, especially when investment processes change or activities are added.
History will not be kind to some notions of risk management that turn out to be formalized manifestations of self‐delusion. Other analytics or frameworks will better last the test of time. But while I am certain that some of the notions described in this book will eventually need to change, just as many were eventually edited out of the first edition, many will stand the test of time. This book presents techniques and practices that are actually used and are actually useful. I recommend it to investors and risk managers who wish to get an insight into how investment risk management is contemporaneously practiced at a major global multi‐investment process asset manager.
Ed Fishwick
Chief Risk Officer
BlackRock, Inc.
Summer 2023
Preface
Changing market dynamics, technological advances, and geopolitical stresses have transformed investment risk management. As new and bespoke products have emerged, new risks and additional complexities have driven advances in risk management processes and analytics. Consider, for example, all the forced and rapid innovations that arose from the Coronavirus pandemic. Given the abundance of change, risk managers have had to adapt their processes and tools to address market turbulence, structural bond market changes, product complexity, and increased regulatory oversight. Additionally, risk managers have learned to take advantage of some of these technological advances, resulting in better analytics and the ability to analyze bigger and broader data sets. An intellectually curious risk management culture, coupled with rigorous risk management processes and technological competence, facilitates risk managers' ability to rapidly and effectively adapt to new circumstances.
Recognizing the dynamic nature of investment risk management informs us that it is almost certain that some of the ideas and methodologies presented in this book will inevitably become obsolete themselves. Similarly, there inevitably are important omissions, either intentionally due to the limitations of time or are outside the wisdom and firsthand knowledge of the authors.
This book is a heavily edited and expanded edition of Risk Management: Approaches for Fixed Income Markets (2000) by Bennett W. Golub and Leo M. Tilman (the first edition). In the 23 years following the original English language publication, much has happened to reshape the investment risk management landscape. For example, the heightened attention by markets and investors to ESG (environmental, social, and governance) characteristics was never envisioned when the original book was published. The definitive book on ESG risk management has probably not been written yet; this book intentionally omits much of that topic, while the profession awaits a conclusive state‐of‐the‐practice volume. Also, while currently a hot topic and one that certainly has demonstrated the ability to generate multiple risk management failures, this book is intentionally silent on the risk management of cryptocurrencies; we await those markets' risk management processes maturing.
The technological intensity of investment risk management has increased dramatically. Twenty‐three years ago, technology was not necessarily at the forefront of most investment management firms. Now, technology is one of the critical success factors—allowing firms to continue to evolve to meet clients' needs, respond to market changes and regulatory requirements, and create operational efficiencies and scale. With improvements in technology, firms are now able to perform tasks that were previously technologically impossible or would take too much time or too much expensive hardware to be useful. When editing sections of the original book for inclusion in this new edition, references to analytic techniques that involved compromising accuracy for computational efficiency were removed from the manuscript; there is much less need to compromise precision for the sake of economizing computational resources. The implications of Moore's Law, broadly speaking, continue to make more and more computational resources economical. Cloud‐based applications, for example, can summon massive amounts of computational power on demand. Organizations can store, analyze, manipulate, and synthesize what would have been unimaginable amounts of data more cheaply and quickly than ever before. The sophisticated and creative use of technology, has become an essential part of effective investment risk management. Having the right technology to manage risk is no longer a luxury; it is a necessity.
BlackRock's commitment to innovation and the use of technology has been one of the key drivers of its ongoing robust growth. Developing and evolving Aladdin and leveraging technology has been part of BlackRock's founding vision and has enabled the firm to become a massive scale operator, highly efficient, integrated, and dynamic. As the firm grows and technology evolves, Aladdin continues to be a best‐in‐class solution, used by BlackRock to operate efficiently at scale. The same Aladdin technology used by BlackRock is also available and heavily used by many major global financial institutions.
The motivation for this book originated due to an inquiry from a BlackRock client about when the first edition would be revised. Initially, this seemed to be a relatively straightforward task, and the project was initiated in 2017. At first, the plan was to update data, tables, and exhibits and remove topics that were no longer relevant. However, after this editing process, it became apparent that simply updating the data and removing obsolete sections would be sorely inadequate given how much markets, products, and risk management have evolved. Instead, it became clear that if the original book were to be properly updated, a substantial expansion of the topics covered would be required. That, of course, made creating the second edition a materially greater task. Given my then current role as chief risk officer of BlackRock, this task would have been beyond my ability to complete. In 2018, I concluded that if we expanded the scope of the book, the only feasible path forward was to ask my fellow BlackRock colleagues to contribute their expertise and enthusiasm and author or coauthor the needed chapters. Unlike the original book, which was written as a unified whole in a single voice, the new edition would include chapters on a wide range of topics written by many authors.
Bringing together BlackRock's leaders in risk management, portfolio management, trading, financial modeling, psychology, and analytics, this successor book to the first edition, now titled BlackRock's Guide to Fixed-Income Risk Management, represents a combination of revised and updated chapters from the original book and a collection of new standalone chapters covering a range of investment risk management topics. Each chapter has been authored by BlackRock's current or former senior subject matter experts. While the book focuses primarily on fixed‐income practices, analytics, and models, many of the concepts presented are equally meaningful in a multi‐asset context. This book can first be considered a practitioner's guide to fixed‐income risk management, leveraging BlackRock's overall investment risk management framework for operating a viable risk management program at scale, heterogeneity, and complexity.
ORGANIZATION OF THE BOOK1
This book is organized into three sections and covers the following themes:
An Approach to Fixed‐Income Investment Risk Management
Fixed‐Income Risk Management—Then and Now
Lessons from the Credit Crisis and Coronavirus Pandemic
SECTION I: AN APPROACH TO FIXED‐INCOME INVESTMENT RISK MANAGEMENT
In Section I, we describe the pillars of BlackRock's Investment Risk Management Paradigm (IRMP). This paradigm evolved over many years as a tool to bring consistency and structure to BlackRock's risk management activities across its various investment management businesses to ensure that risks are properly identified, measured, governed, and reconciled with actual performance. The IRMP rests upon on the following five pillars:
Ex ante risk measurement
Risk governance (i.e., having and maintaining agreed upon levels of risks)
Portfolio manager risk-return awareness
Performance attribution
Performance analysis
Each component of the paradigm is discussed in detail in one or more of the following chapters. Given the various risks to which portfolios are exposed and the diversity of measurements available, several chapters expand upon the first pillar, ex ante risk measurement. Examples and case studies are incorporated to help illustrate the risk management approaches and analytics presented.
Chapter 1 provides an overview of risk management at BlackRock and discusses several elements that underpin a strong risk management program. The chapter reinforces the importance of governance and oversight and introduces BlackRock's approach to investment risk management. To be clear, though, establishing a comprehensive and pervasive risk management program and culture requires commitment and support from all levels of an organization, starting with senior management. My colleagues and I were fortunate to be able to develop our ideas and methodologies in such an environment. This chapter was coauthored by myself and Rick Flynn, managing director in the Risk & Quantitative Analysis group.
Chapter 2 presents parametric approaches to risk management and was initially included in the first edition. This chapter aligns with the first pillar of BlackRock's IRMP, ex ante risk measurement. This chapter includes a discussion of analytical and empirical durations, partial durations, interest rate scenario analysis, and horizon rate of return analysis. The parametric measures of market risk continue to form the backbone of more elaborate and compressive risk methodologies and techniques, which is why we felt that it was an important chapter to revisit and revise in this edition. This chapter has been updated with more recent data. It also includes additional concepts, such as duration times spread (DxS), authored by David Greenberg, former managing director in Technology & Operations—Artificial Intelligence (AI) Labs. Additionally, we added a new section on option usage in portfolio management, which was authored by Jack Hattem, managing director in the Portfolio Management Group. Yury Krongauz, managing director in the Financial Modeling Group, included additional details regarding wave shocks to the Key Rate Duration section. This chapter was updated by Matthew Wang, managing director in the Fundamental Fixed Income Portfolio Management Group.
Chapter 3 reviews the dynamics of interest rate shocks and was also previously published in the first edition. The concepts in this chapter are also part of the first pillar of BlackRock's IRMP and contain an introduction to principal component analysis as well as an investigation of the probability distribution of interest rate shocks. In this chapter, the relationship between the first principal component and the term structure of volatility is explored and the results are applied to the study of big market move days as well as the historical steepeners and flatteners of the US Treasury curve. This chapter was updated by Matthew Wang.
Chapter 4 focuses on estimating and decomposing portfolio risk and also aligns with the first pillar of BlackRock's IRMP. This chapter reviews portfolio volatility estimation and factor structure, along with the empirical challenges associated with estimating covariance matrices. It contains an overview of Value at Risk (VaR) estimation, including a focus on Enhanced Historical VaR (EHVaR), which is a proprietary approach developed for modeling the forward distribution of asset returns. EHVaR blends the advantages of both parametric and nonparametric forecasting techniques. Finally, the chapter discusses decomposition of realized risk and return. This chapter was coauthored by Amandeep Dhaliwal, managing director in the Financial Modeling Group, along with Tom Booker, director in the Financial Modeling Group.
Chapter 5 introduces the Market‐Driven Scenarios (MDS) framework, which is designed to provide structure to the often subjective and ad hoc nature of hypothetical scenario generation. Macroeconomic fundamentals typically drive the general direction of financial market returns. However, tail risks, which can be triggered by geopolitical events, can arise that are difficult to forecast but can have significant adverse effects on fund returns. As an element of the first pillar, this chapter highlights the use of specific econometric techniques and the application of a disciplined multistep process to create Market‐Driven Scenarios. The MDS process is inherently multi‐asset versus being particularly fixed‐income oriented. This chapter was coauthored by myself, David Greenberg, and Ronald Ratcliffe, managing director in the Analytics & Quantitative Solutions team within BlackRock Solutions.
Chapter 6 uses the MDS framework to analyze geopolitical risks and assess their potential market impact in a systematic way. The chapter reviews market responses to unexpected historical geopolitical shocks from 1962–2019. Using one of the top geopolitical risks from 2019 as an example, this chapter demonstrates the application of the MDS framework. This chapter aligns with the first pillar of the IRMP and reinforces the importance of scenario analysis and stress testing portfolios. It was coauthored by Catherine Kress, director and head of Geopolitical Research & Strategy within the BlackRock Investment Institute; Carl Patchen, former vice president in the Risk & Quantitative Analysis group; Ronald Ratcliffe; Eric Van Nostrand, former managing director in the BlackRock Sustainable Investment group; and Kemin Yang, former associate in the BlackRock Investment Institute. Additional contributors include myself, Tom Donilon, chairman of the BlackRock Investment Institute; and Isabelle Mateos y Lago, global head of BlackRock's Official Institutions group.
Chapter 7 presents some approaches for measuring liquidity risk, one of the many investment risks that demands rigorous and continuous oversight. While liquidity risk can have different meanings, this chapter focuses on fund liquidity risk. As a component of the first pillar, this chapter contains a brief history of how liquidity risk management has evolved and covers the various elements of a liquidity risk management framework, including asset liquidity, redemptions, and extraordinary measures. This chapter was coauthored by myself; Philip Sommer, director in the Liquidity & Trading Research Group within BlackRock Solutions; Stefano Pasquali, head of the Liquidity & Trading Research Group within BlackRock Solutions; Michael Huang, managing director in the Risk & Quantitative Analysis group; Kristen Walters, former managing director in the Risk & Quantitative Analysis group; and Nikki Azznara, vice president in the Portfolio Management Group.
Chapter 8 presents approaches for managing market risk in fixed‐income portfolios using portfolio optimization techniques. An earlier version of this chapter was previously included in the first edition. However, it has been significantly updated and transformed to reflect new approaches for optimization, including many that are also applicable to multi‐asset portfolios. The chapter begins with a discussion of the differences between risk measurements versus risk management and covers typical fixed‐income hedges. Then, the chapter transitions to discuss parametric hedging techniques, generalized approaches to hedging, and advanced portfolio optimization and risk management techniques. Various examples are included in the chapter to demonstrate how optimization approaches can be utilized in different situations. This chapter does not necessarily align uniquely with a specific IRMP pillar. Rather, portfolio optimization is a powerful and versatile tool that allows portfolios to be engineered for a variety of reasons. This chapter was primarily authored by Alex Ulitsky, managing director in the Financial Modeling Group. Jack Hattem provided significant updates to this chapter.
Chapter 9 introduces the second pillar, risk governance, and also introduces the concept of risk scans to identify potential risk issues. Specifically, properly designed risk and exposure scans can flag portfolios and positions that may not align with client objectives or expectations. Given the increasing size and heterogeneity of investment processes and products, risk managers need to efficiently analyze a multitude of portfolios. This chapter presents a basic univariate risk scan framework that uses simple algorithms to identify potential risk exceptions—what came to be known at BlackRock as Risk and Performance Targets (RPT). I was the primary author of this chapter. Rory van Zwanenberg, director in the Risk & Quantitative Analysis group, significantly contributed to this chapter, along with Katie Day, managing director in the Risk & Quantitative Analysis group.
The third pillar, portfolio manager risk-return awareness, focuses on the relationship between portfolio and risk managers. Chapter 10 discusses the importance of risk managers working together with portfolio managers to ensure that risks are properly detected, understood, and then appropriately managed for clients. Effective risk management requires regular interaction with portfolio managers to discuss risk positioning and can include identifying potential adverse behavioral aspects of investing. The chapter concentrates on behavioral finance, an evolving risk management domain, which seeks to identify cognitive blind spots that can impact investment decisions. The chapter includes details on decision‐making analytics such as loss aversion, disposition bias, and the endowment effect. The chapter also includes a framework for evaluating behavioral aspects of the investment processes. This chapter was coauthored by Emily Haisley, managing director of the behavioral finance initiatives in the Risk & Quantitative Analysis group, and Nicky Lai, director in the Risk & Quantitative Analysis group.
The fourth pillar, performance attribution, decomposes investment returns into their sources of performance, providing portfolio and risk managers with an understanding of the drivers of investment results. Chapter 11 covers approaches and analytical techniques that practitioners can leverage to conduct performance attribution, including Brinson and factor‐based methodologies. The chapter provides multiple examples to demonstrate how portfolio returns can be viewed and interpreted. This chapter was coauthored by Reade Ryan, managing director in the Risk & Quantitative Analysis group, and Carol Yu, former vice president in the Risk & Quantitative Analysis group.
The fifth pillar, performance analysis, presents a framework to review a portfolio's realized performance relative to its benchmarks, peers, and other comparable accounts. Chapter 12 discusses how to meaningfully measure aggregate platform performance, especially across a heterogeneous set of funds with different benchmarks and risk and performance targets. The chapter covers active performance metrics, such as alpha target ratio, weighted peer percentile, and alpha dollars, along with index performance metrics. Strengths and weaknesses of the various active and index performance measurements are presented. This chapter was coauthored by Mark Paltrowitz, managing director and chief performance officer for BlackRock and the head of fixed‐income and multi‐asset investment risk; Mark Temple‐Jones, former director in the ETF & Index Investments group; Viola Dunne, former managing director in the Risk & Quantitative Analysis group; and Christopher Calingo, director in the Risk & Quantitative Analysis group.
Chapter 13 marks the conclusion of the first section of this book and discusses further evolving the Investment Risk Management Paradigm. Given the dynamic nature of financial risk, continuously evolving a risk management framework to address emerging risks and changing market themes is crucial for a growing investment manager. This chapter starts by covering the characteristics of a traditional buy‐side risk management framework and then discusses evolving the framework to better manage a multiplicity of risks at scale. BlackRock Solutions' Aladdin implementation of the Risk Radar system is presented as a tangible example of how risk governance can be successfully executed at scale. This chapter was coauthored by myself; Michael Huang; and Joe Buehlmeyer, director in the Aladdin Product Group.
SECTION II: FIXED‐INCOME RISK MANAGEMENT—THEN AND NOW
Despite rapid transformation in other areas of financial markets, for decades, the core transactional underpinnings of bond markets remained largely the same—high touch, over‐the‐counter markets dependent on dealers' balance sheets with only limited timely price, volume, and order book transparency. However, in the years following the 2008 Global Financial Crisis, significant structural changes in bond markets have occurred. This section briefly discusses some of those bond market changes over the past 20 years.
Chapter 14 discusses the modernization of the bond market and the emergence of fixed‐income exchange‐traded fund products. The chapter covers the evolution of bond markets, the development of index‐based ecosystems, the implications for investing, portfolio management and risk management, and the future state of portfolio construction. This chapter was coauthored by Daniel Veiner, managing director, co‐head of Global Trading; Stephen Laipply, managing director, global co-head of Fixed Income ETFs; Carolyn Weinberg, managing director, chief product innovation officer and co‐head of the Global Product Group; Samara Cohen, senior managing director, chief investment officer of ETF and Index Investments; Vasiliki Pachatouridi, managing director, head of iShares Fixed Income Product Strategy EMEA; and Hui Sien Koay, director, lead Index Fixed Income Product Strategist for APAC.
Chapter 15 discusses the cessation of LIBOR and the massive undertaking required to shift to Alternative Reference Rates (ARRs). Given the transition's size and scope, the migration away from LIBOR required a significant amount of coordination and organization from various market participants. This chapter also discusses the implications to portfolio management along with risk management. This chapter was written by Jack Hattem.
Chapter 16 covers derivatives reform and the rise of Swap Execution Facilities (SEFs) and central counterparties (CCPs). Following the Global Financial Crisis, market reforms sought to improve transparency in derivatives. Electronification of most trading was required, and counterparty credit risk was reduced by mandating much greater usage of CCPs. This chapter was written by Eileen Kiely, managing director and deputy head of Counterparty Risk in the Risk & Quantitative Analysis group, and Jack Hattem.
SECTION III: LESSONS FROM THE CREDIT CRISIS AND CORONAVIRUS PANDEMIC
Major market disruptions, almost by definition, present the opportunity (and need) to reflect on the necessary changes to risk management practices. The following three chapters were previously published articles that identify some lessons to be learned.
Chapter 17 presents seven specific lessons worth remembering from the Global Financial Crisis of 2007–2008. The credit crisis demonstrated that many widely used risk management techniques relied on critical assumptions that turned out to be profoundly flawed. The Global Financial Crisis changed the risk management profession, with unprecedented extreme market moves and the downfall of well‐known financial institutions. Recommendations to enhance risk management practices and beliefs are included in this chapter to correct or mitigate the negative impact of relying on those faulty assumptions.
Chapter 18 highlights the importance of eight principles for buy‐side risk management. Chapter 17 and Chapter 18 were initially published in the Journal of Portfolio Management and coauthored by myself and Conan Crum, former vice president in the Risk & Quantitative Analysis group.
Finally, the book concludes with a chapter on lessons worth considering from the Coronavirus pandemic. Chapter 19 summarizes 10 key lessons from COVID‐19 and considers the implications of the COVID‐19 crisis across capital markets. This chapter reviews the key market events from March 2020 and the official sector's interventions. The chapter includes some lessons drawn from COVID‐19, identifying what worked and what needs to be addressed further, including policy recommendations and areas for future consideration. This chapter was originally published as a BlackRock ViewPoint and was adapted for this book by coauthors Barbara Novick, a co-founder and former vice chairman of BlackRock; Joanna Cound, managing director and global head of Public Policy; Kate Fulton, managing director and head of Americas Public Policy; and Winnie Pun, former managing director within the Global Public Policy group.
BlackRock's Guide to Fixed-Income Risk Management is written for financial services professionals, including chief investment officers, portfolio managers, risk managers, traders, researchers, compliance officers, and modelers. Using BlackRock's approach to risk management as its foundation, the book is particularly intended for buy‐side firms. It is also suitable in an academic setting for undergraduate students as well as MBA and PhD candidates.
Bennett W. Golub
New York
Summer 2023
NOTE
1.
Several current and former BlackRock subject matter experts authored or coauthored multiple chapters in this book. Their names are listed in the following section and their current (or last) BlackRock titles and team affiliations are provided the first time their names appear. For current BlackRock employees, their titles and team affiliations are representative of their roles as of March 2023.
Acknowledgments
The challenge of measuring and managing the risk of thousands of complex and diverse fixed‐income portfolios during periods of both calm and extremely stressed markets offers the ideal setting for developing and applying new ideas. For 34 years, I was fortunate to work in precisely such an environment at BlackRock, Inc., a premier global asset management and risk advisory firm, which served as a state‐of‐the‐practice “laboratory.” That laboratory grew in assets under management (AUM) massively during my tenure there, along the dimensions of new products, new asset classes, and new geographies. This environment provided a never‐ending and extremely focused demand for practical solutions to real‐life problems. Fortunately, because of sophisticated, knowledgeable, and experienced colleagues, who were never shy about providing critiques and feedback, creative and innovative solutions to many risk management challenges developed naturally. The very nature of the problems faced by risk managers—forecasting and mitigating potential severe financial losses—creates the sense of urgency needed to get things done.
BlackRock's risk management philosophy is embedded in the firm's culture and requires the constant development, enhancement, and validation of rigorous techniques for risk measurement and management. Since inception, the firm's commitment to technology and analytics has led to a significant amount of resources being made available for risk management. At the same time, BlackRock's disciplined investment styles and diversity of investment products and services, ranging from mutual funds and institutional accounts to hedge funds, real estate investments trusts (REITs), and collateralized loan obligations (CLOs), created demand for methodologies that are theoretically sound, accurate, intuitive, and computationally feasible. On the other hand, the collaborative approach to portfolio and risk management has led to empirical validation and enhancement of models through constant interaction among financial modelers, portfolio managers, traders, and analysts. This provided a unique opportunity for reconciling theory with reality. Simply put, nothing makes a risk manager's mind focus better than being 20 feet away from a fixed‐income trading desk!
The conceptual and computational challenges of risk measurement and management increase exponentially with the size of a financial institution and the diversity of asset classes in which it invests. BlackRock was founded in 1988 as a niche fixed‐income investment firm. Since then, it has transformed itself into a global investment company with over $9 trillion of AUM1 and independently provides a wide range of risk management services to third‐parties. This rapid growth created unique challenges. It was not only critical to develop risk management methodologies universally pertinent to (almost) all classes of fixed‐income securities, portfolios, and benchmarks, but to ensure that these approaches were suitable for large‐scale practical application in a computationally and operationally feasible manner.
It goes almost without saying that the BlackRock that served as a hothouse for risk management innovations would not have existed as we know it without the leadership provided by Larry Fink and Rob Kapito. Risk management does not have much value if no risks are being taken. Larry, BlackRock's “fearless leader,” and Rob provided much of the boldness that, in retrospect, seemed so obvious. Rob Goldstein and Derek Stein kept the shop running, defying the challenges of scale, and Barbara Novick worked hard to keep BlackRock out of political and regulatory troubles.
This book builds on the concepts that were published in the first edition and introduces new methodologies and topics. This book represents the thought leadership, research, and analysis of many BlackRock risk managers, portfolio managers, traders, financial modelers, and other subject matter experts who have helped to advance the field.
No list of acknowledgments would be complete without singling out my colleague and friend, Ed Fishwick. Ed was my partner co‐managing BlackRock's Risk & Quantitative Analysis group starting when BlackRock acquired Merrill Lynch Investment Management in 2006. For almost 16 years, we worked together very closely, evolving BlackRock's risk management processes and procedures to ever‐changing circumstances. Ed is a world‐renowned expert in all matters relating to equity and multi‐asset risk management. Yet, this fixed‐income‐oriented book benefited greatly from his insights and wisdom.
I want to recognize the contribution of my former colleague and friend, Charlie Hallac, who was taken from us before his time, for his extraordinary ability to turn many of the ideas presented in this book into practical reality through robust implementation and infrastructure. Absent his efforts, much that was achieved would have only been academic.
