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Quantitative Credit Portfolio Management: Practical Innovations for Measuring and Controlling Liquidity, Spread, and Issuer Concentration Risk


"For many years, this quantitative research team has offered new insights and helpful support to many institutional investors such as APG. By applying these concepts to the portfolio construction process, we have gained more confidence in the robustness of our portfolios."– Eduard van Gelderen, CIO, Capital Markets, APG Asset Management, Netherlands

"A must-read for all future and current credit portfolio managers. The book is a comprehensive review of the quantitative tools available to better manage the risks within a credit portfolio and combines the right amount of statistical work with practical answers to questions confronting credit managers."– Curtis Ishii, Head of Global Fixed Income, California Public Employees' Retirement System

"The practical orientation of this book on institutional credit portfolio management makes it particularly useful for practitioners. All key areas of interest are well covered."– Lim Chow Kiat, President, GIC Asset Management, Singapore

"This book provides enormous insights for beginning practitioners looking to learn the most advanced credit management techniques. For experienced professionals, it provides a great update and advancement.The book is a must-read for all active players in credit markets given the changes after the recent crisis."– Jan Straatman, Global CIO, ING Investment Management, Netherlands

"Lev Dynkin and his team are the highest authority on fixed income portfolio analytics. Their thoughtful and rigorous quantitative research, unparalleled access to high quality data, and cooperative approach with leading fixed income managers sets them apart."– Carolyn Gibbs and Rich King, Co-Heads of U.S. Taxable Fixed Income and Global High Income, Invesco

"Quantitative Credit Portfolio Management is a one of a kind book addressing everyday issues and topics submitted by investors and practitioners to the QPS team. Practical instructions advocated in this book are best practices that we already rely on in our credit investment process for superior active management."– Ibrahima Kobar, CIO, Fixed Income, Natixis Asset Management, France

"The authors ... industry leaders from Barclays Capital ... have done it again! ... They not only delve into improved risk management metrics, but also reveal helpful strategies to improve both passive and active fund management."– Ken Volpert, CFA, Head of Taxable Bond Group, Vanguard

"This book tackles the Big C—CREDIT. Institutional bond investors have long known to go to Lev and his team with their thorniest and most complex portfolio problems. Here, they lay out a very straightforward exposition of best practices in credit portfolio management."– Ken Leech, former CIO, Western Asset Management Company

A more complete list of endorsements may be found inside the book.

ARIK BEN-DOR, PhD, is a Director and Senior Analyst in the Quantitative Portfolio Strategy (QPS) Group at Barclays Capital Research. He joined the group in 2004 after completing a PhD in finance from the Kellogg School of Management. Ben-Dor has published extensively in the Journal of Portfolio Management, Journal of Fixed Income, and Journal of Alternative Investments on innovative approaches to managing risk in credit portfolios and on performance analysis and optimization of hedge fund portfolios.

LEV DYNKIN, PhD, is the founder and Global Head of the Quantitative Portfolio Strategy Group at Barclays Capital Research. Dynkin and the QPS group joined Barclays Capital in 2008 from Lehman Brothers where the group was a part of fixed income research since 1987—one of the longest tenures for an investor-focused research group on Wall Street. QPS was rated first in Quantitative Portfolio Research by Institutional Investor magazine for all three years that this category was included in their fixed income survey. Dynkin is a member of the editorial advisory board of the Journal of Portfolio Management. He coauthored, with other members of QPS (including Hyman and Phelps), Quantitative Management of Bond Portfolios.

JAY HYMAN, PhD, is a Managing Director in the Quantitative Portfolio Strategy Group at Barclays Capital Research. He joined the group in 1991 and has since worked on issues of risk budgeting, cost of investment constraints, improved measures of risk sensitivities, and optimal risk diversification for portfolios spanning all fixed income asset classes. Hyman helped develop a number of innovative measures that have been broadly adopted by portfolio managers and that have changed standard industry practice.

BRUCE D. PHELPS, PhD, is a Managing Director in the Quantitative Portfolio Strategy Group at Barclays Capital Research, which he joined in 2000. Prior to that, he was an institutional portfolio manager and head of fixed income at Ark Asset Management. Phelps was also senior economist at the Chicago Board of Trade, where he designed derivative contracts and electronic trading systems, and an international credit officer and foreign exchange trader at Wells Fargo Bank. Phelps is a member of the editorial board of the Financial Analysts Journal.

Foreword xvii

Introduction xix

Notes on Terminology xxvii

PART ONE Measuring the Market Risks of Corporate Bonds

CHAPTER 1 Measuring Spread Sensitivity of Corporate Bonds 3

Analysis of Corporate Bond Spread Behavior 5

A New Measure of Excess Return Volatility 20

Refinements and Further Tests 25

Summary and Implications for Portfolio Managers 30

Appendix: Data Description 34

CHAPTER 2 DTS for Credit Default Swaps 39

Estimation Methodology 40

Empirical Analysis of CDS Spreads 41

Appendix: Quasi-Maximum Likelihood Approach 51

CHAPTER 3 DTS for Sovereign Bonds 55

Spread Dynamics of Emerging Markets Debt 55

DTS for Developed Markets Sovereigns: The Case of Euro Treasuries 59

Managing Sovereign Risk Using DTS 66

CHAPTER 4 A Theoretical Basis for DTS 73

The Merton Model: A Zero-Coupon Bond 74

Dependence of Slope on Maturity 77

CHAPTER 5 Quantifying the Liquidity of Corporate Bonds 81

Liquidity Cost Scores (LCS) for U.S. Credit Bonds 82

Liquidity Cost Scores: Methodology 88

LCS for Trader-Quoted Bonds 92

LCS for Non-Quoted Bonds: The LCS Model 96

Testing the LCS Model: Out-of-Sample Tests 102

LCS for Pan-European Credit Bonds 113

Using LCS in Portfolio Construction 123

Trade Efficiency Scores (TES) 129

CHAPTER 6 Joint Dynamics of Default and Liquidity Risk 133

Spread Decomposition Methodology 138

What Drives OAS Differences across Bonds? 139

How Has the Composition of OAS Changed? 141

Spread Decomposition Using an Alternative Measure of Expected Default Losses 145

High-Yield Spread Decomposition 147

Applications of Spread Decomposition 147

Alternative Spread Decomposition Models 150

Appendix 152

CHAPTER 7 Empirical versus Nominal Durations of Corporate Bonds 157

Empirical Duration: Theory and Evidence 159

Segmentation in Credit Markets 173

Potential Stale Pricing and Its Effect on Hedge Ratios 173

Hedge Ratios Following Rating Changes: An Event Study Approach 179

Using Empirical Duration in Portfolio Management Applications 186

PART TWO Managing Corporate Bond Portfolios

CHAPTER 8 Hedging the Market Risk in Pairs Trades 197

Data and Hedging Simulation Methodology 199

Analysis of Hedging Results 200

Appendix: Hedging Pair-Wise Trades with Skill 208

CHAPTER 9 Positioning along the Credit Curve 213

Data and Methodology 214

Empirical Analysis 217

CHAPTER 10 The 2007–2009 Credit Crisis 229

Spread Behavior during the Credit Crisis 229

Applications of DTS 234

Advantages of DTS in Risk Model Construction 244

CHAPTER 11 A Framework for Diversification of Issuer Risk 249

Downgrade Risk before and after the Credit Crisis 250

Using DTS to Set Position-Size Ratios 257

Comparing and Combining the Two Approaches to Issuer Limits 260

CHAPTER 12 How Best to Capture the Spread Premium of Corporate Bonds? 265

The Credit Spread Premium 266

Measuring the Credit Spread Premium for the IG Corporate Index 266

Alternative Corporate Indexes 279

Capturing Spread Premium: Adopting an Alternative Corporate Benchmark 288

CHAPTER 13 Risk and Performance of Fallen Angels 295

Data and Methodology 298

Performance Dynamics around Rating Events 303

Fallen Angels as an Asset Class 319

CHAPTER 14 Obtaining Credit Exposure Using Cash and Synthetic Replication 337

Cash Credit Replication (TCX) 338

Synthetic Replication of Cash Indexes 351

Credit RBIs 358

References 367

Index 371