Quant Credit (Fabozzi)

ISBN-13: 9781118117699 / Angielski / Twarda / 2011 / 416 str.

Lev Dynkin

Opis

An innovative approach to post-crash credit portfolio management Credit portfolio managers traditionally rely on fundamental research for decisions on issuer selection and sector rotation. Quantitative researchers tend to use more mathematical techniques for pricing models and to quantify credit risk and relative value. The information found here bridges these two approaches. In an intuitive and readable style, this book illustrates how quantitative techniques can help address specific questions facing today's credit managers and risk analysts. A targeted volume in the area of credit, this reliable resource contains some of the most recent and original research in this field, which addresses among other things important questions raised by the credit crisis of 2008-2009. Divided into two comprehensive parts, Quantitative Credit Portfolio Management offers essential insights into understanding the risks of corporate bonds--spread, liquidity, and Treasury yield curve risk--as well as managing corporate bond portfolios.

• Presents comprehensive coverage of everything from duration time spread and liquidity cost scores to capturing the credit spread premium
• Written by the number one ranked quantitative research group for four consecutive years by Institutional Investor
• Provides practical answers to difficult question, including: What diversification guidelines should you adopt to protect portfolios from issuer-specific risk? Are you well-advised to sell securities downgraded below investment grade?

Credit portfolio management continues to evolve, but with this book as your guide, you can gain a solid understanding of how to manage complex portfolios under dynamic events.

Spis treści

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

O autorze

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.

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.

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.

Blurb

Created by members of the Quantitative Portfolio Strategy Group at Barclays Capital Research a recognized authority in this field Quantitative Credit Portfolio Management contains new insights that credit market practitioners, from portfolio managers to research analysts, will find useful, practical, and easy to apply.

Written in an intuitive yet quantitatively rigorous style, this timely publication opens with a detailed look at new measures of spread risk, liquidity risk, and Treasury curve risk of credit securities. It presents strong empirical evidence of the benefits these measures offer to portfolio managers compared with current standard industry methods. From there, it moves on to examining applications of these risk measures to portfolio construction and management. The authors also examine the best ways of capturing more of the spread premium in credit portfolios.

All along the way, the authors maintain a sharp focus on the "out–of–sample" predictive power of their research results and their practical implications, with special attention given to the 2007 2009 credit crisis and the subsequent European sovereign crisis.

In this book, the authors:

• Build a case for a Duration Times Spread (DTS) approach to forecasting spread changes and managing risk in credit portfolios based on their finding that spread volatility is linearly related to spread levels
• Introduce a security–level numeric measure of transaction costs Liquidity Cost Scores (LCS) which enables investors to quantify the liquidity component of credit spreads and construct portfolios with desired liquidity characteristics
• Demonstrate an approach to optimal diversification of issuer–specific risk in credit portfolios
• Suggest downgrade–tolerant credit portfolios as a way to avoid discarding credit spread premium with the forced liquidation of "fallen angels" as they get dropped from investment grade indices
• Examine "fallen angels" themselves, as a separate asset class, with superior risk and return characteristics