
Quantitative Credit Portfolio Management Practical Innovations for Measuring and Controlling Liquidity, Spread, and Issuer Concentration Risk
by Ben Dor, Arik; Dynkin, Lev; Hyman, Jay; Phelps, Bruce D.Buy New
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Summary
Author Biography
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.
Table of Contents
Foreword | p. xvii |
Introduction | p. xix |
Notes on Terminology | p. xxvii |
Measuring the Market Risks of Corporate Bonds | |
Measuring Spread Sensitivity of Corporate Bonds | p. 3 |
Analysis of Corporate Bond Spread Behavior | p. 5 |
A New Measure of Excess Return Volatility | p. 20 |
Refinements and Further Tests | p. 25 |
Summary and Implications for Portfolio Managers | p. 30 |
Appendix: Data Description | p. 34 |
DTS for Credit Default Swaps | p. 33 |
Estimation Methodology | p. 40 |
Empirical Analysis of CDS Spreads | p. 41 |
Appendix: Quasi-Maximum Likelihood Approach | p. 51 |
DTS for Sovereign Bonds | p. 55 |
Spread Dynamics of Emerging Markets Debt | p. 55 |
DTS for Developed Markets Sovereigns: The Case of Euro Treasuries | p. 59 |
Managing Sovereign Risk Using DTS | p. 66 |
A Theoretical Basis for D7S | p. 73 |
The Merton Model: A Zero-Coupon Bond | p. 74 |
Dependence of Slope on Maturity | p. 77 |
Quantifying the Liquidity of Corporate Bonds | p. 81 |
Liquidity Cost Scores (LCS) for U.S. Credit Bonds | p. 82 |
Liquidity Cost Scores: Methodology | p. 88 |
LCS for Trader-Quoted Bonds | p. 92 |
LCS for Non-Quoted Bonds: The LCS Model | p. 96 |
Testing the LCS Model: Out-of-Sample Tests | p. 102 |
LCS for Pan-European Credit Bonds | p. 113 |
Using LCS in Portfolio Construction | p. 123 |
Trade Efficiency Scores (TES) | p. 129 |
Joint Dynamics of Default and Liquidity Risk | p. 133 |
Spread Decomposition Methodology | p. 138 |
What Drives OAS Differences across Bonds? | p. 139 |
How Has the Composition of OAS Changed? | p. 141 |
Spread Decomposition Using an Alternative Measure of Expected Default Losses | p. 145 |
High-Yield Spread Decomposition | p. 147 |
Applications of Spread Decomposition | p. 147 |
Alternative Spread Decomposition Models | p. 150 |
Appendix | p. 152 |
Empirical versus Nominal Durations of Corporate Bonds | p. 157 |
Empirical Duration: Theory and Evidence | p. 159 |
Segmentation in Credit Markets | p. 73 |
Potential Stale Pricing and Its Effect on Hedge Ratios | p. 173 |
Hedge Ratios Following Rating Changes: An Event Study Approach | p. 179 |
Using Empirical Duration in Portfolio Management Applications | p. 186 |
Managing Corporate Bond Portfolios | |
Hedging the Market Risk in Pairs Trades | p. 197 |
Data and Hedging Simulation Methodology | p. 199 |
Analysis of Hedging Results | p. 200 |
Appendix: Hedging Pair-Wise Trades with Skill | p. 208 |
Positioning along the Credit Curve | p. 213 |
Data and Methodology | p. 214 |
Empirical Analysis | p. 217 |
The 2007-2009 Credit Crisis | p. 229 |
Spread Behavior during the Credit Crisis | p. 229 |
Applications of DTS | p. 234 |
Advantages of DTS in Risk Model Construction | p. 244 |
A Framework for Diversification of Issuer Risk | p. 249 |
Downgrade Risk before and after the Credit Crisis | p. 250 |
Using DTS to Set Position-Size Ratios | p. 257 |
Comparing and Combining the Two Approaches to Issuer Limits | p. 260 |
How Best to Capture the Spread Premium of Corporate Bonds? | p. 265 |
The Credit Spread Premium | p. 266 |
Measuring the Credit Spread Premium for the IG Corporate Index | p. 266 |
Alternative Corporate Indexes | p. 279 |
Capturing Spread Premium: Adopting an Alternative Corporate Benchmark | p. 288 |
Risk and Performance of Fallen Angels | p. 295 |
Data and Methodology | p. 298 |
Performance Dynamics around Rating Events | p. 303 |
Fallen Angels as an Asset Class | p. 319 |
Obtaining Credit Exposure Using Cash and Synthetic Replication | p. 337 |
Cash Credit Replication (TCX) | p. 338 |
Synthetic Replication of Cash Indexes | p. 351 |
Credit RBIs | p. 358 |
References | p. 367 |
Index | p. 371 |
Table of Contents provided by Ingram. All Rights Reserved. |
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