{"product_id":"quantitative-credit-portfolio-management-isbn-9781118117699","title":"Quantitative Credit Portfolio Management","description":"\u003cb\u003eAn innovative approach to post-crash credit portfolio management\u003c\/b\u003e  \u003cp\u003eCredit 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.\u003c\/p\u003e \u003cp\u003eA 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, \u003ci\u003eQuantitative Credit Portfolio Management\u003c\/i\u003e offers essential insights into understanding the risks of corporate bonds—spread, liquidity, and Treasury yield curve risk—as well as managing corporate bond portfolios.\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents comprehensive coverage of everything from duration time spread and liquidity cost scores to capturing the credit spread premium\u003c\/li\u003e \u003cli\u003eWritten by the number one ranked quantitative research group for four consecutive years by \u003ci\u003eInstitutional Investor\u003c\/i\u003e\n\u003c\/li\u003e \u003cli\u003eProvides 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?\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eCredit 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.\u003c\/p\u003e \u003cp\u003eForeword xvii\u003c\/p\u003e \u003cp\u003eIntroduction xix\u003c\/p\u003e \u003cp\u003eNotes on Terminology xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Measuring the Market Risks of Corporate Bonds\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Measuring Spread Sensitivity of Corporate Bonds 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAnalysis of Corporate Bond Spread Behavior 5\u003c\/p\u003e \u003cp\u003eA New Measure of Excess Return Volatility 20\u003c\/p\u003e \u003cp\u003eRefinements and Further Tests 25\u003c\/p\u003e \u003cp\u003eSummary and Implications for Portfolio Managers 30\u003c\/p\u003e \u003cp\u003eAppendix: Data Description 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 DTS for Credit Default Swaps 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEstimation Methodology 40\u003c\/p\u003e \u003cp\u003eEmpirical Analysis of CDS Spreads 41\u003c\/p\u003e \u003cp\u003eAppendix: Quasi-Maximum Likelihood Approach 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 DTS for Sovereign Bonds 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSpread Dynamics of Emerging Markets Debt 55\u003c\/p\u003e \u003cp\u003eDTS for Developed Markets Sovereigns: The Case of Euro Treasuries 59\u003c\/p\u003e \u003cp\u003eManaging Sovereign Risk Using DTS 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 A Theoretical Basis for DTS 73 \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Merton Model: A Zero-Coupon Bond 74\u003c\/p\u003e \u003cp\u003eDependence of Slope on Maturity 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Quantifying the Liquidity of Corporate Bonds 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLiquidity Cost Scores (LCS) for U.S. Credit Bonds 82\u003c\/p\u003e \u003cp\u003eLiquidity Cost Scores: Methodology 88\u003c\/p\u003e \u003cp\u003eLCS for Trader-Quoted Bonds 92\u003c\/p\u003e \u003cp\u003eLCS for Non-Quoted Bonds: The LCS Model 96\u003c\/p\u003e \u003cp\u003eTesting the LCS Model: Out-of-Sample Tests 102\u003c\/p\u003e \u003cp\u003eLCS for Pan-European Credit Bonds 113\u003c\/p\u003e \u003cp\u003eUsing LCS in Portfolio Construction 123\u003c\/p\u003e \u003cp\u003eTrade Efficiency Scores (TES) 129\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Joint Dynamics of Default and Liquidity Risk 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSpread Decomposition Methodology 138\u003c\/p\u003e \u003cp\u003eWhat Drives OAS Differences across Bonds? 139\u003c\/p\u003e \u003cp\u003eHow Has the Composition of OAS Changed? 141\u003c\/p\u003e \u003cp\u003eSpread Decomposition Using an Alternative Measure of Expected Default Losses 145\u003c\/p\u003e \u003cp\u003eHigh-Yield Spread Decomposition 147\u003c\/p\u003e \u003cp\u003eApplications of Spread Decomposition 147\u003c\/p\u003e \u003cp\u003eAlternative Spread Decomposition Models 150\u003c\/p\u003e \u003cp\u003eAppendix 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Empirical versus Nominal Durations of Corporate Bonds 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEmpirical Duration: Theory and Evidence 159\u003c\/p\u003e \u003cp\u003eSegmentation in Credit Markets 173\u003c\/p\u003e \u003cp\u003ePotential Stale Pricing and Its Effect on Hedge Ratios 173\u003c\/p\u003e \u003cp\u003eHedge Ratios Following Rating Changes: An Event Study Approach 179\u003c\/p\u003e \u003cp\u003eUsing Empirical Duration in Portfolio Management Applications 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e Part Two Managing Corporate Bond Portfolios\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Hedging the Market Risk in Pairs Trades 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData and Hedging Simulation Methodology 199\u003c\/p\u003e \u003cp\u003eAnalysis of Hedging Results 200\u003c\/p\u003e \u003cp\u003eAppendix: Hedging Pair-Wise Trades with Skill 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Positioning along the Credit Curve 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData and Methodology 214\u003c\/p\u003e \u003cp\u003eEmpirical Analysis 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 The 2007–2009 Credit Crisis 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSpread Behavior during the Credit Crisis 229\u003c\/p\u003e \u003cp\u003eApplications of DTS 234\u003c\/p\u003e \u003cp\u003eAdvantages of DTS in Risk Model Construction 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 A Framework for Diversification of Issuer Risk 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDowngrade Risk before and after the Credit Crisis 250\u003c\/p\u003e \u003cp\u003eUsing DTS to Set Position-Size Ratios 257\u003c\/p\u003e \u003cp\u003eComparing and Combining the Two Approaches to Issuer Limits 260\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 How Best to Capture the Spread Premium of Corporate Bonds? 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Credit Spread Premium 266\u003c\/p\u003e \u003cp\u003eMeasuring the Credit Spread Premium for the IG Corporate Index 266\u003c\/p\u003e \u003cp\u003eAlternative Corporate Indexes 279\u003c\/p\u003e \u003cp\u003eCapturing Spread Premium: Adopting an Alternative Corporate Benchmark 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Risk and Performance of Fallen Angels 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData and Methodology 298\u003c\/p\u003e \u003cp\u003ePerformance Dynamics around Rating Events 303\u003c\/p\u003e \u003cp\u003eFallen Angels as an Asset Class 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Obtaining Credit Exposure Using Cash and Synthetic Replication 337\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCash Credit Replication (TCX) 338\u003c\/p\u003e \u003cp\u003eSynthetic Replication of Cash Indexes 351\u003c\/p\u003e \u003cp\u003eCredit RBIs 358\u003c\/p\u003e \u003cp\u003eReferences 367\u003c\/p\u003e \u003cp\u003eIndex 371\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eARIK BEN DOR,\u003c\/b\u003e \u003cb\u003eP\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e 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 \u003ci\u003eJournal of Portfolio Management, Journal of Fixed Income,\u003c\/i\u003e and \u003ci\u003eJournal of Alternative Investments\u003c\/i\u003e. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eLEV DYNKIN, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJAY HYMAN, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eBRUCE D. PHELPS, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e 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 \u003ci\u003eFinancial Analysts Journal.\u003c\/i\u003e     \u003c\/p\u003e\u003cp\u003eCreated by members of the Quantitative Portfolio Strategy Group at Barclays Capital Research—a recognized authority in this field—\u003ci\u003eQuantitative Credit Portfolio Management\u003c\/i\u003e contains new insights that credit market practitioners, from portfolio managers to research analysts, will find useful, practical, and easy to apply. \u003c\/p\u003e\u003cp\u003eWritten 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. \u003c\/p\u003e\u003cp\u003eAll 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. \u003c\/p\u003e\u003cp\u003eIn this book, the authors: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eBuild 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\u003c\/li\u003e \u003cli\u003eIntroduce 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\u003c\/li\u003e \u003cli\u003eDemonstrate an approach to optimal diversification of issuer-specific risk in credit portfolios\u003c\/li\u003e \u003cli\u003eSuggest 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\u003c\/li\u003e \u003cli\u003eExamine \"fallen angels\" themselves, as a separate asset class, with superior risk and return characteristics\u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989895397605,"sku":"NP9781118117699","price":115.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118117699.jpg?v=1761785827","url":"https:\/\/k12savings.com\/products\/quantitative-credit-portfolio-management-isbn-9781118117699","provider":"K12savings","version":"1.0","type":"link"}