Modern Multi-Factor Analysis of Bond Portfolios DOI: 10.1057/9781137564863.0001 Other Palgrave Pivot titles Rilka Dragneva and Kataryna Wolczuk: Ukraine between the EU and Russia: The Integration Challenge Viola Fabbrini, Massimo Guidolin and Manuela Pedio: Transmission Channels of Financial Shocks to Stock, Bond, and Asset-Backed Markets: An Empirical Model Timothy Wood: Detainee Abuse During Op TELIC: ‘A Few Rotten Apples’? Lars Klüver, Rasmus Øjvind Nielsen and Marie Louise Jørgensen (editors): Policy-Oriented Technology Assessment Across Europe: Expanding Capacities Rebecca E Lyons and Samantha J Rayner (editors): The Academic Book of the Future Ben Clements: Surveying Christian Beliefs and Religious Debates in Post-War Britain Robert A Stebbins: Leisure and the Motive to Volunteer: Theories of Serious, Casual, and Project-Based Leisure Dietrich Orlow: Socialist Reformers and the Collapse of the German Democratic Republic Gwendolyn Audrey Foster: Disruptive Feminisms: Raced, Gendered, and Classed Bodies in Film Catherine A Lugg: US Public Schools and the Politics of Queer Erasure Olli Pyyhtinen: More-than-Human Sociology: A New Sociological Imagination Jane Hemsley-Brown and Izhar Oplatka: Higher Education Consumer Choice Arthur Asa Berger: Gizmos or: The Electronic Imperative: How Digital Devices have Transformed American Character and Culture Antoine Vauchez: Democratizing Europe Cassie Smith-Christmas: Family Language Policy: Maintaining an Endangered Language in the Home Liam Magee: Interwoven Cities Alan Bainbridge: On Becoming an Education Professional: A Psychosocial Exploration of Developing an Education Professional Practice Bruce Moghtader: Foucault and Educational Ethics Carol Rittner and John K Roth: Teaching about Rape in War and Genocide Robert H Blank: Cognitive Enhancement: Social and Public Policy Issues DOI: 10.1057/9781137564863.0001 Modern Multi-Factor Analysis of Bond Portfolios: Critical Implications for Hedging and Investing Edited by Giovanni Barone Adesi Professor, Università della Svizzera Italiana, Switzerland and Nicola Carcano Lecturer, Faculty of Economics, Università della Svizzera Italiana, Switzerland DOI: 10.1057/9781137564863.0001 Selection and editorial content © Giovanni Barone Adesi and Nicola Carcano 2016 Individual chapters © the contributors 2016 Softcover f reprint off the hardcover 1st edition 2016 978-1-137-56485-6 All rights reserved No reproduction, copy or transmission of this publication may be made without written permission No portion of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, Saffron House, 6–10 Kirby Street, London EC1N 8TS Any person who does any unauthorized act in relation to this publication may be liable to criminal prosecution and civil claims for damages The authors have asserted their rights to be identified as the authors of this work in accordance with the Copyright, Designs and Patents Act 1988 First published 2016 by PALGRAVE MACMILLAN Palgrave Macmillan in the UK is an imprint of Macmillan Publishers Limited, registered in England, company number 785998, of Houndmills, Basingstoke, Hampshire RG21 6XS Palgrave Macmillan in the US is a division of St Martin’s Press LLC, 175 Fifth Avenue, New York, NY 10010 Palgrave Macmillan is the global academic imprint of the above companies and has companies and representatives throughout the world Palgrave® and Macmillan® are registered trademarks in the United States, the United Kingdom, Europe and other countries ISBN: 978-1-137-56486-3 PDF ISBN: 978-1-349-85024-2 A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data Names: Barone Adesi, Giovanni, 1951– editor | Carcano, Nicola, 1964– editor Title: Modern multi-factor analysis of bond portfolios : critical implications for hedging and investing / [edited by] Giovanni Barone Adesi, Professor, Università della Svizzera Italiana, Switzerland, Nicola Carcano, Lecturer, Faculty of Economics, Università della Svizzera Italiana, Switzerland Description: New York : Palgrave Macmillan, 2015 Identifiers: LCCN 2015037662 Subjects: LCSH: Bonds | Bond market | Investments | Hedge funds | Porfolio management Classification: LCC HG4651 M5963 2015 | DDC 332.63/23015195—dc23 LC record available at http://lccn.loc.gov/2015037662 www.palgrave.com/pivot doi: 10.1057/9781137564863 Contents List of Chart & Exhibits List of Figures vi viii Notes on Contributors x Introduction Giovanni Barone Adesi and Nicola Carcano Adjusting Principal Component Analysis for Model Errors Nicola Carcano Alternative Models for Hedging Yield Curve Risk: An Empirical Comparison Nicola Carcano and Hakim Dall’O Applying Error-Adjusted Hedging to Corporate Bond Portfolios Giovanni Barone Adesi, Nicola Carcano and Hakim Dall’O Credit Risk Premium: Measurement, Interpretation and Portfolio Allocation Radu C Gabudean, Kwok Yuen Ng and Bruce D Phelps Overall Conclusion Giovanni Barone Adesi and Nicola Carcano 21 47 78 111 References 115 Index 121 DOI: 10.1057/9781137564863.0001 v List of Chart & Exhibits Chart Assessing the distance between the yields of the 2-year, 5-year, 10-year and 30-year treasury bonds and the future notional coupon 35 Exhibits vi Testing alternative PCA-based strategies on US treasury bonds: hedging quality indicators Testing alternative PCA-based strategies on US treasury bonds: average transaction fees Testing alternative PCA-based strategies including USD interest rate swaps: hedging quality indicators Testing the most common hedging techniques in their traditional form Testing the most common hedging techniques in their error-adjusted form Calculating the performance of hedging models based on the initial cheapest-to-deliver bonds Alternative hedging models based on bond futures: sub-sample analysis Sensitivity of PCA hedging models to small changes in the coefficients 13 14 16 37 38 39 41 42 DOI: 10.1057/9781137564863.0002 List of Chart & Exhibits Summary statistics on spreads related to BBB-rated bonds 10 Variance reduction obtained by alternative hedging strategies 11 Predictability of the hedging errors produced by alternative hedging strategies vii DOI: 10.1057/9781137564863.0002 63 64 66 List of Figures 10 viii Historical reported IG corporate index excess returns Analytical durations (DurOAD & DurDefAdj) for the NC IG corp index and their difference, July 1989–November 2012 Treasury yields and the difference between DurOAD and DurDefAdj, for the NC IG corp index, July 1989–November 2012 Comparison of OAS and the difference between DurOAD and DurDefAdj for the NC IG corp index, July 1989–November 2012 Statistics of various NC IG corp indices using two different analytical duration measures, July 1989–November 2012 Average ExRet (/mo) for NC IG corp index conditional on the change in Treasury yields, July 1989–November 2012 Correlations of various ExRetanalyt measures with Treasury returns, by sub-period, March 2004–November 2012 Evolution of various empirical duration betas for the NC IG corp index, July 1989– November 2012 Rolling correlations of various ExRetemp with Treasury returns, trailing 24 months, May 1991–November 2012 Statistics of various NC IG corp indices, July 1989–November 2012 79 83 84 84 85 86 86 89 91 92 DOI: 10.1057/9781137564863.0003 List of Figures Average ExRetanalyt and ExRetemp dyn (/mo) for NC IG corp index conditional on the change in Treasury yields, July 1989–November 2012 12 Cumulative NC IG corporate index ExRet performance for various duration measures, July 1989–November 2012 13 Duration ratios (betas) for the IG corp index & matched-DurOAD Treasury yields, January 1973–June 1989 14 Relation between IG corp index spreads & matched-DurOAD Treasury yields, January 1973–June 1989 15 Correlation between IG corp spreads and matched-DurOAD Treasury yields & level of matched-DurOAD Treasury yields, January 1973–November 2012 16 Correlation of major assets’ performance with macroeconomic variables, 1953–2011 17 Relationship of asset class performance with real GDP growth (/y), 1953–2011 18 Relationship of asset class performance with CPI inflation (/y), 1953–2011 19 Correlation of asset class returns with macroeconomic variables, 1953–2011 20 Smoothed, de-meaned macroeconomic variables, GDP growth & CPI inflation, Q1/1963–Q3/2012 21 Return statistics for various returns of the IG corp index, January 1978–September 1981 22 Return statistics for mean-variance-optimal portfolios of Treasuries with various returns of the non-call DGT IG index, July 1989–November 2012 23 Net weight to Treasuries (scaled) for various corp/ Treasury portfolios, as of total net allocation, July 1989–November 2012 24 Cumulative performance of various ExRet measures for the IG corporate index, January 1973–November 2012 25 Return statistics of various returns of the IG corporate index, January 1973–November 2012 ix 11 DOI: 10.1057/9781137564863.0003 92 93 95 95 96 97 98 98 99 100 101 105 106 108 108 Radu C Gabudean, Kwok Yuen Ng and Bruce D Phelps Such investors may have an interest in Treasury futures-based, durationhedged versions of their current credit benchmarks For Barclays’ method of generating long time series for real Treasury yields, see: Pond and Mirani (2009) For more details on volatility forecasting see Gabudean and Schuehle (2011) The portfolio of ExRetOAD and Treasuries can be interpreted as a de-levered version of corporate and Treasuries portfolio because the allocations to ExRetOAD and Treasuries translate into allocations to corporate bonds and Treasuries that sum to less than 100 Clearly, this may not be true for portfolios containing equity and high yield DOI: 10.1057/9781137564863.0009 Overall Conclusion Giovanni Barone Adesi and Nicola Carcano Abstract: The main goal of this book is to promote a broader use of multi-factor models for managing the risks of fixed-income portfolios The final chapter of the book supports this use by summarizing the theoretical arguments and empirical evidence presented in the main chapters of the book We express the view that the finance industry has still a long way to go for taking full advantage of multifactor analyses in the management of bond portfolios For portfolios of high credit quality, what is needed in most cases is just an adequate controlling of model risk exposure, which implies relatively straightforward extensions of traditional hedging equations For portfolios of lower credit quality, the availability of liquid credit derivatives displaying a lower basis risk than non-collateralized CDS is necessary for effectively facing phases of significant market disruption Barone Adesi, Giovanni and Nicola Carcano, eds Modern Multi-Factor Analysis of Bond Portfolios: Critical Implications for Hedging and Investing Basingstoke: Palgrave Macmillan, 2016 doi: 10.1057/9781137564863.0010 DOI: 10.1057/9781137564863.0010 Giovanni Barone Adesi and Nicola Carcano The main goal of this book is to promote a broader use of multi-factor models for managing the risks of fixed-income portfolios We believe this use is strongly supported by the theoretical arguments and empirical evidence presented in the main chapters of the book In Chapter 2, we show that there is a very plausible explanation for the inconsistent practical advantages of multi-factor hedging techniques documented so far: the level of exposure to remaining model errors We show how relatively simple adjustments to traditional hedging equations can reduce this exposure enough to consistently and significantly improve the hedging of default risk-free portfolios An important side-effect of these adjustments is a tangible reduction in transaction costs which we expect to be systematic: controlling the exposure to model errors tends to systematically reduce the total absolute value of the hedging positions, thus leading to lower trading volumes All hedging techniques tested in this chapter have been based on Principal Component Analysis and hedging portfolios composed of bonds or interest rate swaps In Chapter 3, we show that the conclusions reached in Chapter are robust to alternative multi-factor modeling approaches, like Duration Vector and/or Key Rate Durations They are also robust to the use of alternative hedging vehicles, like bond futures The key conclusion we reach in Chapter is that a 3-factor Principal Component Analysis controlling the level of exposure to model errors appears to be the most efficient technique for hedging the interest rate risk of default risk-free portfolios In Chapter 4, we show that the conclusions reached in Chapters and can be extended to corporate bond portfolios As already suggested by previous literature, we show that in ordinary market conditions it is not necessary to introduce a specific hedging of corporate spread risk through equity futures, equity volatility futures or CDS: an effective hedging of the default risk-free term structure is sufficient to also effectively protect investors from unexpected changes in corporate spreads We show that the same techniques tested on default risk-free portfolios in Chapters and are also capable to effectively hedge corporate bond portfolios against unexpected changes in the default risk-free term structure However, in extraordinary market conditions leading to unusual corporate spread volatility, like the ones experienced in 2008 and 2009, introducing a specific hedging of corporate spread DOI: 10.1057/9781137564863.0010 Overall Conclusion risk becomes crucial in order to manage the overall risks of corporate bond portfolios Per se, the hedging techniques including the adjustment for controlling the exposure to model errors tested in Chapters and could be helpful also in these conditions Unfortunately, the hedging instruments available today not allow for an effective hedging of corporate spread risk in extraordinary market conditions: equity futures and equity volatility futures display a too loose relationship with corporate spreads to be really useful, whereas non-collateralized CDS embed a too large and persistent basis risk to represent a reliable hedging vehicle As a result, we conclude that Principal Component Analysis controlling the level of exposure to model errors can also be successfully applied to corporate bond portfolios, but the current absence of effective corporate spread hedging vehicles limits the overall hedging quality in extraordinary market conditions leading to unusual corporate spread volatility Chapter of this book highlights the crucial role played by the correlation between the two most critical risk factors determining the performance of corporate bond portfolios: unexpected changes in the level of default risk-free interest rates and in corporate bond spreads A number of previous studies have documented that this correlation can be due to a correlation between default risk-free interest rates and bond’s default probabilities and recovery rates, but even more so to a correlation between risk-free interest rates and time-varying liquidity and/or market risk premiums The authors of Chapter show that the correlation between changes in the level of risk-free interest rates and in corporate bond spreads can explain the puzzle related to the low risk-adjusted excess return provided in the last decades by long-term corporate bonds This correlation can also be effectively used to optimize portfolios in order to match with investors’ macroeconomic views and final objectives The empirical evidence reported in Chapter also suggests that this correlation is time-varying: even though in the long-term it tends to assume negative values, it can take positive values in times of prolonged increases in risk-free interest rates, an observation of particular relevance in the current phase of historically low interest rates Summing up, we are convinced that the finance industry has still a long way to go for taking full advantage of multi-factor analyses in the management of bond portfolios For portfolios of high credit quality, what is needed in most cases is just an adequate controlling of model DOI: 10.1057/9781137564863.0010 Giovanni Barone Adesi and Nicola Carcano risk exposure, which implies relatively straightforward extensions of traditional hedging equations For portfolios of lower credit quality, the availability of liquid credit derivatives displaying a lower basis risk than non-collateralized CDS is necessary for effectively facing phases of significant market disruption Instruments like collateralized CDS or derivatives linked to very liquid bonds ETFs could serve the purpose DOI: 10.1057/9781137564863.0010 References Ahn, S., S Dieckmann and M F Perez, 2008, Exploring the common factors in the term structure of credit spreads, Arizona State University, working paper Armeanu, D., F O Balu and C Obreja, 2008, Interest rate risk management using duration gap methodology Theoretical and Applied Economics, 1, 3–10 Bao, J., J Pan and J Wang, 2011, The illiquidity of corporate bonds, Journal of Finance, 66, 3, 911–946 Beckworth, D., K P Moon and J Holland Toles, 2010, Monetary policy and corporate bond yield spreads, 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M and C Mirani, 2009, TIPS: Predicting history, Barclays Research, March 13 Qi, H., S Liu and C Wu, 2010, Structural models of corporate bond pricing with personal taxes, Journal of Banking and Finance, 34, 1700–1718 Reitano, R R., 1996, Non-parallel yield curve shifts and stochastic immunization Journal of Portfolio Management, Winter, 71–78 Rendleman, R J., 2004, Delivery options in the pricing and hedging of Treasury bond and note futures, Journal of Fixed Income, 2, 20–31 DOI: 10.1057/9781137564863.0011 References Schaefer, S M and I A Strebulaev, 2008, Structural models of credit risk are useful: Evidence from hedge ratios on corporate bonds, Journal of Financial Economics, 90, 1–19 Skinner, F S., 1998, Hedging bonds subject to credit risk, Journal of Banking and Finance, 22, 321–345 Van Landschoot, A., 2008, Determinants of yield spread dynamics: Euro versus US dollar corporate bonds, Journal of Banking and Finance, 32, 2597–2605 DOI: 10.1057/9781137564863.0011 Index ALM (Asset and Liability Management), barbell portfolio hedging models, 34, 39 interest rate swaps, 16 sensitivity of PCA hedging models, 42 sub-sample analysis of bond futures, 41 testing hedging technique, 37, 38 US treasury bonds, 13, 14 Barclays Gov/Corp Index, 101, 104, 106, 107 BBB-rated bonds, 50 interest rates for, 56–7 statistics on spreads for, 62–3, 70 unexpected returns, 53–4, 59 yield curves, 62, 64–6, 68 BK1 model, Barclays, 109n4 BK2 model, Barclays, 95, 96, 109n6 bond future, theoretical price of, 26 BRAIS (Barclays Risk Analytics and Index Solutions), 108–9n1 bullet portfolios hedging models, 33–4, 39 interest rate swaps, 16 sensitivity of PCA hedging models, 42 DOI: 10.1057/9781137564863.0012 sub-sample analysis of bond futures, 41 testing hedging technique, 37, 38 US treasury bonds, 13, 14 butterfly portfolio hedging models, 34, 39 interest rate swaps, 16 sensitivity of PCA hedging models, 42 sub-sample analysis of bond futures, 41 testing hedging technique, 37 US treasury bonds, 13, 14 CDS (Credit Default Swaps) basis, 51, 52, 69 hedging, 49, 51–2, 56, 112–14 hedging through T-bond futures and, 62, 68–70 CDX contracts, 72, 74, 77n22 CDX returns, 62, 85–6 hedging strategies, 64, 66, 68–9, 71 North American investment grade, 56 CF (conversion factor), 26, 33 CME (Chicago Mercantile Exchange), 33, 34, 45, 54–5, 74 Consumer Confidence Index, 61, 67, 76n12 convexity, concept of, 2, 17, 22 Index corporate bond portfolios aggregate, 48–9 CDS (Credit Default Swaps), 49, 51–2, 56, 62, 69–71, 112–14 dataset and calculation of unexpected returns, 52–6 equations using matrices and vectors, 72–4 equity index and volatility futures, 49–51 general framework, 56–9 hedging spreads, 48 hedging through T-bond futures, 59–61, 65–7 hedging through T-bond futures and credit default swaps, 62, 68–70 hedging through T-bond futures and S&P500 futures, 61, 67–8 methodology, 56–62 models for hedging, predictability of hedging errors, 66 results by alternative hedging strategies, 62–70 statistics on spread of BBB-rated bonds, 63 treasury bond futures, 49, 76n9 variance reduction by alternative hedging, 64 coupon bonds, 8, 11–12, 20n4, 53 credit risk premium analytical corporate excess returns, 82–7 analytical durations for non-call IG corporate index, 83 asset class performance and GDP growth, 97, 98, 99–100 asset class performance and inflation, 97, 98, 99–100 asset class performance with macroeconomic variables, 97, 99–100 constructing empirical duration measure, 87–90 corporate spreads and treasury yields, 94, 95 correlations of corporate spreads with treasury yields, 85–7, 96–100 default-adjusted duration, 83 duration ratios, 94, 95 empirical corporate excess returns, 87 long-term (Jan 1973–Nov 2012), 93–100 measures of, 81–93 Non-Call Downgrade Tolerant (DGT) Corporate Index, 80, 85, 91–2, 103, 105–6, 109n2 Non-Call IG Corporate Index, 79, 80, 84–5, 89, 91, 93, 108, 109n2 optimal combination of IG corporates and treasuries, 101–7 option adjusted duration (OAD), 82–3 portfolio construction process, 103–4 selecting best empirical duration measure, 90–3 statistics of non-call corp indices, 85 credit spread puzzle, 4, 48, 75n4 CRSP database, 11–12, 33–4, 55 CSA (Cash Settlement Amount), 56 CTD (cheapest-to-deliver) bonds, 24–7, 39, 44, 77n19 duration, 45n1 concept of, 2, 5n1, 17, 22 constructing empirical measure, 87–90 dynamic, in-sample empirical, 88 equation, 109n5 EWMA (exponentially weighted moving average), 89–92, 105, 106, 108 fixed, in-sample empirical, 88 forecast empirical, 89 long-term credit risk premium, 93–100 option adjusted duration (OAD), 83–8, 91, 92, 94–6 selecting best empirical measure, 90–3 DOI: 10.1057/9781137564863.0012 Index DV (duration vector) model, 3, 22, 45n2 error-adjusted form, 38 hedging results, 36–9, 41 methodology, 30–2 sub-sample analysis, 41 interest rate risk, 2, 3–4 interest rates, model error, 25 interest rate swaps, PCA hedging strategies, 15, 16 IRR (implied repo rate), 33, 77n19 end-of-the-month option, 45n4 equity index and volatility futures, 49–51 excess returns, 79–81 analytical corporate, 82–7 empirical corporate, 87 long-term credit risk premium, 93–100 KRD (key rate duration) concept of, 3, 23, 45n2, 50 error-adjusted form, 38 hedging equations, 30, 32 hedging results, 36–9, 41 sub-sample analysis, 41 GDP (gross domestic product), asset class performance and, 97, 98, 99–100 GDV (generalized duration vector) model, 3, 22 methodology, 30, 31–2 minimization procedure, 46n8 hedging equations, vectors and matrices, 72–4 hedging errors predictability of, 66, 70–1 standard deviation of, 20n5 hedging methodology, 24–32 hedging models DV (duration vector) model, 3, 22, 30–2 GDV (generalized duration vector) model, 3, 22, 30, 31–2 multi-factor, 3, 22, 50, 70, 112–13 PCA (principal component analysis), 8–11, 30 Hessian matrix, PCA-hedging strategies, 19–20 ICE (Intercontinental Exchange), 71 IG Corporate Index, 79, 80, 108 immunization, 2–3, 9, 22–3, 31 index excess returns, 79–81 industrial corporate bonds, 76n12 inflation, asset class performance and, 97, 98, 99–100 DOI: 10.1057/9781137564863.0012 ladder portfolio hedging models, 33, 39 hedging quality, 53 interest rate swaps, 16 sensitivity of PCA hedging models, 42 sub-sample analysis of bond futures, 41 testing hedging technique, 37, 38 US treasury bonds, 13, 14 LCS (Liquidity Cost Scores), 75n2 LDI (Liability Driven Investments), liability time bucket, 9, 20n7 mean weights sensitivity, 43 model error, interest rates, 25 monetary policy, 75n7 Moody’s Baa Corporate Index, 76n11–12, 96, 97 M-square model, 3, 7, 17, 22 multi-factor hedging techniques, 3, 22, 50, 70, 112–13 M-vector model, 3, 7, 17, 22 Non-Call Downgrade-Tolerant (DGT) Corporate Index, 80, 85, 91–2, 103, 105–6, 109n2 Non-Call IG Corporate Index, 79, 80, 84–5, 89, 91, 93, 108, 109n2 PCA (principal component analysis) approximating unexpected return, 17–18 bullet portfolios, 11, 13, 14 Index PCA (principal component analysis) – continued controlling exposure to model errors, 23 error-adjusted form, 38, 46n6 hedging models, 8–11, 30, 45n2 hedging results, 11–15, 36–43 Hessian matrix of hedging strategies, 19–20 immunization model, 22–3 model errors, 16–18 sensitivity of PCA hedging model, 42, 46n5 statistical technique, 3, 7–8 sub-sample analysis, 41 USD interest rate swaps, 16 US treasury bonds, 13, 14 portfolio construction, 4–5 S&P500 futures, 55 hedging through T-bond futures and, 61, 67–8 predictability of hedging errors, 66 variance reduction, 64 SEI (Standard Error of Immunization) hedging error, 34, 37, 38, 39, 41 quality of hedging strategy, 12, 15 USD interest rate swaps, 16 US treasury bonds, 13 self-financing constraint, 9, 18, 20n6, 24, 29, 30, 32 sensitivity interest rate changes, 46n5 mean weights, 43 PCA hedging model, 42, 46n5 volatility weights, 43 Sharpe ratio, 79–80, 85, 92, 101–6, 108 T-bond (treasury) futures, 49–52, 64, 65–7, 76n9 CDX contract and, 64 hedging through, 59–61, 65–7 hedging through, and credit default swaps, 62, 68–70 hedging through, and S&P500 futures, 61, 64, 67–8 transaction fees, 12–13, 14 treasury bonds, see T-bond futures; US treasury bonds unexpected returns approximating, 17–18 corporate bonds and corporate yield curves, 52–4 dataset and calculation of, 52–6 North American investment grade CDX contracts, 56 S&P500 futures, 55 US Treasury bond futures and yield curves, 54–5 US treasury bonds correlation of corporate spreads and treasury yields, 85–7, 96–100 distance between yields of 2-, 5-, 10- and 30-year, 35 futures and yield curves, 54–5 hedging quality indicators, 13 interest rate swaps, 15 predictability of hedging errors, 66 transaction fees, 14 volatility weights sensitivity, 43 wild card option, 45–6n4 yield curve risk, 22 dataset and testing approach, 32–6 hedging methodology, 24–32 model results, 36–43 zero-coupon rates, 54, 68 duration, maturity, 8, 25, 32 Unsmoothed Fama-Bliss, 12, 33, 55 zero-coupon risk-free rate, 8, 25 zero-coupon yields, 57–60 DOI: 10.1057/9781137564863.0012 ... fees implied by the hedging strategies Barone Adesi, Giovanni and Nicola Carcano, eds Modern Multi- Factor Analysis of Bond Portfolios: Critical Implications for Hedging and Investing Basingstoke:... Modern multi- factor analysis of bond portfolios : critical implications for hedging and investing / [edited by] Giovanni Barone Adesi, Professor, Università della Svizzera Italiana, Switzerland,... capable of adding value over the abovementioned traditional models both for hedging and portfolio management Barone Adesi, Giovanni and Nicola Carcano, eds Modern Multi- Factor Analysis of Bond Portfolios: