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The definitive guide to fixed income valuation and risk analysis The Trilogy in Fixed Income Valuation and Risk Analysis comprehensively covers the most definitive work on interest rate risk, term structure analysis, and credit risk. The first book on interest rate risk modeling examines virtually every well-known IRR model used for pricing and risk analysis of various fixed income securities and their derivatives. The companion CD-ROM contain numerous formulas and programming tools that allow readers to better model risk and value fixed income securities. This comprehensive resource provides readers with the hands-on information and software needed to succeed in this financial arena.
Presenting the most advanced thinking on the topic, this book covers the latest valuation models and techniques. It addresses essential topics such as the subtleties of fixed-income mathematics, new approaches to modeling term structures, and the applications of fixed-income valuation on credit risk, mortgages, munis, and indexed bonds.
This study compares continuous-time stochastic interest rate and stochastic volatility models of interest rate derivatives, examining these models across several dimensions: different classes of models, factor structures, and pricing algorithms. We consider a broader universe of pricing models, using improved econometric and numerical methodologies. We establish several criteria for model quality that are motivated by financial theory as well as practice: realism of the assumed stochastic process for the term structure, consistency with no-arbitrage or financial market equilibrium, consistency with financial practice, parsimony, as well as computational efficiency. A model which scores well along these grounds will also exhibit superior pricing performance with regard to traded interest rate options. This helps resolve the controversies over the stochastic process for yield curve dynamics, the models that best manage and measure interest rate risk, and theories of the term structure that are supported by empirical results. We perform econometric experiments at three levels: the short rate, bond prices, as well as interest rate derivatives. We extend CKLS (1992) to a broader class of single factor spot rate models and international interest rates. We find that a single-factor general parametric model (1FGPM) of the term structure, with non-linearity in the drift function, better captures the time series dynamics of US 30 Day T-Bill rates. The 1FGPM not only forecasts interest rate changes out-of-sample better relative to other parametric models, but also relative to the non-parametric model of Jiang (1998). Finally, our results vary greatly across international markets. Building upon the work of Longstaff and Schwartz (1992), we perform a statistical analysis of the U.S. default-free term structure over the period 4:1964 to 10:1997. We utilize a constant correlation multivariate GARCH principal components analysis (CCM-PCA), and identify at least three factors associated with traditional measures of risk in the fixed income literature (level, slope, and curvature) that capture 98% of the variation in the default-free term structure. We perform tests of various term structure models on US Treasury bonds, comparing a two factor Cox-Ingersoll-Ross (2FCIR) model with a multi-layer perceptron neural network approach (MLP-ANN), in pricing and hedging discount bonds. We find that while the MLP-ANN can better fit bond prices in-sample, the 2F-CIR model is superior in hedging against unanticipated changes in the short rate and its volatility. Furthermore, we find the 2FCIR model to perform favorably in comparison to the CCM-PCA, MLP-ANN, as well as the 1FGPM in forecasting bond yield changes. Finally, we compare various interest rate bond option pricing models, in their ability to price interest rate derivatives and manage and interest rate risk. We compare three approaches to pricing interest rate derivatives: spot rate (e.g., CIR), forward-rate (i.e., HJM), and non-parametric models (e.g., multivariate kernel estimation.) This is extended to a broader factor structure. While the best model in terms of mean square error (MSE) is the non parametric (MNWK) model, the 3 factor jump diffusion (3FGJD) model performs best among parametric models. In hedging analysis, while these preferred models still outperform within each grouping, the non parametric model is no longer the best performing model, while the 2FCIR is the best model in hedging options in terms of MSE.
The essential guide to fixed income portfolio management, from the experts at CFA Fixed Income Analysis is a new edition of Frank Fabozzi's Fixed Income Analysis, Second Edition that provides authoritative and up-to-date coverage of how investment professionals analyze and manage fixed income portfolios. With detailed information from CFA Institute, this guide contains comprehensive, example-driven presentations of all essential topics in the field to provide value for self-study, general reference, and classroom use. Readers are first introduced to the fundamental concepts of fixed income before continuing on to analysis of risk, asset-backed securities, term structure analysis, and a general framework for valuation that assumes no prior relevant background. The final section of the book consists of three readings that build the knowledge and skills needed to effectively manage fixed income portfolios, giving readers a real-world understanding of how the concepts discussed are practically applied in client-based scenarios. Part of the CFA Institute Investment series, this book provides a thorough exploration of fixed income analysis, clearly presented by experts in the field. Readers gain critical knowledge of underlying concepts, and gain the skills they need to translate theory into practice. Understand fixed income securities, markets, and valuation Master risk analysis and general valuation of fixed income securities Learn how fixed income securities are backed by pools of assets Explore the relationships between bond yields of different maturities Investment analysts, portfolio managers, individual and institutional investors and their advisors, and anyone with an interest in fixed income markets will appreciate this access to the best in professional quality information. For a deeper understanding of fixed income portfolio management practices, Fixed Income Analysis is a complete, essential resource.
THE THOROUGHLY REVISED AND UPDATED FOURTH EDITION OF THE COMPANION WORKBOOK TO FIXED INCOME ANALYSIS Now in its fourth edition, the Fixed Income Analysis Workbook offers a range of practical information and exercises that will enhance your understanding of the tools, strategies, and techniques associated with fixed-income portfolio management. Written by a team of knowledgeable contributors, this hands-on resource helps busy professionals and those new to the discipline apply the concepts and methodologies that are essential for mastery. The Workbook is an accessible guide for understanding the metrics, methods, and mechanics as applied in the competitive world of fixed-income analysis. It also provides a stress-free way to practice the tools and techniques described in the companion text. The Fixed Income Analysis Workbook includes information and exercises to help you: Work real-world problems associated with fixed-income risk and return Review the fundamentals of asset-backed securities Comprehend the principles of credit analysis Understand the arbitrage-free valuation framework Practice important methods and techniques before applying them in actual situations The fourth edition provides updated coverage of fixed income portfolio management including detailed applications of liability-driven and index-based strategies, exposure to the major types of yield curve strategies, and practical approaches to implementing active credit strategies. For anyone who wants a more solid understanding of fixed-income portfolio management, the Fixed Income Analysis Workbook is a comprehensive and practical resource.
CFA Institute's essential guide to fixed-income portfolio management, revised and updated Now in its fourth edition, Fixed Income Analysis offers authoritative and up-to-date coverage of how successful investment professionals analyze and manage fixed-income portfolios. With contributions from a team of financial experts, the text is filled with detailed information from CFA Institute and contains a comprehensive review of the essential topics in the field. Fixed Income Analysis introduces the fundamental concepts of fixed-income securities and markets and provides in-depth coverage of fixed-income security valuation and portfolio management. The book contains a general framework for valuation that is designed to be accessible to both professionals and those new to the field. The fourth edition provides updated coverage of fixed-income portfolio management including detailed coverage of liability-driven and index-based strategies, the major types of yield curve strategies, and approaches to implementing active credit strategies. The authors include examples that help build the knowledge and skills needed to effectively manage fixed-income portfolios. Fixed Income Analysis gives a real-world understanding of how the concepts discussed are practically applied in client-based scenarios. Investment analysts, portfolio managers, individual and institutional investors and their advisors, and anyone with an interest in fixed-income markets will appreciate this accessible guide to fixed-income analysis.
Advances in Fixed Income Valuation Modeling and Risk Management provides in-depth examinations by thirty-one expert research and opinion leaders on topics such as: problems encountered in valuing interest rate derivatives, tax effects in U.S. government bond markets, portfolio risk management, valuation of treasury bond futures contract's embedded options, and risk analysis of international bonds.
The global fixed income market is an enormous financial market whose value by far exceeds that of the public stock markets. The interbank market consists of interest rate derivatives, whose primary purpose is to manage interest rate risk. The credit market primarily consists of the bond market, which links investors to companies, institutions, and governments with borrowing needs. This dissertation takes an optimization perspective upon modeling both these areas of the fixed-income market. Legislators on the national markets require financial actors to value their financial assets in accordance with market prices. Thus, prices of many assets, which are not publicly traded, must be determined mathematically. The financial quantities needed for pricing are not directly observable but must be measured through solving inverse optimization problems. These measurements are based on the available market prices, which are observed with various degrees of measurement noise. For the interbank market, the relevant financial quantities consist of term structures of interest rates, which are curves displaying the market rates for different maturities. For the bond market, credit risk is an additional factor that can be modeled through default intensity curves and term structures of recovery rates in case of default. By formulating suitable optimization models, the different underlying financial quantities can be measured in accordance with observable market prices, while conditions for economic realism are imposed. Measuring and managing risk is closely connected to the measurement of the underlying financial quantities. Through a data-driven method, we can show that six systematic risk factors can be used to explain almost all variance in the interest rate curves. By modeling the dynamics of these six risk factors, possible outcomes can be simulated in the form of term structure scenarios. For short-term simulation horizons, this results in a representation of the portfolio value distribution that is consistent with the realized outcomes from historically observed term structures. This enables more accurate measurements of interest rate risk, where our proposed method exhibits both lower risk and lower pricing errors compared to traditional models. We propose a method for decomposing changes in portfolio values for an arbitrary portfolio into the risk factors that affect the value of each instrument. By demonstrating the method for the six systematic risk factors identified for the interbank market, we show that almost all changes in portfolio value and portfolio variance can be attributed to these risk factors. Additional risk factors and approximation errors are gathered into two terms, which can be studied to ensure the quality of the performance attribution, and possibly improve it. To eliminate undesired risk within trading books, banks use hedging. Traditional methods do not take transaction costs into account. We, therefore, propose a method for managing the risks in the interbank market through a stochastic optimization model that considers transaction costs. This method is based on a scenario approximation of the optimization problem where the six systematic risk factors are simulated, and the portfolio variance is weighted against the transaction costs. This results in a method that is preferred over the traditional methods for all risk-averse investors. For the credit market, we use data from the bond market in combination with the interbank market to make accurate measurements of the financial quantities. We address the notoriously difficult problem of separating default risk from recovery risk. In addition to the previous identified six systematic risk factors for risk-free interests, we identify four risk factors that explain almost all variance in default intensities, while a single risk factor seems sufficient to model the recovery risk. Overall, this is a higher number of risk factors than is usually found in the literature. Through a simple model, we can measure the variance in bond prices in terms of these systematic risk factors, and through performance attribution, we relate these values to the empirically realized variances from the quoted bond prices. De globala ränte- och kreditmarknaderna är enorma finansiella marknader vars sammanlagda värden vida överstiger de publika aktiemarknadernas. Räntemarknaden består av räntederivat vars främsta användningsområde är hantering av ränterisker. Kreditmarknaden utgörs i första hand av obligationsmarknaden som syftar till att förmedla pengar från investerare till företag, institutioner och stater med upplåningsbehov. Denna avhandling fokuserar på att utifrån ett optimeringsperspektiv modellera både ränte- och obligationsmarknaden. Lagstiftarna på de nationella marknaderna kräver att de finansiella aktörerna värderar sina finansiella tillgångar i enlighet med marknadspriser. Därmed måste priserna på många instrument, som inte handlas publikt, beräknas matematiskt. De finansiella storheter som krävs för denna prissättning är inte direkt observerbara, utan måste mätas genom att lösa inversa optimeringsproblem. Dessa mätningar görs utifrån tillgängliga marknadspriser, som observeras med varierande grad av mätbrus. För räntemarknaden utgörs de relevanta finansiella storheterna av räntekurvor som åskådliggör marknadsräntorna för olika löptider. För obligationsmarknaden utgör kreditrisken en ytterligare faktor som modelleras via fallissemangsintensitetskurvor och kurvor kopplade till förväntat återvunnet kapital vid eventuellt fallissemang. Genom att formulera lämpliga optimeringsmodeller kan de olika underliggande finansiella storheterna mätas i enlighet med observerbara marknadspriser samtidigt som ekonomisk realism eftersträvas. Mätning och hantering av risker är nära kopplat till mätningen av de underliggande finansiella storheterna. Genom en datadriven metod kan vi visa att sex systematiska riskfaktorer kan användas för att förklara nästan all varians i räntekurvorna. Genom att modellera dynamiken i dessa sex riskfaktorer kan tänkbara utfall för räntekurvor simuleras. För kortsiktiga simuleringshorisonter resulterar detta i en representation av fördelningen av portföljvärden som väl överensstämmer med de realiserade utfallen från historiskt observerade räntekurvor. Detta möjliggör noggrannare mätningar av ränterisk där vår föreslagna metod uppvisar såväl lägre risk som mindre prissättningsfel jämfört med traditionella modeller. Vi föreslår en metod för att dekomponera portföljutvecklingen för en godtycklig portfölj till de riskfaktorer som påverkar värdet för respektive instrument. Genom att demonstrera metoden för de sex systematiska riskfaktorerna som identifierats för räntemarknaden visar vi att nästan all portföljutveckling och portföljvarians kan härledas till dessa riskfaktorer. Övriga riskfaktorer och approximationsfel samlas i två termer, vilka kan användas för att säkerställa och eventuellt förbättra kvaliteten i prestationshärledningen. För att eliminera oönskad risk i sina tradingböcker använder banker sig av hedging. Traditionella metoder tar ingen hänsyn till transaktionskostnader. Vi föreslår därför en metod för att hantera riskerna på räntemarknaden genom en stokastisk optimeringsmodell som också tar hänsyn till transaktionskostnader. Denna metod bygger på en scenarioapproximation av optimeringsproblemet där de sex systematiska riskfaktorerna simuleras och portföljvariansen vägs mot transaktionskostnaderna. Detta resulterar i en metod som, för alla riskaverta investerare, är att föredra framför de traditionella metoderna. På kreditmarknaden använder vi data från obligationsmarknaden i kombination räntemarknaden för att göra noggranna mätningar av de finansiella storheterna. Vi angriper det erkänt svåra problemet att separera fallissemangsrisk från återvinningsrisk. Förutom de tidigare sex systematiska riskfaktorerna för riskfri ränta, identifierar vi fyra riskfaktorer som förklarar nästan all varians i fallissemangsintensiteter, medan en enda riskfaktor tycks räcka för att modellera återvinningsrisken. Sammanlagt är detta ett större antal riskfaktorer än vad som brukar användas i litteraturen. Via en enkel modell kan vi mäta variansen i obligationspriser i termer av dessa systematiska riskfaktorer och genom prestationshärledningen relatera dessa värden till de empiriskt realiserade varianserna från kvoterade obligationspriser.
As western governments issue increasing amounts of debt, the fixed income markets have never been more important. Yet the methods for analyzing these markets have failed to keep pace with recent developments, including the deterioration in the credit quality of many sovereign issuers. In Fixed Income Relative Value Analysis, Doug Huggins and Christian Schaller address this gap with a set of analytic tools for assessing value in the markets for government bonds, interest rate swaps, and related basis swaps, as well as associated futures and options. Taking a practitioner’s point of view, the book presents the theory behind market analysis in connection with tools for finding and expressing trade ideas. The extensive use of actual market examples illustrates the ways these analytic tools can be applied in practice. The book covers: Statistical models for quantitative market analysis, in particular mean reversion models and principal component analysis. An in-depth approach to understanding swap spreads in theory and in practice. A comprehensive discussion of the various basis swaps and their combinations. The incorporation of credit default swaps in yield curve analysis. A classification of option trades, with appropriate analysis tools for each category. Fitted curve techniques for identifying relative value among different bonds. A multi-factor delivery option model for bond future contracts. Fixed Income Relative Value Analysis provides an insightful presentation of the relevant statistical and financial theories, a detailed set of statistical and financial tools derived from these theories, and a multitude of actual trades resulting from the application of these tools to the fixed income markets. As such, it’s an indispensable guide for relative value analysts, relative value traders, and portfolio managers for whom security selection and hedging are part of the investment process.
This textbook will be designed for fixed-income securities courses taught on MSc Finance and MBA courses. There is currently no suitable text that offers a 'Hull-type' book for the fixed income student market. This book aims to fill this need. The book will contain numerous worked examples, excel spreadsheets, with a building block approach throughout. A key feature of the book will be coverage of both traditional and alternative investment strategies in the fixed-income market, for example, the book will cover the modern strategies used by fixed-income hedge funds. The text will be supported by a set of PowerPoint slides for use by the lecturer First textbook designed for students written on fixed-income securities - a growing market Contains numerous worked examples throughout Includes coverage of important topics often omitted in other books i.e. deriving the zero yield curve, deriving credit spreads, hedging and also covers interest rate and credit derivatives