Fixed Income Mathematics: Analytical and Statistical Techniques, Fifth Edition
- 9h 38m
- Francesco Fabozzi, Frank J. Fabozzi
- McGraw-Hill
- 2022
The standard reference for fixed income portfolio managers—fully updated with new analytical frameworks
Fixed Income Mathematics is known around the world as the leading guide to understanding the concepts, valuation models for bonds with embedded option, mortgage-backed securities, asset-backed securities, and other fixed income instruments, and portfolio analytics.
Fixed Income Mathematics begins with basic concepts of the mathematics of finance, then systematically builds on them to reveal state-of-the-art methodologies for evaluating them and managing fixed-income portfolios. Concepts are illustrated with numerical examples and graphs, and you need only a basic knowledge of elementary algebra to understand them.
This new edition includes several entirely new chapters?Risk-Adjusted Returns, Empirical Duration, Analysis of Floating-Rate Securities, Holdings-Based Return Attribution Analysis, Returns-Based Style Attribution Analysis, Measuring Bond Liquidity, and Machine Learning?and provides substantially revised chapters on:
- Interest rate modeling
- Probability theory
- Optimization models and applications to bond portfolio management
- Historical return measures
- Measuring historical return volatility
The concepts and methodologies for managing fixed income portfolios has improved dramatically over the past 15 years. This edition explains these changes and provides the knowledge you need to value fixed-income securities and measure the various types of risks associated with individual securities and portfolios.
About the Author
Frank J. Fabozzi, Ph.D., CFA, CPA, is professor of practice at the Carey Business School at Johns Hopkins University. He is the editor of The Journal of Portfolio Management, co-editor of the The Journal of Financial Data Science, and associate editor of The Journal of Fixed Income. and serves on the board of directors of the BlackRock Fixed Income Funds and the BlackRock BCIA Funds Board. Recipient of the CFA Institute’s 2007 C. Stewart Sheppard Award and 2015 James R. Vertin Award. In 2002, Frank was inducted into the Fixed Income Society’s Hall of Fame.
Francesco A. Fabozzi is the managing editor of The Journal of Financial Data Science and the director of Data Science for the CFA Institute Research Foundation. He has worked as a research associate at NYU’s Courant Institute in the Department of Mathematical Finance and is on the Curriculum Board of the Financial Data Professionals Institution. He has coauthored several books on asset management. He earned an undergraduate degree in economics from Princeton University, a master’s degree in Financial Analytics from the Stevens Institute of Technology, where he is an ABD doctoral student in data science.
In this Book
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Fifth versus Fourth Edition
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Introduction
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Future Value
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Present Value
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Yield (Internal Rate of Return)
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The Price of a Bond
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Bond Yield Measures
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The Yield Curve, Spot-Rate Curve, and Forward Rates
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Potential Sources of Dollar Return
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Total Return
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Historical Return Measures
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Risk-Adjusted Returns/Reward-Risk Ratios
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Price Volatility Properties of Option-Free Bonds
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Duration as a Measure of Price Volatility
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Combining Duration and Convexity to Measure Price Volatility
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Duration and the Yield Curve
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Empirical Duration
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Measuring Historical Return Volatility
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Measuring and Forecasting Yield Volatility
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Interest-Rate Modeling
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Call Options—Investment and Price Characteristics
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Valuation and Price Volatility of Bonds with Embedded Options
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Analysis of Floating-Rate Securities
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Credit Risk Concepts and Measures
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Measuring Bond Liquidity
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Cash-Flow Characteristics of Fixed-Rate Amortizing Mortgage Loans
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Cash-Flow Characteristics of Mortgage-Backed Securities
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Analysis of Agency Mortgage-Backed Securities
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Holdings-Based Performance Attribution Analysis
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Returns-Based Style Attribution Analysis
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Probability Distributions and Statistics
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Regression and Principal Component Analysis
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Multifactor Risk Models and Their Application to Portfolio Construction
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Monte Carlo Simulation
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Optimization Models
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Machine Learning
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Valuation of Bonds with Embedded Options
