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Fixed-income portfolio analytics : a practical guide to implementing, monitoring and understanding fixed-income portfolios /

The book offers a detailed, robust, and consistent framework for the joint consideration of portfolio exposure, risk, and performance across a wide range of underlying fixed-income instruments and risk factors. Through extensive use of practical examples, the author also highlights the necessary tec...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Bolder, David (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer, [2015]
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Bolder, David,  |e author. 
245 1 0 |a Fixed-income portfolio analytics :  |b a practical guide to implementing, monitoring and understanding fixed-income portfolios /  |c David Jamieson Bolder. 
264 1 |a Cham :  |b Springer,  |c [2015] 
264 4 |c ©2015 
300 |a 1 online resource 
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504 |a Includes bibliographical references and indexes. 
588 0 |a Online resource; title from PDF title page (EBSCO, viewed February 9, 2015). 
520 |a The book offers a detailed, robust, and consistent framework for the joint consideration of portfolio exposure, risk, and performance across a wide range of underlying fixed-income instruments and risk factors. Through extensive use of practical examples, the author also highlights the necessary technical tools and the common pitfalls that arise when working in this area. Finally, the book discusses tools for testing the reasonableness of the key analytics to help build and maintain confidence for using these techniques in day-to-day decision making. This will be of keen interest to risk managers, analysts and asset managers responsible for fixed-income portfolios. ℗ℓ ℗ℓ 
505 0 |6 880-01  |a What Is Portfolio Analytics?- From Risk Factors to Returns: Computing Exposures -- A Useful Approximation -- Extending Our Framework -- The Yield Curve: Fitting Yield Curves -- Modelling Yield Curves -- Performance: Basic Performance Attribution -- Advanced Performance Attribution -- Traditional Performance Attribution -- Risk: Introducing Risk -- Portfolio Risk -- Exploring Uncertainty in Risk Measurement -- Risk and Performance: Combining Risk and Return -- The Ex-Post World -- Appendix: Some Mathematical Background -- A Few Thoughts on Optimization -- Index. 
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650 0 |a Portfolio management. 
650 0 |a Fixed-income securities. 
650 1 4 |a Finance. 
650 2 4 |a Finance, general. 
650 2 4 |a Quantitative Finance. 
650 2 4 |a Wealth Management/Pension Planning. 
650 2 4 |a Management. 
650 6 |a Gestion de portefeuille. 
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650 7 |a Portfolio management  |2 fast 
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880 0 |6 505-00/(S  |a 10. Introducing Risk -- 10.1. Defining Risk -- 10.1.1. Determining Outcomes -- 10.1.2. Assigning Probabilities -- 10.1.3. Getting to Risk -- 10.2.A Simple Example -- 10.3.A More Complicated Example -- 10.3.1. Enter the Distribution -- 10.3.2. Relaxing Normality -- 10.3.3. The Role of Dependence -- 10.4.A Specific Risk Measure -- 10.4.1. Looking Backwards -- 10.4.2. Looking Forward -- 10.4.3.Comparing Forward- and Backward-Looking Perspectives -- 10.5. Using Tracking Error -- 10.6. Concluding Thoughts -- References -- 11. Portfolio Risk -- 11.1. The Punchline -- 11.2. Getting Started -- 11.2.1. Portfolio Weights -- 11.2.2. Incorporating Risk-Factor Exposures -- 11.2.3. Handling Market Movements -- 11.2.4.Computing Return Distributions -- 11.3. Understanding and Exploring ΩR -- 11.3.1. Variance 101 -- 11.3.2. Linking Covariance and Correlation -- 11.3.3. Classic and Alternative Estimators of ΩR -- 11.3.4. Simulating Random Realizations -- 11.4. The Final Results. 
880 0 |6 505-01/(S  |a 14.2. Some Theory -- 14.2.1. Introducing β -- 14.2.2. Introducing α -- 14.2.3.α and β -- 14.3. Relative Risk -- 14.4. Risk-Adjusted Ratios -- 14.5. Beyond CAPM -- 14.6. Bringing It All Together -- 14.7. Concluding Thoughts -- References -- A.1. Set Theory -- A.2. Probability -- A.2.1. Conditional Probability -- A.2.2. Independence -- A.3. Statistics -- A.3.1. Distributions and Densities -- A.3.2. Working with Distribution and Density Functions -- A.3.3. Some Sample Statistical Distributions -- A.3.4. Multivariate Statistics -- A.4. Matrix Theory -- A.4.1. Solving Linear Systems -- A.4.2. Cholesky Decomposition -- A.4.3. Eigenvalues and Eigenvectors -- References -- B.1.A Linear Program -- B.1.1.A Simple Case -- B.1.2. Extending the Simple Case -- B.2. Concluding Thoughts -- References. 
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