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Practical Financial Optimization: Decision Making for Financial Engineers

Book Description

Practical Financial Optimization is a comprehensive guide to optimization techniques in financial decision making. This book illuminates the relationship between theory and practice, providing the readers with solid foundational knowledge.

  • Focuses on classical static mean-variance analysis and portfolio immunization, scenario-based models, multi-period dynamic portfolio optimization, and the relationships between classes of models

  • Analyizes real world applications and implications for financial engineers

  • Includes a list of models and a section on notations that includes a glossary of symbols and abbreviations

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Dedication
  5. Contents
  6. Foreword
  7. Preface
  8. Acknowledgments
  9. Text Credits
  10. Notation
  11. List of Models
  12. PART I: INTRODUCTION
    1. Chapter 1: An Optimization View of Financial Engineering
      1. 1.1 Preview
      2. 1.2 Optimization in Financial Engineering
      3. 1.3 Enterprise-Wide Risk Management
      4. 1.4 The Scope for Optimization in Enterprise-Wide Risk Management
      5. 1.5 Overview of Financial Optimization Models
      6. 1.6 Postview
      7. Notes and References
    2. Chapter 2: Basics of Risk Management
      1. 2.1 Preview
      2. 2.2 A Classification of Financial Risks
      3. 2.3 Risk Measurement for Equities
      4. 2.4 Risk Measurement for Fixed-Income Securities
      5. 2.5 Scenario Analysis for Fixed-Income Securities
      6. 2.6 Enterprise-Wide Risk Measurement
      7. 2.7 Coherent Risk Measurement
      8. 2.8 Measurement of Reward and Performance Evaluation
      9. 2.9 Classification of Risk Management Models
      10. 2.10 Postview
      11. Notes and References
  13. PART II: PORTFOLIO OPTIMIZATION MODELS
    1. Chapter 3: Mean-Variance Analysis
      1. 3.1 Preview
      2. 3.2 Mean-Variance Optimization
      3. 3.3 Incorporating Liabilities
      4. 3.4 Factor Models of Return
      5. 3.5 Are Optimized Portfolios Optimal?
      6. 3.6 Postview
      7. Notes and References
    2. Chapter 4: Portfolio Models for Fixed Income
      1. 4.1 Preview
      2. 4.2 Portfolio Dedication
      3. 4.3 Portfolio Immunization
      4. 4.4 Factor Immunization
      5. 4.5 Factor Immunization for Corporate Bonds
      6. 4.6 Postview
      7. Notes and References
    3. Chapter 5: Scenario Optimization
      1. 5.1 Preview
      2. 5.2 Basics of Scenario Optimization
      3. 5.3 Mean Absolute Deviation Models
      4. 5.4 Regret Models
      5. 5.5 Conditional Value-at-Risk Models
      6. 5.6 Expected Utility Maximization
      7. 5.7 Put/Call Efficient Frontiers
      8. 5.8 Asset Valuation using Scenario Optimization
      9. 5.9 Postview
      10. Notes and References
    4. Chapter 6: Dynamic Portfolio Optimization with Stochastic Programming
      1. 6.1 Preview
      2. 6.2 Setting the Stage for Dynamic Models
      3. 6.3 Decision Rules for Dynamic Portfolio Strategies
      4. 6.4 Stochastic Dedication
      5. 6.5 Basic Concepts of Stochastic Programming
      6. 6.6 Stochastic Programming for Dynamic Portfolio Strategies
      7. 6.7 Comparison of Stochastic Programming with Other Methods
      8. 6.8 Postview
      9. Notes and References
    5. Chapter 7: Index Funds
      1. 7.1 Preview
      2. 7.2 Basics of Market Indices
      3. 7.3 Indexation Models
      4. 7.4 Models for International Index Funds
      5. 7.5 Models for Corporate Bond Index Funds
      6. 7.6 Stochastic Programming for Index Funds
      7. 7.7 Applications of Indexation Models
      8. 7.8 Postview
      9. Notes and References
    6. Chapter 8: Designing Financial Products
      1. 8.1 Preview
      2. 8.2 Financial Innovation
      3. 8.3 Financial Product Novelties
      4. 8.4 A Framework for Financial Product Design
      5. 8.5 Optimal Design of Callable Bonds
      6. 8.6 Postview
      7. Notes and References
    7. Chapter 9: Scenario Generation
      1. 9.1 Preview
      2. 9.2 Scenarios and their Properties
      3. 9.3 A Framework for Scenario Generation
      4. 9.4 Scenario Generation Methodologies
      5. 9.5 Constructing Event Trees
      6. 9.6 Postview
      7. Notes and References
  14. PART III: APPLICATIONS
    1. Chapter 10: International Asset Allocation
      1. 10.1 Preview
      2. 10.2 The Risks of International Asset Portfolios
      3. 10.3 Hedging Strategies
      4. 10.4 Statistical Characteristics of International Data
      5. 10.5 Model for Selective Hedging
      6. 10.6 Asset Allocation
      7. 10.7 Risk Measure for International Asset Allocation
      8. 10.8 Postview
      9. Notes and References
    2. Chapter 11: Corporate Bond Portfolios
      1. 11.1 Preview
      2. 11.2 Credit Risk Securities
      3. 11.3 Integrating Market and Credit Risk
      4. 11.4 Optimizing the Right Risk Metric
      5. 11.5 Index Funds for Corporate Bond Portfolios
      6. 11.6 Tracking the Merrill Lynch Euro Dollar Corporate Bond Index
      7. 11.7 Funding Liabilities with Corporate Bonds
      8. 11.8 Postview
      9. Notes and References
    3. Chapter 12: Insurance Policies with Guarantees
      1. 12.1 Preview
      2. 12.2 Participating Policies with Guarantees
      3. 12.3 The Italian Insurance Industry
      4. 12.4 The Scenario Optimization Model
      5. 12.5 Model Testing and Validation
      6. 12.6 Postview
      7. Notes and References
    4. Chapter 13: Personal Financial Planning
      1. 13.1 Preview
      2. 13.2 The Demand for Personal Financial Planning
      3. 13.3 The Provision of Financial Services
      4. 13.4 Web-Based Personal Financial Tools
      5. 13.5 Model for Personal Financial Planning
      6. 13.6 Model Validation and Testing
      7. 13.7 The Integrated Decision Support System
      8. 13.8 Postview
      9. Notes and References
  15. PART IV: LIBRARY OF FINANCIAL OPTIMIZATION MODELS
    1. Chapter 14: FINLIB: A Library of Financial Optimization Models
      1. 14.1 Preview
      2. 14.2 FINLIB: Financial Optimization Library
      3. 14.3 Studio Designs: Project Suggestions
      4. Notes and References
    2. Appendix A: Basics of Optimization
      1. A.1 Duality
      2. A.2 Optimality Conditions
      3. A.3 Lagrange Multipliers
    3. Appendix B: Basics of Probability Theory
      1. B.1 Probability Spaces
    4. Appendix C: Stochastic Processes
      1. C.1 The Poisson Process
      2. C.2 The Gaussian Process
      3. C.3 The Wiener Process
      4. C.4 Markov Chains
  16. Bibliography
  17. Index