This book describes computational finance tools. It covers fundamental numerical analysis and computational techniques, such as option pricing, and gives special attention to simulation and optimization. Many chapters are organized as case studies around portfolio insurance and risk estimation problems. In particular, several chapters explain optimization heuristics and how to use them for portfolio selection and in calibration of estimation and option pricing models. Such practical examples allow readers to learn the steps for solving specific problems and apply these steps to others. At the same time, the applications are relevant enough to make the book a useful reference. Matlab and R sample code is provided in the text and can be downloaded from the book's website.
1. Chapter 1: Introduction 2. Part One: Fundamentals 3. Chapter 2: Numerical Analysis in a Nutshell 4. Chapter 3: Linear Equations and Least Squares Problems 5. Chapter 4: Finite Difference Methods 6. Chapter 5: Binomial Trees 7. Part Two: Simulation 8. Chapter 6: Generating Random Numbers 9. Chapter 7: Modeling Dependencies 10. Chapter 8: A Gentle Introduction to Financial Simulation 11. Chapter 9: Financial Simulation at Work: Some Case Studies 12. Part Three: Optimization 13. Chapter 10: Optimization Problems in Finance 14. Chapter 11: Basic Methods 15. Chapter 12: Heuristic Methods in a Nutshell 16. Chapter 13: Portfolio Optimization 17. Chapter 14: Econometric Models 18. Chapter 15: Calibrating Option Pricing Models 19. Bibliography 20. Index
This book describes computational finance tools. It covers fundamental numerical analysis and computational techniques, such as option pricing, and gives special attention to simulation and optimization. Many chapters are organized as case studies around portfolio insurance and risk estimation problems. In particular, several chapters explain optimization heuristics and how to use them for portfolio selection and in calibration of estimation and option pricing models. Such practical examples allow readers to learn the steps for solving specific problems and apply these steps to others. At the same time, the applications are relevant enough to make the book a useful reference. Matlab and R sample code is provided in the text and can be downloaded from the book's website.
1. Chapter 1: Introduction 2. Part One: Fundamentals 3. Chapter 2: Numerical Analysis in a Nutshell 4. Chapter 3: Linear Equations and Least Squares Problems 5. Chapter 4: Finite Difference Methods 6. Chapter 5: Binomial Trees 7. Part Two: Simulation 8. Chapter 6: Generating Random Numbers 9. Chapter 7: Modeling Dependencies 10. Chapter 8: A Gentle Introduction to Financial Simulation 11. Chapter 9: Financial Simulation at Work: Some Case Studies 12. Part Three: Optimization 13. Chapter 10: Optimization Problems in Finance 14. Chapter 11: Basic Methods 15. Chapter 12: Heuristic Methods in a Nutshell 16. Chapter 13: Portfolio Optimization 17. Chapter 14: Econometric Models 18. Chapter 15: Calibrating Option Pricing Models 19. Bibliography 20. Index
