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. - Shows ways to build and implement tools that help test ideas - Focuses on the application of heuristics; standard methods receive limited attention - Presents as separate chapters problems from portfolio optimization, estimation of econometric models, and calibration of option pricing models
Autorentext
Manfred Gilli is Professor emeritus at the Geneva School of Economics and Management at the University of Geneva, Switzerland, where he has taught numerical methods in economics and finance. He is also a Faculty member of the Swiss Finance Institute, a member of the Advisory Board of Computational Statistics and Data Analysis, and a member of the editorial board of Computational Economics. He formerly served as president of the Society for Computational Economics.
Klappentext
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.
- Shows ways to build and implement tools that help test ideas
- Focuses on the application of heuristics; standard methods receive limited attention
- Presents as separate chapters problems from portfolio optimization, estimation of econometric models, and calibration of option pricing models
Inhalt
1. Introduction
I. Fundamentals
2. Numerical Analysis in a Nutshell
3. Linear Equations and Least-Squares Problems
4. Finite Difference Methods
5. Binomial Trees
II Simulation
6. Generating Random Numbers
7. Modelling Dependencies
8. A Gentle Introduction to Financial Simulation
9. Financial Simulation at Work: Some Case Studies
III Optimization
10. Optimization Problems in Finance
11. Basic Methods
12. Heuristic Methods in a Nutshell
13. Portfolio Optimization
14. Econometric Models
15. Calibrating Option Pricing Models