Summary: 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.

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Enhanced descriptions from Syndetics:

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.

Includes bibliographical references and index.

Fundamentals -- Simulation -- Optimization.

Table of contents provided by Syndetics

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

Author notes provided by Syndetics

Manfred Gilli is Professor emeritus at the Department of Econometrics (now Economics) at the University of Geneva, Switzerland, where he taught numerical methods in economics and finance. His main research interests include numerical solution of large and sparse systems of equations, parallel computing, heuristic optimization techniques and numerical methods for pricing financial instruments. He is a member of the Advisory Board of the Computational Statistics and Data Analysis and a member of the editorial boards of Computational Economics and the Springer book series on Advances in Computational Economics and Advances in Computational Management Science. He also is a past president of the Society for Computational Economics.

Dietmar Maringer is Professor of Computational Economics and Finance at the University of Basel and University of Geneva, Switzerland. His research interests include heuristic optimization, computational and financial econometrics, high frequency trading and algo trading, and computational finance.

Enrico Schumann holds a Ph.D. in econometrics, an MSC in economics, and a BA in economics and law. He has written on accuracy and precision in finance, optimization cultures, and heuristics for portfolio selections, among other subjects.