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.

Key Features

  • 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


Graduate students studying quantitative or computational finance, as well as finance professionals, especially in banking and insurance.

Table of Contents

  • List of Algorithms
  • Acknowledgements
  • Chapter One. Introduction
    • Publisher Summary
    • 1.1 About this book
    • 1.2 Principles
    • 1.3 On software
    • 1.4 On approximations and accuracy
    • 1.5 Summary: the theme of the book
  • Part One: Fundamentals
    • Chapter Two. Numerical Analysis in a Nutshell
      • Publisher Summary
      • 2.1 Computer Arithmetic
      • 2.2 Measuring Errors
      • 2.3 Approximating Derivatives with Finite Differences
      • 2.4 Numerical Instability and Ill-Conditioning
      • 2.5 Condition Number of a Matrix
      • 2.6 A Primer on Algorithmic and Computational Complexity
      • 2.A Operation Count for Basic Linear Algebra Operations
    • Chapter Three. Linear Equations and Least Squares Problems
      • Publisher Summary
      • 3.1 Direct Methods
      • 3.2 Iterative Methods
      • 3.3 Sparse Linear Systems
      • 3.4 The Least Squares Problem
    • Chapter Four. Finite Difference Methods
      • Publisher Summary
      • 4.1 An example of a numerical solution
      • 4.2 Classification of differential equations
      • 4.3 The Black–Scholes equation
      • 4.4 American options
      • 4.A A note on Matlab's function spdiags
    • Chapter Five. Binomial Trees
      • Publisher Summary
      • 5.1 Motivation
      • 5.2 Growing the Tree
      • 5.3 Early Exercise
      • 5.4 Dividends
      • 5.5 The Greeks
  • Part Two: Simulation
    • Chapter Six. Generating Random Numbers
      • Publisher Summary
      • 6.1 Monte Carlo Methods and Sampling
      • 6.2 Uniform Random Number Generators
      • 6.3 Nonuniform Distributions
      • 6.4 Specialized Methods for Selected Distributions
      • 6.5 Sampling from a Discrete Set
      • 6.6 Sampling Errors—and How to Reduce them
      • 6.7 Drawing from Empiri


No. of pages:
© 2011
Academic Press
Print ISBN:
Electronic ISBN:

About the authors

Manfred Gilli

Affiliations and Expertise

University of Geneva, Switzerland; and Swiss Finance Institute

Dietmar Maringer

Affiliations and Expertise

University of Basel and University of Geneva, Switzerland

Enrico Schumann

VIP Value Investment Professionals, Switzerland

Affiliations and Expertise

VIP Value Investment Professionals AG, Switzerland


"This book aims at providing guidance which is practical and useful for practitioners in finance with emphasis on computational techniques which are manageable by modern day desktop personal computers’ processing power when building, testing, comparing and using mathematical and econometric models of finance in the pursuit of analysis of actual financial market data in day to day activities of financial analysts, be they students of courses in finance programs or analysts in financial institutions."--Zentralblatt MATH 2012-1236-91001
"With as much rigor as can be mastered by anyone in the still-developing field of computational finance and a sense of humor, the authors unravel its mysteries. The presentations are clear and the models are practical --- these are the two ingredients that make for a valuable book in this field. The book is both practical in scope and rigorous on its theoretical foundations. It  is a must for anyone who needs to apply quantitative methods for financial planning --- and who doesn’t need to in our days?"--Stavros A. Zenios, University of Cyprus and the Wharton Financial Institutions Center
Numerical Methods and Optimization in Finance is an excellent introduction to computational science. The combination of methodology, software, and examples allows the reader to quickly grasp and apply serious computational ideas."--Kenneth L. Judd, Hoover Institution, Stanford University