Numerical Methods and Optimization in Finance - 1st Edition - ISBN: 9780123756626, 9780123756633

Numerical Methods and Optimization in Finance

1st Edition

Authors: Manfred Gilli Dietmar Maringer Enrico Schumann
eBook ISBN: 9780123756633
Hardcover ISBN: 9780123756626
Imprint: Academic Press
Published Date: 11th July 2011
Page Count: 600
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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 Empirical Distributions
      • 6.8 Controlled Experiments and Experimental Design
    • Chapter Seven. Modeling Dependencies
      • Publisher Summary
      • 7.1 Transformation Methods
      • 7.2 Markov Chains
      • 7.3 Copula Models
    • Chapter Eight. A Gentle Introduction to Financial Simulation
      • Publisher Summary
      • 8.1 Setting the Stage
      • 8.2 Single-Period Simulations
      • 8.3 Simple Price Processes
      • 8.4 Processes with Memory in the Levels of Returns
      • 8.5 Time-Varying Volatility
      • 8.6 Adaptive expectations and patterns in Price Processes
      • 8.7 Historical Simulation
      • 8.8 Agent-based Models and Complexity
    • Chapter Nine. Financial Simulation at Work: Some Case Studies
      • Publisher Summary
      • 9.1 Constant proportion portfolio insurance (CPPI)
      • 9.2 VaR estimation with Extreme Value Theory
      • 9.3 Option pricing
  • Part Three: Optimization
    • Chapter Ten. Optimization Problems in Finance
      • Publisher Summary
      • 10.1 What to optimize?
      • 10.2 Solving the model
      • 10.3 Evaluating solutions
      • 10.4 Examples
      • 10.5 Summary
    • Chapter Eleven. Basic Methods
      • Publisher Summary
      • 11.1 Finding the Roots of f(x) = 0
      • 11.2 Classical Unconstrained Optimization
      • 11.3 Unconstrained Optimization in One Dimension
      • 11.4 Unconstrained Optimization in Multiple Dimensions
      • 11.5 Nonlinear Least Squares
      • 11.6 Solving Systems of Nonlinear Equations F(x) = 0
      • 11.7 Synoptic View of Solution Methods
    • Chapter Twelve. Heuristic Methods in a Nutshell
      • Publisher Summary
      • 12.1 Heuristics
      • 12.2 Trajectory Methods
      • 12.3 Population-Based Methods
      • 12.4 Hybrids
      • 12.5 Constraints
      • 12.6 The Stochastics of Heuristic Search
      • 12.7 General Considerations
      • 12.8 Summary
      • 12.A Implementing Heuristic Methods with Matlab
    • Chapter Thirteen. Portfolio Optimization
      • Publisher Summary
      • 13.1 The Investment Problem
      • 13.2 The Classical Case: Mean–Variance Optimization
      • 13.3 Heuristic Optimization of One-Period Models
      • 13.A More Implementation Issues in R
    • Chapter Fourteen. Econometric Models
      • Publisher Summary
      • 14.1 Term Structure Models
      • 14.2 Robust and Resistant Regression
      • 14.A Maximizing the Sharpe Ratio
    • Chapter Fifteen. Calibrating Option Pricing Models
      • Publisher Summary
      • 15.1 Implied volatility with Black–Scholes
      • 15.2 Pricing with the characteristic function
      • 15.3 Calibration
      • 15.4 Final remarks
      • 15.A Quadrature rules for infinity
  • Bibliography
  • Index


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© Academic Press 2011
Academic Press
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About the Author

Manfred Gilli

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.

Affiliations and Expertise

University of Geneva, Switzerland; and Swiss Finance Institute

Dietmar Maringer

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.

Affiliations and Expertise

University of Basel and University of Geneva, Switzerland

Enrico Schumann

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.

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