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© 2008

Implementing Models in Quantitative Finance: Methods and Cases

Benefits

  • Fills a gap in the current published literature by delivering a case-study collection together with a self-contained course on major numerical methods developed and used by the finance industry

  • Learning-by-doing approach: all steps detailed in a self-contained way

  • Covers a range of numerical methods

  • Blends theoretical presentation and practical implementations

  • Originality in the choice of cases

  • Provides detailed algorithm and the corresponding code

Book

Part of the Springer Finance book series (FINANCE)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Methods

  3. Problems

    1. Front Matter
      Pages 269-269
    2. Portfolio Management and Trading

    3. Vanilla Options

    4. Exotic Derivatives

About this book

Introduction

This book puts numerical methods into action for the purpose of solving concrete problems arising in quantitative finance. Part one develops a comprehensive toolkit including Monte Carlo simulation, numerical schemes for partial differential equations, stochastic optimization in discrete time, copula functions, transform-based methods and quadrature techniques. The content originates from class notes written for courses on numerical methods for finance and exotic derivative pricing held by the authors at Bocconi University since the year 2000. Part two proposes eighteen self-contained cases covering model simulation, derivative valuation, dynamic hedging, portfolio selection, risk management, statistical estimation and model calibration. It encompasses a wide variety of problems arising in markets for equity, interest rates, credit risk, energy and exotic derivatives. Each case introduces a problem, develops a detailed solution and illustrates empirical results. Proposed algorithms are implemented using either Matlab® or Visual Basic for Applications® in collaboration with contributors.

Keywords

JEL: G11, G13, C15, C22, C63 MATLAB Monte Carlo simulation Numerical methods in finance STATISTICA Simulation Stochastic Optimization algorithms copula financial engineering linear optimization optimization partial differential equation partial differential equations

Authors and affiliations

  1. 1.Dipartimento di Scienze Economiche e Metodi Quantitativi Facoltà di EconomiaUniversità del Piemonte Orientale “A. Avogadro”NovaraItaly
  2. 2.Finance DepartmentESSEC Graduate Business SchoolCergy Pontoise CedexFrance

About the authors

Gianluca Fusai is Associate Professor in Financial Calculus at Università degli Studi del Piemonte Orientale (Italy) and a Research Associate at Financial Options Research Center, Univeristy of Warwick. He holds a Ph.D in Finance from the Warwick Business School and a MS in Statistics and Operational Research from University of Essex, UK. His research interest are Financial Engineering, Numerical Methods, Portfolio Selection, and Financial Statistics. On this topics he has published in journals like Journal of Computational Finance, Risk, Annals of Applied Probability, International Journal of Theoretical and Applied Finance. He has worked as a consultant in the private sector (Mediolanum Assicurazioni, Selenia Luxco, Nike Consulting, Software Company, Equitable House).

Andrea Roncoroni is Associate Professor of Finance at ESSEC Business School (Paris-Singapore), Senior Lecturer at Bocconi University (Milan), and Co-director of the Master in Energy Finance at MIP - Politecnico di Milano. He holds PhDs in Applied Mathematics and in Finance. His research interests cover Energy and Commodity Finance, Financial Modeling, Risk Management and Derivative Structuring. He consults for private companies and lectures for private and public institutions (International Energy Agency, Italian Stock Exchange, Italian Energy Authority, Italian Power Exchange, University Paris Dauphine, University of Oslo). He regularly publishes in academic journals (J.of Business, J.of Banking and Finance, Intl.J.of Business).

Bibliographic information

Industry Sectors
Engineering
Finance, Business & Banking

Reviews

From the reviews:

"As the title suggests the book is divided into two parts. … The style of the book is very inviting and it should be on the shelf of every serious researcher and practitioner in quantitative finance, including graduate students. Teachers could easily use the book in their applied courses. Overall, I think the book is a clear self-contained guide to implementing models in quantitative finance and as such it is going to be very popular in quant and academic circles." (Ita Cirovic Donev, MathDL, July, 2008)

"This application-oriented book presents the major numerical methods currently used and describes how these methods can be used to solve problems in quantitative finance. … Each chapter includes exercises for student practice … . The presentation is at an intermediate-advanced level and serves as an introductory tutorial to the field of quantitative finance. Quantitative analysts, researchers and graduate students in quantitative finance will find this book useful." (Stefan Henn, Mathematical Reviews, Issue 2009 g)