Monte Carlo Methods in Finance / Edition 1

Monte Carlo Methods in Finance / Edition 1

by Peter Jackel, Peter Jä Ckel, Peter Jaeckel
     
 

Monte Carlo Methods in Finance is an important reference for those working in investment banks, insurance and strategic management consultancy. Of particular importance are the many known variance reduction methods, and they are duly covered, not only in their own right, but also with respect to their potential combinations, and in the direct context of realistic

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Overview

Monte Carlo Methods in Finance is an important reference for those working in investment banks, insurance and strategic management consultancy. Of particular importance are the many known variance reduction methods, and they are duly covered, not only in their own right, but also with respect to their potential combinations, and in the direct context of realistic applications. Most notably, the issue of the reliability of low-discrepancy numbers in high dimensions is discussed in detail. The book also contains an introduction to the theory of copule as an extension to the modelling of correlation of financial securities. An entire chapter is dedicated to the evaluation of interest rate derivatives in the Brace-Gatarek-Musiela/Jamshidian framework by the aid of fast-convergence Monte Carlo simulations. What's more, for the first time, this book also gives a description of the construction of non-recombining trees. Whilst non-recombining trees are usually not viable in a production environment, they often are the very tool of last resort when Monte Carlo approximations to problems such as Bermudan swaptions are to be tested, and the tricks for the construction of non-recombining trees presented in this book are invaluable for that purpose.

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Product Details

ISBN-13:
9780471497417
Publisher:
Wiley
Publication date:
03/26/2002
Series:
Wiley Finance Series, #5
Edition description:
BOOK & CD
Pages:
238
Product dimensions:
6.99(w) x 9.92(h) x 0.82(d)

Table of Contents

Preface

Acknowledgements

Mathematical Notation

Introduction

The Mathematics Behind Monte Carlo Methods

Stochastic Dynamics

Process-driven Sampling

Correlation and Co-movement

Salvaging a Linear Correlation Matrix

Pseudo-random Numbers

Low-discrepancy Numbers

Non-uniform Variates

Variance Reduction Techniques

Greeks

Monte Carlo in the BGM/J Framework

Non-recombining Trees

Miscellanea

Bibliography

Index

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