p>Michael Rees
Business Risk and Simulation Modelling in Practice
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This edition first published 2015
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Library of Congress Cataloging-in-Publication Data
Rees, Michael, 1964–
Business risk and simulation modelling in practice: using Excel, VBA and @RISK / Michael Rees.
pages cm
Includes index.
ISBN 978-1-118-90405-3 (cloth)
1. Risk management-Computer simulation. 2. Risk management-Data processing. 3. Microsoft Excel (Computer file) I. Title.
HD61.R44 2015
658.15′50285554-dc23
A catalogue record for this book is available from the British Library.
ISBN 978-1-118-90405-3 (hbk) ISBN 978-1-118-90403-9 (ebk)
ISBN 978-1-118-90404-6 (ebk) ISBN 978-1-118-90402-2 (ebk)
Cover Design: Wiley
Cover Image: ©iStock.com/Mordolff
Preface
This book aims to be a practical guide to help business risk managers, modelling analysts and general management to understand, conduct and use quantitative risk assessment and uncertainty modelling in their own situations. It is intended to provide a solid foundation in the most relevant aspects of quantitative modelling and the associated statistical concepts in a way that is accessible, intuitive, pragmatic and applicable to general business and corporate contexts. It also discusses the interfaces between quantitative risk modelling activities and the organisational context within which such activities take place. In particular, it covers links with general risk assessment processes and issues relating to organisational cultures, incentives and change management. Some knowledge of these issues is generally important in order to ensure the success of quantitative risk assessment approaches in practical organisational contexts.
The text is structured into three parts (containing 13 chapters in total):
• Part I provides an introduction to the topic of risk assessment in general terms.
• Part II covers the design and use of quantitative risk models.
• Part III provides an introduction to key ways to implement the repeated calculation steps that are required when conducting simulation, covering the use of VBA macros and that of the @RISK add-in.
The text has been written to be software independent as far as reasonably practical. Indeed (apart from an assumption that the reader wishes to use Excel to build any models), most of the text in Parts I and II would be identical whichever platform is used to actually perform the simulation process (i.e. whether it is VBA or @RISK). Thus, although some of the example files use Excel functionality only, and others use features of @RISK, essentially all could be readily built in either platform if necessary (there are a handful of exceptions): One would have to make a few simple formula changes in each case, with the tools presented in this text showing the reader how to do so. On the other hand, in the context of presenting data arising from probabilistic processes and simulation results, @RISK's graphical capabilities are generally more flexible (and quicker to implement) than those in Excel. Thus, for purposes of quality, consistency and convenience, many of the illustrations in the book use @RISK in order to show associated graphs, even where the model itself does not require @RISK per se. Thus, a reader is not required to have a copy of @RISK at that point in the text. Indeed, apart from when working with the examples in Chapter 13, there is no fundamental requirement for a reader to own a copy (or a trial version) of @RISK in order to gain value from the text. In fact, readers who wish to use other implementation platforms for the simulation itself may find many aspects of this text of relevance.
The choice to present both Excel/VBA and @RISK approaches serves a number of purposes:
• Whichever platform is used for the simulation, the core concepts, most of the modelling techniques and issues concerning process alignment and other organisational challenges are essentially the same. An integrated approach allows a reinforcement of some of the concepts from different perspectives, and provides a comparison between the possible implementation approaches whilst ensuring minimum repetition.
• Each platform has its own merits, so that in practice, some readers may need one approach whilst other readers would need another. In particular, not only is Excel essentially ubiquitous (and hence the implementation within Excel/VBA involves no additional cost), but also the range of possibilities to use Excel/VBA for risk modelling is larger than is often realised. For example, it is fairly straightforward to create random samples from over 20 probability distributions, and to correlate them. On the other hand, the use of @RISK can facilitate many aspects of the process associated with the building and communication of risk models and their results;
