style="font-size:15px;"> Readers who wish to review specific models that use @RISK may install a trial version (if they do not have, or do not wish to buy, a full version). Trial versions are fully functional but time limited, so that readers should ensure that the installation of any trial is appropriately timed. In particular, readers may choose to read (or skim) all of the text before installing the trial, and revisit relevant parts of the text afterwards. At the time of writing, trial versions are valid for 10 days and are available at www.palisade.com. Readers may contact Palisade Corporation directly who may – entirely at its discretion – be able to extend the duration of a trial. For the purposes of the models used in this text, it is sufficient to download @RISK Industrial; however, some features within this – whilst briefly mentioned in this text – are not required for the example files provided, so that the additional software associated with these features does not need to be acquired (in particular the SQL-related content used for the Library functionality, and Microsoft Project, are not required). Technical aspects of installation and licensing options for @RISK are not covered in this text. Please note that the author is totally independent of Palisade Corporation, and has no control over the availability (or not) of trial versions, so the above is (in theory) subject to change, although trial versions have been available for many years without issue.
As far as possible, we have aimed to present concepts in a logical and linear order, but also to remain practical and to introduce technical aspects only where they are genuinely needed, and not simply for their own sake. Due to the richness of the subject, this has not been possible to do perfectly. In particular, whereas the detailed discussion of simulation concepts and definitions of statistical terms is covered in Part II (Chapter 8), on occasion in Part I we make reference to some basic statistical concepts (such as averages or percentiles, or to probability in general), and also show some simulation results. It is hoped that readers will nevertheless be able to follow this earlier discussion; many will no doubt have some (at least limited) experience of such concepts that is sufficient to be able to follow it; if not, of course the option to read first (or selectively refer to) this later chapter is open to them.
About the Author
Michael Rees has a Doctorate in Mathematical Modelling and Numerical Algorithms, and a BA with First Class Honours in Mathematics, both from Oxford University. He has an MBA with Distinction from INSEAD in France. In addition, he studied for the Wilmott Certificate of Quantitative Finance, where he graduated in first place for course work and also received the Wilmott Award for the highest final exam mark.
Since 2002, he has worked as an independent expert in financial modelling, risk modelling and quantitative decision support, providing training, model building and advisory services to a wide range of corporations, consulting firms, private equity businesses and training companies. As part of his activities as an independent consultant, Michael worked closely with Palisade Corporation, the developers of the @RISK software, for which he is one of the world's most experienced instructors, having taught several thousand people in this area.
Prior to becoming independent, Michael was employed at J.P. Morgan, where he conducted valuation and research work, and prior to that he was a partner with strategy consultants Mercer Management Consulting (now Oliver Wyman), both in London, UK, and Munich, Germany. His earlier career was spent at Braxton Associates (a strategy consulting firm that became part of Deloitte and Touche), where he worked in London and as a founding member of the start-up team in Munich.
Michael is a dual UK/Canadian citizen. He is fluent in French and German, and has wide experience of working internationally and with clients with diverse cultural backgrounds. In addition to this text, he is the author of Financial Modelling in Practice: A Concise Guide for Intermediate and Advanced Level (John Wiley & Sons, 2008). He can be contacted at [email protected] or through the website www.michaelrees.co.uk.
About the Website
Please visit this book's companion website at www.wiley.com/go/reesbrsm for more information on the models discussed in this book.
To find the password, you'll need to answer the following question: what is the first word of the caption for Figure 8.8 in the book?
Part I
An Introduction to Risk Assessment – Its Uses, Processes, Approaches, Benefits and Challenges
CHAPTER 1
The Context and Uses of Risk Assessment
This chapter provides a general discussion about the uses of risk assessment. We start by describing some simple examples; these demonstrate that risk assessment is a natural process that is conducted by most people in day-to-day situations, albeit informally and often implicitly. We also present some prominent examples of risk management failures in business-related contexts. We then describe some contextual challenges in decision-making processes, including that of achieving an appropriate balance between rational considerations and intuition, as well as the presence of biases. In the latter part of the chapter, we present key drivers of the need for structured, explicit and formal approaches to risk assessment in some contexts, and present the main uses and objectives of such activities.
1.1 Risk Assessment Examples
This section presents some simple examples of the use of risk assessment in everyday situations. From these, we aim to draw some general conclusions, including that the conducting of risk assessment is quite natural to most of us (and not something unusual, in principle). Indeed, in situations that are fairly simple or that are encountered frequently, the process is usually implicit; our plans automatically incorporate some element of risk mitigation and contingency planning based on experience, without us being particularly aware of it. For situations faced less frequently (or where the situation does not closely fit a recognised pattern), the process is generally slightly more explicit.
We also aim to show that a risk assessment process – whether explicit or implicit – may result in modifications to original (base case) plans in several possible ways:
• It may result in no change to the underlying plan or project, but simply to the adaptation of targets or of objectives to make them more realistic or achievable, such as the addition of contingency, whether it be extra time, resources or budget.
• It may lead to moderate changes to the initial plan or project, by leading one to look for measures to respond to risks, such as mitigation or exploitation measures.
• It could result in more fundamental changes to the project, such as the requirement for it to be re-scoped or changed in a major way, or for completely new structural or contextual possibilities to be developed.
We also show that the results of the process often depend on personal judgement, rather than robust analysis and criteria. In particular, we typically make a number of judgements in ways that are neither explicit nor formalised, and these depend on our experiences, personal situations, preferences and biases. Although, in personal situations, we often have discretion as to which decision option or mitigation measure to implement, and the consequences are borne directly by us, in some cases, consultation and agreement with others may nevertheless be required.
The following describes some simple examples, each of which aims to demonstrate some of the above points.
When planning to cross a road, in normal circumstances, one first looks each way. This can be considered as risk mitigation behaviour that has been instilled in us since a young age, and has become a natural reflex: it is clear that
