Risk Management & Real Options I. Introduction Stefan Scholtes Judge Institute of Management University of Cambridge MPhil Course 2004-05 Course website

Risk Management & Real Options

I. Introduction

Stefan ScholtesJudge Institute of Management

University of Cambridge

MPhil Course 2004-05

Course website with accompanying material http://www.eng.cam.ac.uk/~ss248/real_options

2 September 2004 © Scholtes 2004 Page 2

Let’s play a game…

Based on US game show “Let’s Make a Deal” You, the contestant, choose one of three closed doors to

win the prize behind the door Behind one of the doors is a sports car, behind the other

two doors are goats Before he opens your door, Monty Hall, the host, who

knows where the car is, opens one of the remaining doors that has a goat behind it

The goat jumps on the stage and Monty asks if you want to switch from the chosen door to the remaining closed door


Aims and objectives of the course

General issue:

How can we use (simple) models to help us understand uncertainty and the consequences of our decisions in an uncertain world?

General objectives:

This is a skills-based course. You will learn to use a computer to help you understand and improve system value

• Computational tools based on Excel plus a few add-ins

But it is also intellectually stretching. I hope to change the way you think about uncertainty in your everyday life


Examples of systems we have in mind

Harbour expansion in Sidney

Designing communications satellites at Motorola

Terminal 5, 3rd run-way at Heathrow

Satellite-based toll collection system in Germany

Sonic cruiser vs 7E7 at Boeing

Fleet planning at BA

Bidding for G3 telecom licenses

Production sharing contract between BP and Petronas, Malaysia

Drug co-development contract between Cambridge Antibody Technology and Astra Zeneca


Key challenges

Understanding the system value

Improving the system design

This course focuses on the valuation and design optimisation of systems that operate in an unpredictable dynamic environment

We will mainly focus on economic valuations ($$) as system values but the general framework applies to non-monetary value measures, too


What are we concerned with?Starting point: System value is more than a number

We are constantly forced to make decisions with uncertain consequences

• Decision = Allocation of resources

We are not good at understanding or communicating effects of uncertainty

• We feel uncomfortable with uncertainty and, as a consequence, tend to blend it out in our system valuations

We work with forecasts of uncertain variables (demand, prices, costs, regulation, political scenarios,…) to generate a single output – the “value”

BUT THE FORECAST IS ALWAYS WRONG

A single number as system value • Gives the wrong impression of certainty and “correctness”• Allows for easy “reverse engineering”, i.e.. begin with the value and

find uncertainties to explain the value


What are we concerned with?I. Recognising uncertainty: Values as shapes

The good, the bad and the ugly:• Uncertainty is best represented by a SHAPE (“distribution”)• 2nd best is a range of possible values• Worst is a single number

If we want to work with shapes, we need a shape calculator

SKILL: LEARN HOW TO USE A SHAPE CALCULATOR

But: Are “shape models” any more trustworthy than the “number models”?


What are we concerned with?II. Developing valuation models: No right answer

Engineering models of systems focus on “the right answers”• Precise mathematical model plus reliable data

Economic valuation of systems must acknowledge that THERE IS NO RIGHT VALUE … unless the system is traded in the market place

• If there is no right answer then there is no right model either!

Response I: “Hard” modelling is useless for managers, give up on it and base your decision on

• gut-feeling and intuition• industry comparison

Response II: “Hard” modelling is even more important to make sense of complex systems and understand consequences of decisions

• Improved understanding of the system gives competitive advantage

BUT: We have to revise our expectations on modelling


What are we concerned with?II. Developing valuation models: Less is more

Develop models that help you “ask the right questions”, not “give the right answers”

• Use models to learn about value drivers, not so much about the value itself

• Use many models - each one is part of the “valuation puzzle”• Confidence in the decision is more important than accuracy of the

“value”

A host of simple but different models is more useful than developing one complicated black-box!

• Simple models help you build intuition• Simple models help you communicate your intuition

Skill: DEVELOPING VALUATION MODELS


What are we concerned with?III. How to cope with uncertainty: The 3 weapons

Diversification: Don’t put all your eggs in one basket

Information: Gather information to narrow down the level of uncertainty

• Buy in information• Wait until uncertainty is resolved

Flexibility: Make sure you can act to avoid losses and amplify gains as uncertainties unfold

Skill: DEVELOPING SIMPLE MODELS TO ALLOW YOU TO ANALYSE THE EFFECTS OF THESE WEAPONS


What are we concerned with?IV. Whose risk is it anyway? Risk sharing in contracts

Contracts are the building blocks of business

Need to understand the effect of contract terms on risk exposure and opportunity sharing

Skill: DEVELOPING SIMPLE MODELS FOR CONTRACT VALUATION


Course content

I. IntroductionII. The forecast is always wrong

I. The industry valuation standard: Net Present Value

II. Sensitivity analysisIII. The system value is a shape

I. Value profiles and value-at-risk charts

II. SKILL: Using a shape calculatorIII. CASE: Overbooking at EasyBeds

IV. Developing valuation modelsI. Easybeds revisited

V. Designing a system means sculpting its value shapeI. CASE: Designing a Parking Garage

III. The flaw of averages: Effects of

system constraintsVI. Coping with uncertainty I:

DiversificationI. The central limit theoremII. The effect of statistical

dependenceIII. Optimising a portfolio

VII. Coping with uncertainty II: The value of information

I. SKILL: Decision Tree Analysis

II. CASE: Market Research at E-PhoneVIII. Coping with uncertainty III: The value

of flexibility

I. Investors vs. CEOs

II. CASE: Designing a Parking Garage II

III. The value of phasing

IV. SKILL: Lattice valuation

V. Example: Valuing a drug development projects

VI. The flaw of averages: The effect of flexibility

VII. Hedging: Financial options analysis and Black-Scholes

IX. Contract design in the presence of uncertainty

I. SKILL: Two-party scenario tree analysis

II. Project: Valuing a co-development contract

X. Wrap-up and conclusions

Documents

Risk Management & Real Options I. Introduction Stefan Scholtes Judge Institute of Management University of Cambridge MPhil Course 2004-05 Course website