Transcript Wrap-up - University of Cambridge

Risk Management & Real Options
Wrap-up
Stefan Scholtes
Judge Institute of Management
University of Cambridge
MPhil Course 2004-05
Course website with accompanying material
http://www.eng.cam.ac.uk/~ss248/real_options
Course content
I.
II.
III.
IV.
V.
VI.
Introduction
The forecast is always wrong
I.
The industry valuation standard: Net
Present Value
II.
Sensitivity analysis
The system value is a shape
I.
Value profiles and value-at-risk charts
II.
SKILL: Using a shape calculator
III.
CASE: Overbooking at EasyBeds
Developing valuation models
I.
Easybeds revisited
Designing a system means sculpting
its value shape
I.
CASE: Designing a Parking Garage I
II.
The flaw of averages: Effects of
system constraints
Coping with uncertainty I:
Diversification
I.
The central limit theorem
II.
The effect of statistical dependence
III.
Optimising a portfolio
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VII.
Coping with uncertainty II: The value of
information
I.
SKILL: Decision Tree Analysis
CASE: Market Research at E-Phone
Coping with uncertainty III: The value of
flexibility
II.
VIII.
I.
Investors vs. CEOs
II.
CASE: Designing a Parking Garage II
III.
The value of phasing
IV.
SKILL: Lattice valuation
V.
VI.
Example: Valuing a drug development
projects
The flaw of averages: The effect of
flexibility
Hedging: Financial options analysis and
Black-Scholes
Contract design in the presence of
uncertainty
VII.
IX.
I.
SKILL: Two-party scenario tree analysis
Project: Valuing a co-development
contract
Wrap-up and conclusions
II.
X.
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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
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Examples of systems we have in mind

Harbour expansion in Sidney

Designing communications satellites at Motorola

Terminal 5, 3rd run-way at Heathrow
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Satellite-based toll collection system in Germany

Sonic cruiser vs 7E7 at Boeing

Fleet planning at BA

Bidding for G3 telecom licenses
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Production sharing contract between BP and Petronas, Malaysia
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Drug co-development contract between Cambridge Antibody Technology and Astra
Zeneca
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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
•
E.g. service level
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What were we concerned with?
Starting point: System value is more than a number
Big Points



We lack an intuitive understanding and clear communication of the
effects of uncertainty on system value
We work with forecasts of uncertain variables to generate a single
output – the “value” – to re-assure ourselves
BUT: The forecast is always wrong
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What were we concerned with?
I. Recognising uncertainty: Values as shapes
Big points

Uncertainty is best represented by a SHAPE

If we want to work with shapes, we need a shape calculator

SKILL: LEARNED HOW TO USE A SHAPE CALCULATOR
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What were we concerned with?
II. Developing valuation models: No right answer
Big points
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
Engineering models focus on “the right answers” - economic valuation of
systems must acknowledge that THERE IS NO RIGHT VALUE
Disheartened response: “Hard” modelling is useless
My (and hopefully our) response: “Hard” modelling is even more
important BUT we have to revise our expectations on modelling
Good models test and improve our intuition about the value
Good models help us communicate insight - models are vehicles for story
telling
Work with many valuation models – each of them is part of the
“Valuation puzzle”
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What were we concerned with?
The flaw of averages

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

The system value calculated on the basis of average conditions is not
the average system value
Constraints imply that the system value calculated on the basis of
average conditions OVERESTIMATES the average system value
Flexibility implies that the system value calculated on the basis of
average conditions UNDERESTIMATES the average system value
Scenario-based analyses, such as decision trees or Monte Carlo
simulation, avoid the flaw
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What were 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 uncertain


Flexibility: Make sure you can act to avoid losses and amplify gains as
uncertainties unfold
Skill: HAVE SEEN SOME SIMPLE MODELLING TEMPLATES THAT ALLOW
YOU TO ANALYSE THE EFFECTS OF THESE WEAPONS
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What were 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
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THAT’S IT!
I HOPE YOU HAVE ENJOYED THE COURSE
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