Risk Management & Real Options Wrap-up

1. Risk Management & Real OptionsWrap-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

2. Introduction The forecast is always wrong The industry valuation standard: Net Present Value Sensitivity analysis The system value is a shape Value profiles and value-at-risk charts SKILL: Using a shape calculator CASE:Overbooking at EasyBeds Developing valuation models Easybeds revisited Designing a system means sculpting its value shape CASE: Designing a Parking Garage I The flaw of averages: Effects of system constraints Coping with uncertainty I: Diversification The central limit theorem The effect of statistical dependence Optimising a portfolio Coping with uncertainty II: The value of information SKILL:Decision Tree Analysis CASE: Market Research at E-Phone Coping with uncertainty III: The value of flexibility Investors vs. CEOs CASE: Designing a Parking Garage II The value of phasing SKILL: Lattice valuation 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 SKILL:Two-party scenario tree analysis Project: Valuing a co-development contract Wrap-up and conclusions Course content © Scholtes 2004

3. 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 © Scholtes 2004

4. 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 © Scholtes 2004

5. 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 © Scholtes 2004

6. 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 © Scholtes 2004

7. 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 © Scholtes 2004

8. What were we concerned with?II. Developing valuation models: No right answer Big points • 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” © Scholtes 2004

9. What were we concerned with?The flaw of averages • 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 © Scholtes 2004

10. 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 © Scholtes 2004

11. 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 © Scholtes 2004

12. THAT’S IT! I HOPE YOU HAVE ENJOYED THE COURSE © Scholtes 2004