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

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Presentation on theme: "Risk Management & Real Options I. Introduction Stefan Scholtes Judge Institute of Management University of Cambridge MPhil Course 2004-05 Course website."— Presentation transcript:

1 Risk Management & Real Options I. Introduction Stefan Scholtes Judge Institute of Management University of Cambridge MPhil Course Course website with accompanying material

2 2 September 2004 © Scholtes 2004Page 2 Lets play a game… Based on US game show Lets 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

3 2 September 2004 © Scholtes 2004Page 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

4 2 September 2004 © Scholtes 2004Page 4 Examples of systems we have in mind Harbour expansion in Sidney Designing communications satellites at Motorola Terminal 5, 3 rd 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

5 2 September 2004 © Scholtes 2004Page 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

6 2 September 2004 © Scholtes 2004Page 6 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

7 2 September 2004 © Scholtes 2004Page 7 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) 2 nd 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?

8 2 September 2004 © Scholtes 2004Page 8 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

9 2 September 2004 © Scholtes 2004Page 9 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

10 2 September 2004 © Scholtes 2004Page 10 What are we concerned with? III. How to cope with uncertainty: The 3 weapons Diversification: Dont 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

11 2 September 2004 © Scholtes 2004Page 11 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

12 2 September 2004 © Scholtes 2004Page 12 Course content I. Introduction II. The forecast is always wrong I. The industry valuation standard: Net Present Value II. Sensitivity analysis III. 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 IV. Developing valuation models I. Easybeds revisited V. Designing a system means sculpting its value shape I. CASE: Designing a Parking Garage I II. The flaw of averages: Effects of system constraints VI. Coping with uncertainty I: Diversification I. The central limit theorem II. The effect of statistical dependence III. Optimising a portfolio VII. Coping with uncertainty II: The value of information I. SKILL: Decision Tree Analysis II. CASE: Market Research at E-Phone VIII. 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


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