Cheat Sheet MDM Risk analysis

Topics: Normal distribution, Risk, Standard deviation Pages: 7 (1889 words) Published: January 29, 2015
RISK ANALYSIS

Why take risk into account?
Every business decision entails risk
Incomplete knowledge of future
Incorrect information
Uncertain outcome of decision
Risk Analysis
Identifying risk
Analysing impact of risk
Analysing likelihood of risk
Understanding risk
Risk Management
Anticipate and cope with risk (contingency planning)
Risk reduction (hedging)
Determine the best business decision under risk
 make decisions in the face of uncertainty

Risk identification:

Key steps
1) NEVER EVER use an average!
Planning for the average scenario is dangerous
Flaw of Averages
A single point forecast is always wrong! Decisions based on them are dangerous! A single point only ever tells us what the average of two cases is, never what happens between the two cases! Poor understanding of downside risk

Poor understanding of upside opportunity
2) Scenario analysis: Define your scenarios; best-worst-base There are a range of results!
 Check if risk makes a difference
3) Use distributions for the uncertainties to describe key risk drivers Choose distribution based on historical data or expert opinion Distribution is important for the simulation; based on the given distribution, the simulator will be more/less likely to pick numbers in specific ranges Uniform: same probability of all numbers in a given range

Triangle: point within the range is much more likely than the other points Normal: you know the middle point but it could be off by X in either direction 4) Run (at)Risk (Monte-Carlo simulation)
Define distributions (step 3)
Define output cell fir which to simulate results
Things to look out for
Mean of objective variable (usually NPV)
Compare results with scenario results (atRisk will give better indication of the range than the scenarios!) Look at full range of outcomes
Look at standard deviation and at confidence range
Look at downside risk and upside potential. What is % of being above/below specific number? What is breakeven probability?
What is the distribution like?
Perform Monte-Carlo simulation to
Evaluate different possible outcomes
Determine expected result, range of results, probability of results (e.g. probability of break-even), downside risk, etc.. Advantages: avoid the Flaw of Averages, understand the risk, test your intuition 5) Sensitivity analysis

Purpose
Examine sensitivity of results when model parameters are varied Observe change in results due to change in assumptions
Identify main uncertainty drivers / key risk drivers
Methodology
What-if analysis (simple changing of numbers to see what happens) One-way & two-way sensitivity analysis
Tornado diagrams
One-way & two-way sensitivity analysis
Use one-way sensitivity analysis (data table) to check how changes to a variable effect the output variable. Use Goal Seek to find breakeven point of that variable. Use two-way sensitivity analysis (data table) to check for changes in two different variables at the same time Tornado diagram

Check for impact of each variable / parameter, sorted in order of magnitude Shows you on which variables you should focus most, where the most important risks lie! Some Excel info points:
Simulation settings:

EXAMPLE QUESTIONS ON RISK ANALYSIS
1. In what type of decision context could risk analysis be useful and why may it be dangerous to rely on single point forecasts? What techniques can you use to overcome the problems of such forecasts? How do you decide what technique is most appropriate to use? Every business decision entails risk

Incomplete knowledge of future
Incorrect information
Uncertain outcome of decision
Flaw of Averages
A single point forecast is always wrong! Decisions based on them are dangerous! A single point only ever tells us what the average of two cases is, never what happens between the two cases! Example answer for this part: These numbers are based on the average scenario which is not necessarily representative of the true value (argue why could...
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