1. The Project risk/return profile in conjunction with the conditions of the financial markets at the time will determine the final outcome of the financing structure. 2. Generally, it is expected that a 50%-50% Equity to Loans structure will be adopted. This may change slightly + or - 10%. 3. Equity goes in first, followed by Guaranteed debt/Vendor Finance or Suppliers' Credit and Limited recourse finance. 4. Limited recourse debt will depend on: - AdequatevPerformance guarantees - Capacity Commitments 5. The Risk/return profile of the project will determine the type and level of commitment required which is expected to vary within the indicative limits presented.
1. The Project risk/return profile in conjunction with the conditions of the financial markets at the time will determine the final outcome of the financing structure. 2. Generally, it is expected that a 50%-50% Equity to Loans structure will be adopted. This may change slightly + or - 10%. 3. Equity goes in first, followed by Guaranteed debt/Vendor Finance or Suppliers' Credit and Limited recourse finance. 4. Limited recourse debt will depend on: - AdequatevPerformance guarantees - Capacity Commitments 5. The Risk/return profile of the project will determine the type and level of commitment required which is expected to vary within the indicative limits presented.
Transcript
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Lecture Nine
FINA 522: Project Finance and Risk Management Updated: 29 April, 2007
Each variable affecting NPV is subject to a high level of uncertainty
Information and data needed for more accurate forecasts are costly to acquire
Need to reduce the likelihood of undertaking a "bad" project while not failing to accept a "good" project
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A good predictive model in project appraisal depends on: Correct methodology Accurate data Cash-Flow Projections Marketing Module Technical Module Input Data Input Data
How similar past events are to the object of forecast
How big is the sample of past events
How recent are past events
How consistent the outcome historically
How far into the future is the forecast
How dependent the outcome is on previous years (trend) and on other projected variables (correlations)
We use the past to forecast the future Ability to forecast accurately depends on: x x x x x x x x x x x o o o o o Time Present Past Future Variable Value Past Events Forecasts
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Inputs are projected as certainties (Base Case Scenario)
When we provide inputs to a predictive model we use one particular probability distribution – the Deterministic Probability Distribution.
By that we assign 100% probability that the single value of the input we use in the projection will actually arise.
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Forecasting the outcome of a future event: Single-value estimate
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From a frequency to a probability distribution
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Multi-value probability distributions as their inputs to a predictive model.
Any possible deviation in any of the critical input variables of a predictive model from their base case values will generate a new scenario with a different outcome (or outcomes).
There are potentially an infinite number of combinations of input values possible, each causing a different set of results.
Test the sensitivity of a project's outcome (NPV or the key variable) to changes in value of one parameter at a time
"What if" analysis
Allows you to test which variables are important as a source of risk
A variable is important depending on:
A) Its share of total benefits or costs
B) Likely range of values
Sensitivity analysis allows you to determine the direction of change in the NPV
Break-even analysis allows you to determine how much a variable must change before the NPV or these key variable moves into its critical range turns negative
Scenario analysis recognizes that certain variables are interrelated. Thus a small number of variables can be altered in a consistent manner at the same time.
What is the set of circumstances that are likely to combine to produce different "cases" or "scenarios"?
A. Worst case / Pessimistic case
B. Expected case / Best estimate case
C. Best case / Optimistic case
Note: Scenario analysis does not take into account the Probability of cases arising
Interpretation is easy when results are robust:
A. Accept project if NPV > 0 even in the worst case
B. Reject project if NPV < 0 even in the best case
C. If NPV is positive in some cases and negative in other cases, then results are not conclusive
Difficult to define what scenario’s to specify without first examining the range of possible outcomes by a Monte Carlo Analysis.
Scenario analysis is a good way to communicate the results of a Monte Carlo analysis.
Monte Carlo simulation is a methodology that handles the complexity arising from projecting multi-value probability distributions as inputs to a model.
Practically this is only possible to be applied with the use of a computer and specialised software.
Identify the critical/most uncertain input variables in a projected model – risk variables .
Substitute single-value assumptions with probability distributions which tend to express the possible variability for each of the identified risk variables.
Run simulation creating a sample of computer scenarios based on inputs from the probability distributions and with respect to any correlation conditions set.
Analyse results generated in the simulation run, calculating statistical measures and plotting probability distribution graphs of the results, which indicate all the potential outcomes and their likelihood of occurrence.
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Case 3: Probability of zero NPV greater than 0 and less than 1
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Case 4: Mutually exclusive projects (given the same probability, one project always shows a higher return) Case 4: Non‑intersecting cumulative probability distributions of project return for mutually exclusive projects
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Case 5: Mutually exclusive projects (high return vs. low loss) Case 5: Intersecting cumulative probability distributions of project return for mutually exclusive projects
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Expected Loss Ratios: Example of project outcomes expected value of project Expected value of losses Expected value of gains
It bridges the communication gap between the analyst and the decision maker.
It supplies a framework for evaluating project result estimates.
It provides the necessary information base to facilitate a more efficient allocation and management of risk among various parties involved in a project.
It makes possible the identification and measurement of explicit liquidity and repayment problems in terms of time and probability that these may occur during the life of the project.
Traditionally it is the most likely outcome (mode) that has been presented for decision making.
Monte Carlo analysis enables one to estimate the expected values of the outcome of our project.
It also allows us to estimate the impact on the expected value and standard deviation of the outcomes when contracts and other risk management techniques are applied to the project.
Finalize the financial/economic analysis of project
Calculate NPV, IRR, Debt Service Ratios
All these will be “deterministic case” under the base assumptions in Table of Parameters
Risk analysis will model changes in the base assumptions
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Step 2: Identify “Risk Assumptions” and “Risk Forecasts”
Risk assumptions – parameters that will be changed (prices of inputs and outputs, growth rates, any other risky and uncertain variables)
Risk forecasts – results, at which we look during the risk analysis (NPVs, IRRs, Debt Service Ratios, Distributions, etc.)
In Road case, all risk assumptions and forecasts are already given
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Step 3: Choose a Probability Distribution and Correlations for Risk Assumptions
Each risk assumption must be assigned a probability distribution
If you don’t know the appropriate probability distribution – find it either from past data, or use whatever information available to develop subjective probability distribution.
There are many types of probability distributions available
Some variables may be correlated with each other – their exact relationship must be identified
In Road case, probability distributions for risk assumptions are already given
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Step 4: Define Risk Assumptions and Correlations
Click on the CELL in Table of Parameters, which will be defined as a risk assumption
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