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  • © Cambridge Resources International
  • © Cambridge Resources International
  • © Cambridge Resources International
  • © Cambridge Resources International
  • © Cambridge Resources International
  • © Cambridge Resources International
  • © Cambridge Resources International
  • © Cambridge Resources International
  • © Cambridge Resources International
  • 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

  • 1.
    • Lecture Nine
    FINA 522: Project Finance and Risk Management Updated: 29 April, 2007
  • 2. RISK ANALYSIS
  • 3. What is risk?
    • Risk generally describes the possible deviation from a projected outcome.
    • To project any uncertain outcome into the future you need to have a “predictive model”.
    • A predictive model could be a simple formula or a very complex worksheet.
  • 4. Decision-Making Under Uncertainty
    • 1. Risk analysis
    • How to identify, analyze, and interpret the expected variability in project outcomes
    • 2. Risk diversification and management
    • How to diversify unsystematic risk
    • How to redesign and reorganize projects in order to reallocate risk
  • 5. Risk Analysis
    • 1. WHY?
    • Project returns are spread over time
    • 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
  • 6. A good predictive model in project appraisal depends on: Correct methodology Accurate data Cash-Flow Projections Marketing Module Technical Module Input Data Input Data
  • 7. Uncertainty and Forecasting
    • 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
  • 8. 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.
  • 9. Forecasting the outcome of a future event: Single-value estimate
  • 10. From a frequency to a probability distribution
  • 11. Multi-value probability distributions
  • 12. 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.
  • 13. 2. Alternative Methods of Dealing With Risk
    • 2.1 Sensitivity Analysis
    • 2.2 Scenario Analysis
    • 2.3 Monte Carlo Risk Analysis
    • (or Simulation Analysis)
  • 14. 2.1 Sensitivity Analysis
    • 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
  • 15. Another Important Use of Sensitivity Analysis
    • Sensitivity analysis on the PV of each row of the spreadsheet (Banker’s, Owner’s and Economy’s point of view) is the best way to de-bug a spreadsheet
    • If results do not make sense then it is likely that there is an computation or logistical error in the spreadsheet
  • 16. Sensitivity Analysis for the Mindanao Poverty Reduction Case
    • Inflation
    • Rate
    • 5%
    • 8%
    • 11%
    • 14%
    • 17%
    • 20%
    • 23%
    • 26%
    • 29%
    • 32%
    • 35%
    • Capacity
    • Utilization
    • Factor
    • 60%
    • 65%
    • 70%
    • 75%
    • 80%
    • 85%
    • 90%
    • 95%
    • 100%
    • 105%
    • 110%
    Real NPV (Million Pesos) 161 147 136 126 118 111 105 99 94 89 84 Real NPV (Million Pesos) -189 -147 -105 -63 -21 21 63 105 147 189 231 World T.P. Price (S.F. FOB) US$/Ton 587 637 687 737 787 837 887 937 987 1037 1087 Divergence from Original Cost Estimate -10% -5% 0% 5% 10% 15% 20% 25% 30% 35% 40% Real NPV (Million Pesos) -228 -103 22 147 272 397 522 647 772 897 1022 Real NPV (Million Pesos) 190 169 147 125 103 82 60 38 17 -5 -27
  • 17.
    • For Tomato Paste Plant Capacity Utilization is critical.
    • What can cause Capacity Utilization to be low?
      • Technical problems with the plant.
      • The demand for product does not exist at the price that covers the costs
      • The plant can not get adequate supplies of raw materials.
    • Factsheet:
      • this plant eventually run into financial troubles
      • could not attain adequate supplies of raw materials
  • 18. Cautionary Notes for Sensitivity Analysis
    • 1. Range and probability distribution of variables
    • Sensitivity analysis doesn't represent the possible range of values
    • Sensitivity analysis doesn't represent the probabilities for each range. Generally there is a small probability of being at the extremes.
    • 2. Direction of effects
    • For most variables, the direction is obvious
    • A) Revenue increases NPV increases
    • B) Cost increases NPV decreases
    • C) Inflation Not so obvious
  • 19. Cautionary Notes for Sensitivity Analysis
    • 3. One-at-a-Time Testing Is Not Realistic
    • One-at-a-time testing is not realistic because of correlation among variables
      • A) If Q sold increases, costs will increase
    • Profits = Q (P - UC)
      • B) If inflation rate changes, all prices change
      • C) If exchange rate changes, all tradable goods' prices and foreign liabilities change
    • One method of dealing with these combined or correlated effects is scenario analysis
  • 20. 2.2 Scenario Analysis
    • 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.
  • 21. 2.3 Monte Carlo Method of Risk Analysis
    • A natural extension of sensitivity and scenario analysis
    • Simultaneously takes into account different probability distributions and different ranges of possible values for key project variables
    • Allows for correlation (covariation) between variables
    • Generates a probability distribution of project outcomes (NPV) instead of just a single value estimate
    • The probability distribution of project outcomes may assist decision-makers in making choices, but there can be problems of interpretation and use.
  • 22. Monte-Carlo Simulation
    • 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.
  • 23. The Risk Analysis Process
  • 24. Simple Model B-C Relationships Result Variables B=3 C=2 R=1
  • 25. The Financial Model Cash Flow Owner’s View Cash Flow Project View Projected Profit & Loss Projected Sources & Applications Projected Balance Sheets Loans Depreciation Taxation Project Cost & Financing Plan Assumptions
  • 26. Taking uncertainty into consideration Model Inputs Output
  • 27. The Monte-Carlo Simulation process
    • 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.
  • 28. Forecasting Model
  • 29. Set Probability Distributions
  • 30. The Monte-Carlo Simulation process
    • Set correlation conditions to limit the possibility of generating internally inconsistent scenarios during a simulation.
    • Identify the critical calculated results you wish to apply the analysis on – model results .
  • 31. Set correlation conditions
  • 32. Correlated variables – Generating Relationship Data Correlated Variables (r = 0.8), 200 runs 8 9 10 11 12 13 14 15 16 Sales price (independent variable) 70 80 90 100 110 120 130 Volume of sales (dependent variable)
  • 33.
    • 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.
    The Monte-Carlo Simulation process
  • 34. Simulation Runs
  • 35. Distribution of results (net cash flow) where: p = probability weight for a single run n = sample size
  • 36. Net present value distribution (different project perspectives)
  • 37. Time Mastering Cash Flow - What lies beneath the projections? Cash Flow Base-Case Cash flow
  • 38. Time The impact of uncertainty on the projected cash flow Debt Service Cash Flow NET CASH FLOW Base-Case Cash flow
    • Key Benefits
    • Risk Measurement
    • Risk Mitigation
    • Risk Management
    Downside Cash flow Up side Cash flow
  • 39. Interpretation of Risk Analysis Results
  • 40.  
  • 41. Case 1: Probability of negative NPV=0
  • 42. Case 2: Probability of positive NPV=0
  • 43. Case 3: Probability of zero NPV greater than 0 and less than 1
  • 44. 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
  • 45. Case 5: Mutually exclusive projects (high return vs. low loss) Case 5: Intersecting cumulative probability distributions of project return for mutually exclusive projects
  • 46. Expected Loss Ratios: Example of project outcomes expected value of project Expected value of losses Expected value of gains
  • 47. Expected Loss Ratios
  • 48. Risk under conditions of limited liability
  • 49. Advantages of risk analysis
    • It enhances decision making on marginal projects.
    • It screens new project ideas and aids the identification of investment opportunities.
    • It highlights project areas that need further investigation and guides the collection of information.
    • It aids the reformulation of projects to suit the attitudes and requirements of the investor.
    • It induces the careful re‑examination of the single‑value estimates in the deterministic appraisal.
    • It helps reduce project evaluation bias through eliminating the need to resort to conservative estimates.
  • 50.
    • It facilitates the thorough use of experts.
    • 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.
    Advantages of risk analysis (cont.)
  • 51. Finally two words of caution:
    • Overlooking significant inter-relationships among the projected variables can distort the results of risk analysis and lead to misleading conclusions.
    • The accuracy of the results of risk analysis can only be as good as the predictive capacity of the model employed.
  • 52.
    • Lecture on Crystal Ball
    FINA 522: Project Finance and Risk Management
  • 53. INTRODUCTION TO RISK ANALYSIS PROGRAM MICROSOFT EXCEL & CRYSTAL BALL
  • 54. INTRODUCTION TO RISK ANALYSIS PROGRAM MICROSOFT EXCEL & CRYSTAL BALL June 2006
  • 55. WHY do we need Risk Analysis ?
    • Project returns are spread over time, therefore are subject to risk as they are the result of many uncertain events.
    • Each variable affecting NPV is subject to high level of uncertainty
    • Need to reduce the likelihood to undertake a &quot;bad&quot; project while not failing to accept a &quot;good&quot; project
    • Crystal ball risk software will help us
    • identify, analyze, and interpret the expected variability in project outcomes.
  • 56.
    • WHAT CRYSTAL BALL SOFTWARE DOES?
    • 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.
  • 57. Methods
    • Sensitivity Analysis
    • Monte Carlo Risk Analysis (or Simulation Analysis) using Crystal Ball Software
  • 58. Sensitivity Analysis
    • Test the sensitivity of a project's outcome (NPV or IRR) to changes in value of one or two parameter at a time
    • &quot;What if&quot; analysis
    • Allows you to test which variables are important as a source of risk
    • Sensitivity analysis allows you to determine the direction of change of the NPV
  • 59. Monte Carlo Method of Risk Analysis
    • A natural extension of sensitivity analysis
    • Simultaneously takes into account different probability distributions and different ranges of possible values for key project variables.
    • Allows for correlation between variables.
    • Generates a probability distribution of project outcomes (NPV) instead of just a single value estimate
    • The probability distribution of project outcomes may assist decision-makers in making choices, but there can be problems of interpretation and use.
  • 60. Steps in Building a Monte Carlo Simulation
    • 1. Mathematical model: project evaluation spreadsheet
    • 2. Identify variables which are sensitive and uncertain
    • 3. Define uncertainty
    • Establish a range of options (minimum and maximum)
    • Allocate probability distribution
      • Normal distribution
      • Triangular distribution
      • Uniform distribution
      • Step distribution
    • 4. Identify and define correlated variables
    • Positive or negative correlation
    • Strength of correlation
    • 5. Simulate model
    • 6. Analysis of results
    • Statistics
    • Distributions
  • 61. ORGANIZATION CHART FOR CASH-FLOW MODEL TABLE OF PARAMETERS CASH FLOWS SENSITIVITY ANALYSIS LINK LINK LINK RISK ANALYSIS
  • 62. DETERMINISTIC ANALYSIS (Unit Price of Goods) $250 $350 $150
  • 63. SENSITIVITY ANALYSIS (Unit Price of Goods) $250 $350 $150
  • 64. MONTE CARLO RISK ANALYSIS (Unit Price of Goods) $250 $350 $150
  • 65. Upgrading a Gravel Road to Tar Risk Analysis Guidelines for Crystal Ball ©
  • 66. Steps to Follow:
    • Step 1: Complete Financial Analysis
    • Step 2: Identify “Risk Assumptions” and “Risk Forecasts”
    • Step 3: Choose a Probability Distribution and Correlations for Risk Assumptions
    • Step 4: Define Risk Assumptions and Correlations
    • Step 5: Define Risk Forecasts
    • Step 6: Configure Risk Simulation
    • Step 7: Running a Risk Simulation
    • Step 8: Prepare a Risk Report
    • Step 9: Interpretation of Results
  • 67. Step 1: Complete Financial Analysis (Deterministic Case)
    • 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
  • 68. 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
  • 69. 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
  • 70. Step 4: Define Risk Assumptions and Correlations
    • Click on the CELL in Table of Parameters, which will be defined as a risk assumption
    • For example: Traffic Growth Rate Cell: E8
    • In CELL menu choose: Define Assumption…
  • 71.
    • Choose from available types of distributions
    • Press “More” for other types
    • Press “Fit…” to estimate probability distribution from actual data (if you have any)
    • Once chosen, press “OK”, this assumption has triangular distribution.
  • 72.
    • Insert the distribution as given.
    • For example: Traffic Growth Rate Cell E8 (Triangular)
    Assumption Name Mean (Likeliest) Minimum Value Maximum Value
    • Insert the distribution as given 
  • 73.
    • For triangular distribution, fill-in:
      • Assumption Name
      • Minimum and Maximum Values
      • Press “Enter” to update display
    • Press “OK” (risk assumption is defined)
  • 74.
    • Investment Cost-over run has step distribution
    • Fill-in the following fields in the box:
    Minimum and Maximum Values for Each Step Assumption Name Probability of Occurrence for Each Step Custom Distributions
  • 75.
    • For custom distribution, fill-in:
      • Assumption Name
      • Minimum and Maximum Values for a Step
      • Probability of Occurrence for that Step
      • Press “Enter” to update display
      • Continue with other steps
    • Finally, press “OK” (risk assumption is defined)
    • Note: if mistakenly entered, steps can be edited later by clicking on them, changing to new values and pressing “Enter” and then “OK”.
  • 76.
    • Maintenance Costs Savings Factor has triangular
    • Continue with ALL other assumptions
    • For example: Maintenance Costs Savings Factor (Triangular)
    Assumption Name Mean Minimum Value Maximum Value Triangular Distribution
  • 77.
    • This example shows, how to define the correlation between two variables.
    • In this assignment, we assume that traffic growth rate and the maintenance costs saving factor have correlation coefficient of -0.6
    • Click on the value which have correlation then go to the define assumption
    • Press “Correlate…” in the assumption of maintenance costs saving factor
  • 78.
    • After click on the correlation, the following screen appears.
    Correlation Coefficient Assumption Name
    • Press “Select Assumption” (in this case Traffic Growth Rate)
    • Fill-in the correlation coefficient (in this case -0.6)
    • Press “Enter” to update display
    • Repeat procedure for all assumptions being correlated
    • Finally, press “OK” (correlations are defined)
    • See the picture on the right.
  • 79.
    • VOC Savings Factor has normal distribution
    • Continue with ALL other assumptions
    • For example: VOC Savings Factor Cell: F22 (Normal)
    Assumption Name Standard Deviation Deterministic Value Normal Distribution
  • 80.
    • Time Saving Factor has uniform distribution
    • Continue with ALL other assumptions
    • Some assumptions will have different probability distributions
    • For example: Time Saving Factor Cell: F23 (Uniform)
    Assumption Name Minimum Value Maximum Value Uniform Distribution
  • 81. Step 5: Define Risk Forecasts
    • Click on the CELL in spreadsheet, which will be defined as a risk forecast
    • For example: NPV (Economic) H149
    • In CELL menu choose: Define Forecast…
  • 82.
    • In the dialog box for risk forecast, fill-in:
      • Forecast Name
      • Units
    • Press “OK” (forecast is defined)
    • Repeat procedure for ALL, like PV of Road Agency, PV of Light Vehicle Users and PV of Heavy Vehicle Users; other risk forecasts to be defined
  • 83. NOTES TO ADVANCED USER
    • Parameters of risk assumptions and forecasts can be copied by special Crystal Ball copy-paste commands
    • This saves time and effort in repeated tasks (e.g. defining yearly inflation rate)
    • Select the cell from which you want to copy risk parameters --> in CELL menu choose COPY DATA
    • Select the cell to which the risk parameters are applied --> in CELL menu choose PASTE DATA
    • ALL risk parameters can be removed in a cell by choosing CELL menu – CLEAR DATA
  • 84. Step 6: Configure Risk Simulation
    • Any risk simulation must be properly configured BEFORE running it
    • In RUN menu choose: Run Preferences…
  • 85.
    • Set the necessary Number of Trials (5,000 runs is usually considered to be sufficient)
    • Switch OFF the following:
      • Stop if specified precision is reached ; and
      • Stop if calculation error occurs
    • Press “ >> ” to go to next stage …
    Set Number of Trials Switch OFF Got to NEXT stage
  • 86.
    • Choose Sampling Method :
      • Monte Carlo (most often used)
      • Latin Hypercube (computer memory intensive)
    • Do NOT change any other parameters here
    • Press “ >> ” to go to next stage …
    Sampling Method
  • 87.
    • Do NOT change Use Burst Mode When Idle
    • Select one of the options in Minimize While Running:
      • All Spreadsheets (recommended)
    • Switch ON the option of Suppress Forecast Windows
    • Press “ OK ” (configuration is complete)
    Speed Options Switch ON
  • 88. Step 7: Running a Risk Simulation
    • To start running a simulation, In RUN menu choose: Run
    • Wait until it tells you that Maximum Number of trials is Reached (sometimes takes a while…)
  • 89. Step 8: Prepare a Risk Report
    • Risk report is a sheet containing the summary of the risk assumptions and forecasts parameters as well as the final results of the simulation
    • Forecasts should be formatted for easier visual representation
    • In RUN menu choose: Forecast Windows
    Select Open ALL Forecasts
  • 90.
    • In the forecast window, choose PREFERENCES menu and select CHART…
  • 91.
    • Make sure you select the following options:
    • When done, press “ APPLY TO ALL ”
  • 92.
    • Each forecast window should be adjusted for range:
      • NPV range: from –Infinity to Zero
      • IRR range: from –Infinity to Discount Rate used
      • Debt Service Ratios: from –Infinity to 1.50
    Fill-in either 0 for NPV, or Discount Rate used for IRR, or 1.50 for Debt Service Ratio and press ENTER on keyboard
  • 93.
    • Create an Overlay Chart , in RUN menu choose: Open Overlay Chart
    Press Choose Forecasts… Choose NPV Forecasts Press OK
  • 94.
    • Overlay chart should be also formatted for better visual presentation, as shown below:
    Press Chart Prefs… Press OK
  • 95.
    • After completion of simulation, and format save its results to a file
    • You can later access the results of your risk simulation WITHOUT running it again
  • 96.
    • To generate a Report , in RUN menu choose: Create Report…
    Choose Assumptions NO Percentiles
  • 97. Step 9: Interpretation of Results
    • Analyze the results, which will be presented by:
      • Overlay Chart (comparison of several NPVs)
      • Forecast Charts for each risk forecast (NPVs, IRR, Debt Service Ratios)
    • Summary Statistics for each risk forecast
    • Risk results must be compared with the results of deterministic analysis
  • 98.
    • Summary Statistics for a risk forecast:
    Commonly used and widely understood descriptors
  • 99.
    • What do we need to do to manage risks?
    • Is risk management proposal effective? – Need to test contracts.