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Because options to abandon, contract, or switch current operations all involve selling a current investment, they are called put options. They become “in the money” when the value that could realize by selling the real underlying asset is greater than the value of the asset in the way it is currently deployed. Decision to defer an investment, expand a current investment, or make a new investment are called call options. They go “in the money” whenever the present value of the new investment exceeds the present value of its costs.

Figure 2 Because options to abandon, contract, or switch current operations all involve selling a current investment, they are called put options. They become “in the money” when the value that could realize by selling the real underlying asset is greater than the value of the asset in the way it is currently deployed. Decision to defer an investment, expand a current investment, or make a new investment are called call options. They go “in the money” whenever the present value of the new investment exceeds the present value of its costs.

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The first step is to consider the market uncertainty, and therefore to take it in account whether to decide to go on with the investment:
The first step is to consider the market uncertainty, and therefore to take it in account whether to decide to go on with the investment:
Information Technology: Yankee24 Case Study
Information Technology: Yankee24 Case Study
We have added expenditures below the lines and revenues above the lines (see figure below). At year zero an expenditure of $100 million is incurred to develop the new phones. At the beginning of the second phase, a revenue of $50 million i: generated, resulting in a net value of $750 million, discounted by 5%, to return $714 million. We now get to a circle where an uncertainty arises (market reaction). Wé then compute the probabilities related to taking one of three possible outcomes Favorable acceptance, Moderate Acceptance, and Poor Acceptance of the new phones each with probabilities 0.4, 0.4, and 0.2 respectively. At the end of this phase there are expected revenues of $750 million, $500 million, and $300 million, all o: which are discounted by (5%) 2. These numbers are added to the tree. In the las phase, the final expected revenues are computed and added along the lines. These are $750 million, $500 million, and $300 million, respectively. Again, these numbers are discounted by (5%) 3. The final tree and its corresponding table are given below:
We have added expenditures below the lines and revenues above the lines (see figure below). At year zero an expenditure of $100 million is incurred to develop the new phones. At the beginning of the second phase, a revenue of $50 million i: generated, resulting in a net value of $750 million, discounted by 5%, to return $714 million. We now get to a circle where an uncertainty arises (market reaction). Wé then compute the probabilities related to taking one of three possible outcomes Favorable acceptance, Moderate Acceptance, and Poor Acceptance of the new phones each with probabilities 0.4, 0.4, and 0.2 respectively. At the end of this phase there are expected revenues of $750 million, $500 million, and $300 million, all o: which are discounted by (5%) 2. These numbers are added to the tree. In the las phase, the final expected revenues are computed and added along the lines. These are $750 million, $500 million, and $300 million, respectively. Again, these numbers are discounted by (5%) 3. The final tree and its corresponding table are given below:
Table B1: Assumptions Under these assumptions we have constructed 2 cost flow models for every alternative. In the first one gas is available since the first year, in the second one gas is never available during the operating life of the project. Through this first analysis we can get a first overlook on what would be the cost of producing 1 KWh at the gross of investment costs, depending on both the technology and the fuel used. Table 2 shows the main findings of this analysis: CCGT is the technology that achieves the best performance between all the gas plants, with respect to the plants running on other fuels the oil fired had the best performance followed by CCGT running on oil. In order to assess which alternative allows the higher cost reduction we have built a cost flow model for every alternative. Table B1 shows the main assumptions that have been made tc construct each model’,
Table B1: Assumptions Under these assumptions we have constructed 2 cost flow models for every alternative. In the first one gas is available since the first year, in the second one gas is never available during the operating life of the project. Through this first analysis we can get a first overlook on what would be the cost of producing 1 KWh at the gross of investment costs, depending on both the technology and the fuel used. Table 2 shows the main findings of this analysis: CCGT is the technology that achieves the best performance between all the gas plants, with respect to the plants running on other fuels the oil fired had the best performance followed by CCGT running on oil. In order to assess which alternative allows the higher cost reduction we have built a cost flow model for every alternative. Table B1 shows the main assumptions that have been made tc construct each model’,
Table B.2: cost flow analysis results Risk analysis of the project is based on a dynamic cash flow model for each technological alternative. During the analysis we take into account two sources of uncertainty: gas availability and fuel cost. The analysis has been carried out using a Monte Carlo simulation. Gas availability
Table B.2: cost flow analysis results Risk analysis of the project is based on a dynamic cash flow model for each technological alternative. During the analysis we take into account two sources of uncertainty: gas availability and fuel cost. The analysis has been carried out using a Monte Carlo simulation. Gas availability
Picture 1: Gas/oil probability distribution
Picture 1: Gas/oil probability distribution
TableB.3: Monte Carlo Scenarios (1000 trials for each project scenario Table B.3 shows the main findings of the risk analysis 0} ect 4 is the riskiest considering that when gas is not available the cost of producing 1MW_/h is 19.269”.
TableB.3: Monte Carlo Scenarios (1000 trials for each project scenario Table B.3 shows the main findings of the risk analysis 0} ect 4 is the riskiest considering that when gas is not available the cost of producing 1MW_/h is 19.269”.
Related topics:
Information TechnologyAccountingCorporate FinanceInsuranceHeat RecoveryUse ValueOil and gas reservesCalorific ValueMarket Riskvariable cost
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