Incorporating Risk into Capital Budgeting Decisions Using Simulation

Smith, DJ

doi:10.1108/00251749410071595

Cited by 37 publications

(16 citation statements)

References 6 publications

(6 reference statements)

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“…Several publications discuss the methods of quantification of risk in investment projects [1,[5][6][7][8][9][10][11][12][13][14][15][16]. At present, methods with quantitative risk assessments are preferred.…”

Section: A Computer Simulation Applied To An Appraisal Of Investment mentioning

confidence: 99%

Fuzziness and randomness in investment project risk appraisal

Rębiasz

2007

Computers & Operations Research

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Section: A Computer Simulation Applied To An Appraisal Of Investment mentioning

confidence: 99%

Fuzziness and randomness in investment project risk appraisal

Rębiasz

2007

Computers & Operations Research

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“…This assumption may seem more unusual than it actually is. It has become popular to perform cost estimation using Monte Carlo simulation in project management (e.g., Smith, 1994). Using Monte Carlo simulation to forecast the total cost of a project, the p.d.f.…”

Section: The Mathematical Modelmentioning

confidence: 99%

Managing Cost Overrun Risk in Project Funding Allocation

2005

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This paper discusses decision making of project funding allocation under uncertain project costs. Because project costs are uncertain and funding allocations may not necessarily match the costs required, each project is inherently subject to a cost overrun risk (COR). In this paper, a model is proposed in which project cost is treated as a factor with a probability density function. The decision maker then allocates the total funding to the projects while minimizing a weighted sum of mean and variance of the COR of the project portfolio. Some properties of project COR are derived and interpreted. Optimal funding allocation, in relationship to factors such as various project sizes and riskiness, project interdependency, and the decision maker's risk preference, is analyzed. The proposed funding allocation model can be integrated with project selection decision-making and provides a basis for more effective project control.

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“…He argues that potential risks are more clearly identified, and the expected return is more accurately measured. Smith (1994) outlines how simulations may assist managers in choosing among different potential investment projects. Spinney and Watkins (1996) apply Monte Carlo simulation as a technique for integrated resource planning at electric utilities.…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

Simulation-Based Valuation of Project Finance - Does Model Complexity Really Matter?

et al. 2011

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Simulation-based valuation of project finance -Does model complexity really matter? AbstractThis paper analyzes the impact of model complexity on the net present value distribution and the expected default probability of equity investments in project finance. Model complexity is analyzed along two dimensions: simulation complexity and forecast complexity. We aim to identify model elements which are crucial for the valuation of project finance in practice.First, we present a simulation-based project finance valuation model. Second, we vary several model aspects in order to analyze their impact on the valuation result. For forecast complexity, we apply different volatility and correlation forecasting techniques, e.g. correlation forecasts based on historical values and on a dynamic conditional correlation (DCC) model. Regarding simulation complexity, the number of Monte Carlo iterations, the equity valuation method, and the time resolution are varied.We find that the applied volatility forecasting models have a strong influence on the expected net present value distribution and on the probability of default. In contrast, correlation forecasting models play a minor role. Time resolution and equity valuation are both crucial when specifying a valuation model for project finance. For the number of Monte Carlo iterations, we demonstrate that 100,000 iterations are sufficient to obtain reliable results.

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Incorporating Risk into Capital Budgeting Decisions Using Simulation

Cited by 37 publications

References 6 publications

Fuzziness and randomness in investment project risk appraisal

Fuzziness and randomness in investment project risk appraisal

Managing Cost Overrun Risk in Project Funding Allocation

Simulation-Based Valuation of Project Finance - Does Model Complexity Really Matter?

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