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Risk analysis in investment appraisal based on the Monte Carlo simulation technique

Author

Listed:
  • A. Hacura

    (Institute of Physics, Silesian University, Uniwersytecka 4, 40-007 Katowice, Poland)

  • M. Jadamus-Hacura

    (Department of Econometrics, Academy of Economics, Bogucicka 14, 40-226 Katowice, Poland)

  • A. Kocot

    (Institute of Physics, Silesian University, Uniwersytecka 4, 40-007 Katowice, Poland)

Abstract

This work has been prepared for the purpose of presenting the methodology and uses of the Monte Carlo simulation technique as applied in the evaluation of investment projects to analyze and assess risk. In the deterministic appraisal the basic decision rule for a project is simply to accept or reject the project depending on whether its net present value (NPV) is positive or negative, respectively. Similarly, when choosing among alternative (mutually exclusive) projects, the decision rule is to select the one with the highest NPV, provided that it is positive. Recognizing the fact that the key project variables (as: volume of sales, sales price, costs) are not certain, an appraisal report is usually supplemented to include sensitivity and scenario analysis tests. Both tests are static and rather arbitrary in their nature. During the simulation process, random scenarios are built up using input values for the project's key uncertain variables, which are selected from appropriate probability distributions. The results are collected and analyzed statistically so as to arrive at a probability distribution of the potential outcomes of the project and to estimate various measures of project risk.

Suggested Citation

  • A. Hacura & M. Jadamus-Hacura & A. Kocot, 2001. "Risk analysis in investment appraisal based on the Monte Carlo simulation technique," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 20(4), pages 551-553, April.
    • Handle: RePEc:spr:eurphb:v:20:y:2001:i:4:d:10.1007_s100510170238
    DOI: 10.1007/s100510170238
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    Citations

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    Cited by:

    1. Yonggu Kim & Eul-Bum Lee, 2018. "A Probabilistic Alternative Approach to Optimal Project Profitability Based on the Value-at-Risk," Sustainability, MDPI, vol. 10(3), pages 1-24, March.
    2. Klein, Martin & Deissenroth, Marc, 2017. "When do households invest in solar photovoltaics? An application of prospect theory," Energy Policy, Elsevier, vol. 109(C), pages 270-278.
    3. Padi, Richard Kingsley & Douglas, Sean & Murphy, Fionnuala, 2023. "Techno-economic potentials of integrating decentralised biomethane production systems into existing natural gas grids," Energy, Elsevier, vol. 283(C).
    4. Georgios Tziralis & Konstantinos Kirytopoulos & Athanasios Rentizelas & Ilias Tatsiopoulos, 2009. "Holistic investment assessment: optimization, risk appraisal and decision making," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 30(6), pages 393-403.
    5. Andres Soage & Ruben Juanes & Ignasi Colominas & Luis Cueto-Felgueroso, 2024. "Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis," Energies, MDPI, vol. 17(4), pages 1-25, February.
    6. Knoope, M.M.J. & Ramírez, A. & Faaij, A.P.C., 2015. "The influence of uncertainty in the development of a CO2 infrastructure network," Applied Energy, Elsevier, vol. 158(C), pages 332-347.

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