IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1006-d1026298.html
   My bibliography  Save this article

New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method

Author

Listed:
  • Andrea Senova

    (Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

  • Alica Tobisova

    (Faculty of Aeronautics, Technical University of Kosice, Rampova 7, 041 21 Kosice, Slovakia)

  • Robert Rozenberg

    (Faculty of Aeronautics, Technical University of Kosice, Rampova 7, 041 21 Kosice, Slovakia)

Abstract

An environment of turbulence in the market in recent years and increasing inflation, mainly as a result of the post-COVID period and the ongoing military operation in Ukraine, represents a significant financial risk factor for many companies, which has a negative impact on managerial decisions. A lot of enterprises are forced to look for ways to effectively assess the riskiness of the projects that they would like to implement in the future. The aim of the article is to present a new approach for companies with which to assess the riskiness of projects. The basis of this is the use of the new Crystal Ball software tool and the effective application of the Monte Carlo method. The article deals with the current issues of investment and financial planning, which are the basic pillars for effective management decisions with the goal of sustainability. The article has verified a methodology that allows companies to make effective investment decisions based on assessing the level of risk. For practical application, the Monte Carlo method was chosen, as it uses sensitivity analysis and simulations, which were evaluated for two types of projects. Both simulations were primarily carried out based on a deterministic approach through traditional mathematical models. Subsequently, stochastic modeling was performed using the Crystal Ball software tool. As a result of the sensitivity analysis, two tornado graphs were created, which display risk factors according to the degree of their influence on the criterion value. The output of this article is the presentation of these new approaches for financial decision-making within companies.

Suggested Citation

  • Andrea Senova & Alica Tobisova & Robert Rozenberg, 2023. "New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1006-:d:1026298
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1006/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/2/1006/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Silvana M. Pesenti, 2022. "Reverse Sensitivity Analysis for Risk Modelling," Risks, MDPI, vol. 10(7), pages 1-23, July.
    2. Marco Nunes & António Abreu & Célia Saraiva, 2021. "A Model to Manage Cooperative Project Risks to Create Knowledge and Drive Sustainable Business," Sustainability, MDPI, vol. 13(11), pages 1-28, May.
    3. Arkadiy Larionov & Ekaterina Nezhnikova & Elena Smirnova, 2021. "Risk Assessment Models to Improve Environmental Safety in the Field of the Economy and Organization of Construction: A Case Study of Russia," Sustainability, MDPI, vol. 13(24), pages 1-37, December.
    4. Yunyu Zhang, 2020. "The value of Monte Carlo model-based variance reduction technology in the pricing of financial derivatives," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-13, February.
    5. Georgios K. Koulinas & Olympia E. Demesouka & Konstantinos A. Sidas & Dimitrios E. Koulouriotis, 2021. "A TOPSIS—Risk Matrix and Monte Carlo Expert System for Risk Assessment in Engineering Projects," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    6. Jungmin An & Dong-Kwan Kim & Jinyeong Lee & Sung-Kwan Joo, 2021. "Least Squares Monte Carlo Simulation-Based Decision-Making Method for Photovoltaic Investment in Korea," Sustainability, MDPI, vol. 13(19), pages 1-14, September.
    7. Alica Tobisova & Andrea Senova & Robert Rozenberg, 2022. "Model for Sustainable Financial Planning and Investment Financing Using Monte Carlo Method," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    8. Rama Cont & Romain Deguest & Giacomo Scandolo, 2010. "Robustness and sensitivity analysis of risk measurement procedures," Quantitative Finance, Taylor & Francis Journals, vol. 10(6), pages 593-606.
    9. Katarina Valaskova & Tomas Kliestik & Lucia Svabova & Peter Adamko, 2018. "Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
    10. Li, Zhenghui & Yang, Cunyi & Huang, Zhehao, 2022. "How does the fintech sector react to signals from central bank digital currencies?," Finance Research Letters, Elsevier, vol. 50(C).
    11. Silvana M. Pesenti, 2021. "Reverse Sensitivity Analysis for Risk Modelling," Papers 2107.01065, arXiv.org, revised May 2022.
    12. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    13. Jaroslava Janekova & Jana Fabianova & Andrea Rosova, 2016. "Environmental And Economic Aspects In Decision Making Of The Investment Project “Wind Park”," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 13(1), pages 90-100, June.
    14. Kyoung-jae Kim & Kichun Lee & Hyunchul Ahn, 2018. "Predicting Corporate Financial Sustainability Using Novel Business Analytics," Sustainability, MDPI, vol. 11(1), pages 1-17, December.
    15. Daniel J Arenas & Lanair A Lett & Heather Klusaritz & Anne M Teitelman, 2017. "A Monte Carlo simulation approach for estimating the health and economic impact of interventions provided at a student-run clinic," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-12, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrey Kulikov & Naief Alabed Alkader & Galina Panaedova & Aleksandr Ogorodnikov & Evgenii Rebeka, 2023. "Modelling Optimal Capital Structure in Gas and Oil Sector by Applying Simulation Theory and Programming Language of Python (Qatar Gas Transport Company)," Energies, MDPI, vol. 16(10), pages 1-15, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fissler, Tobias & Pesenti, Silvana M., 2023. "Sensitivity measures based on scoring functions," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1408-1423.
    2. Tobias Fissler & Silvana M. Pesenti, 2022. "Sensitivity Measures Based on Scoring Functions," Papers 2203.00460, arXiv.org, revised Jul 2022.
    3. Alica Tobisova & Andrea Senova & Gabriela Izarikova & Ivana Krutakova, 2022. "Proposal of a Methodology for Assessing Financial Risks and Investment Development for Sustainability of Enterprises in Slovakia," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    4. Alica Tobisova & Andrea Senova & Robert Rozenberg, 2022. "Model for Sustainable Financial Planning and Investment Financing Using Monte Carlo Method," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    5. Silvana M. Pesenti, 2021. "Reverse Sensitivity Analysis for Risk Modelling," Papers 2107.01065, arXiv.org, revised May 2022.
    6. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2021. "Sustainable Supply Chains with Blockchain, IoT and RFID: A Simulation on Order Management," Sustainability, MDPI, vol. 13(11), pages 1-23, June.
    7. Makam, Vaishno Devi & Millossovich, Pietro & Tsanakas, Andreas, 2021. "Sensitivity analysis with χ2-divergences," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 372-383.
    8. Plischke, Elmar & Borgonovo, Emanuele, 2019. "Copula theory and probabilistic sensitivity analysis: Is there a connection?," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1046-1059.
    9. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2012. "When more is less: Using multiple constraints to reduce tail risk," Journal of Banking & Finance, Elsevier, vol. 36(10), pages 2693-2716.
    10. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    11. Wenxuan Ma, 2022. "Exploring the Role of Educational Human Capital and Green Finance in Total-Factor Energy Efficiency in the Context of Sustainable Development," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    12. F. Wang & G. H. Huang & Y. Fan & Y. P. Li, 2020. "Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3199-3217, August.
    13. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    14. Barrieu, Pauline & Scandolo, Giacomo, 2014. "Assessing financial model risk," LSE Research Online Documents on Economics 60084, London School of Economics and Political Science, LSE Library.
    15. Carole Bernard & Ludger Rüschendorf & Steven Vanduffel & Ruodu Wang, 2017. "Risk bounds for factor models," Finance and Stochastics, Springer, vol. 21(3), pages 631-659, July.
    16. Daniel Harenberg & Stefano Marelli & Bruno Sudret & Viktor Winschel, 2019. "Uncertainty quantification and global sensitivity analysis for economic models," Quantitative Economics, Econometric Society, vol. 10(1), pages 1-41, January.
    17. Lazar, Emese & Zhang, Ning, 2019. "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 74-93.
    18. Giovanni Paolo Crespi & Elisa Mastrogiacomo, 2020. "Qualitative robustness of set-valued value-at-risk," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 91(1), pages 25-54, February.
    19. Mai Jan-Frederik & Schenk Steffen & Scherer Matthias, 2015. "Analyzing model robustness via a distortion of the stochastic root: A Dirichlet prior approach," Statistics & Risk Modeling, De Gruyter, vol. 32(3-4), pages 177-195, December.
    20. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1006-:d:1026298. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.