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Variance and Deviations in the Budgets of Regional Enterprises as an Element of Risk Measurement in the Probabilistic Model

Author

Listed:
  • Łukasz KUŹMIŃSKI

    (Wroclaw University of Economics and Business, Department of Process Management, Management Department)

  • Zdzisław KES

    (Wroclaw University of Economics and Business, Department of Process Management, Management Department)

  • Yuriy BILAN

    (Széchenyi István University, Gyor, Hungary; Sumy State University, Sumy, Ukraine)

  • Tomasz NOREK

    (The Institute of Spatial Management and Socio-Economic Geography, University of Szczecin)

  • Marcin RABE

    (University of Szczecin, Szczecin, Poland,ManagementInstitute)

  • Katarzyna WIDERA

    (Opole University of Technology, Opole, Poland,Department of Economics and Finance;k.widera@po.edu.pl Institute of Economics and Finance, University of Szczecin, Szczecin, Poland)

  • Agnieszka ŁOPATKA

    (Institute of Economics and Finance, University of Szczecin, Szczecin, Poland)

  • Dalia STREIMIKIENE

    (Institute of Sport Science and Innovations, Lithuanian Sports University, Kaunas, Lithuania)

Abstract

The aim of this article is to develop models that can measure probabilistic budget volatility risk in a manner that is not dependent on the type of cost or financing unit. Budgets are essential tools in facilitating the management process of any organization, while budget control helps optimize resource allocation and enhance operational efficiency. Using the methodology of budget deviation analysis can significantly improve the management of organizational units. However, the authors identify a research gap in terms of both methodology and application when it comes to analyzing the risk of budget variances. To address this, the authors develop models based on the theory of extreme values. The models can determine the deviation level for a specific probability level and estimate the limit level of deviation for assumed probabilities. These models can be used to holistically evaluate the level of budget implementation in the enterprise, compare the quality of budget implementation overtime and across units, and identify materiality limits of budget variances. To validate the models, empirical data from the budget control system of a major European city university was used. Empirical distributions obtained from the data were used to determine budget variances that indicate the level of deviation for a given probability level.

Suggested Citation

  • Łukasz KUŹMIŃSKI & Zdzisław KES & Yuriy BILAN & Tomasz NOREK & Marcin RABE & Katarzyna WIDERA & Agnieszka ŁOPATKA & Dalia STREIMIKIENE, 2024. "Variance and Deviations in the Budgets of Regional Enterprises as an Element of Risk Measurement in the Probabilistic Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 120-139, October.
  • Handle: RePEc:rjr:romjef:v::y:2024:i:3:p:120-139
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    References listed on IDEAS

    as
    1. Younes Bensalah, 2000. "Steps in Applying Extreme Value Theory to Finance: A Review," Staff Working Papers 00-20, Bank of Canada.
    2. Kaplan, Rs, 1975. "Significance And Investigation Of Cost Variances - Survey And Extensions," Journal of Accounting Research, Wiley Blackwell, vol. 13(2), pages 311-337.
    3. Fam, Kim-Shyan & Yang, Zhilin, 2006. "Primary influences of environmental uncertainty on promotions budget allocation and performance: A cross-country study of retail advertisers," Journal of Business Research, Elsevier, vol. 59(2), pages 259-267, February.
    4. H. Apel & G. Aronica & H. Kreibich & A. Thieken, 2009. "Flood risk analyses—how detailed do we need to be?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 49(1), pages 79-98, April.
    5. Opait, Gabriela & Bleoju, Gianita & Nistor, Rozalia & Capatina, Alexandru, 2016. "The influences of competitive intelligence budgets on informational energy dynamics," Journal of Business Research, Elsevier, vol. 69(5), pages 1682-1689.
    6. Turan G. Bali, 2003. "An Extreme Value Approach to Estimating Volatility and Value at Risk," The Journal of Business, University of Chicago Press, vol. 76(1), pages 83-108, January.
    7. Lau, Chong M. & Scully, Glennda & Lee, Alina, 2018. "The effects of organizational politics on employee motivations to participate in target setting and employee budgetary participation," Journal of Business Research, Elsevier, vol. 90(C), pages 247-259.
    8. Potter, W. D., 1949. "Normalcy Tests of Precipitation and Frequency Studies of Runoff on Small Watersheds," Technical Bulletins 170380, United States Department of Agriculture, Economic Research Service.
    9. McNeil, Alexander J., 1997. "Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 117-137, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    budget variance; probabilistic model; risk; enterprise AR;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • H68 - Public Economics - - National Budget, Deficit, and Debt - - - Forecasts of Budgets, Deficits, and Debt

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