Optimal cash flow modeling using the Cash Flow at Risk (CFaR) method for Russian corporations

N.A. Garunov, D.A. Maksimov

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Abstract: The relevance of this study stems from the need to apply advanced proactive methods, such as Cash Flow at Risk (CFaR), to manage liquidity and inform management decisions. This article addresses the problem of quantitatively assessing the cash flow risks of Russian commodity corporations in the face of macroeconomic volatility.
Aim. The study is to develop and test a practical algorithm for assessing CFaR, adapted to the factor structure of risk for Russian corporations.
The methodology involves constructing a regression model linking cash flow to key macroeconomic variables, followed by probabilistic modeling.
The study results indicate that the projected EBITDA value for the planning period is approximately 1,600 billion rubles. Calculations based on the model’s random error distribution demonstrate that, with a 5% probability, EBITDA could be less than the expected value by up to 80 billion rubles, which quantifies the CFaR indicator. The scientific novelty lies in the development of a structural algorithm integrating regression analysis and the CFaR methodology for Russian corporations.
Conclusions. The practical significance of this approach is confirmed by the fact that it provides financial managers with a tool for building liquidity reserves and developing hedging strategies.

Keywords: Cash Flow at Risk (CFaR), risk management, cash flows, macroeconomic volatility, regression model, stress testing, Russian corporations, PJSC Lukoil

For citation. Garunov N.A., Maksimov D.A. Optimal cash flow modeling using the Cash Flow at Risk (CFaR) method for Russian corporations. News of the Kabardino-Balkarian Scientific Center of RAS. 2026. Vol. 28. No. 1. Pp. 11–24. DOI: 10.35330/1991-6639-2026-28-1-11-24

©   Garunov N.A., Maksimov D.A., 2026

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Information about the authors

Nurmagomed A. Garunov, Postgraduate Student, Basic Department of Digital Economy, Institute for the Development of the Information Society, Plekhanov Russian University of Economics;
36, Stremyannyy lane, Moscow, 115054, Russia;
ngarunov@inbox.ru, ORCID: https://orcid.org/0009-0007-0203-9899, SPIN-code: 6489-4897
Denis A. Maksimov, Doctor of Economics, Professor, Plekhanov Russian University of Economics;
36, Stremyannyy lane, Moscow, 115054, Russia;
Maksimov.DA@rea.ru, ORCID: https://orcid.org/0000-0001-8316-8359, SPIN-code: 6104-5875

Funding

The study was performed without external funding.