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RESEARCH CENTRE FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS of Ukraine (KHARKIV, UKRAINE)

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Liburkina L. M.

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The Contemporary Methods of Financial Risk Assessment
Makarenko Y. P., Avrakhov L. A.

Makarenko, Yuliia P., and Avrakhov, Leonid A. (2026) “The Contemporary Methods of Financial Risk Assessment.” Business Inform 3:444–454.
https://doi.org/10.32983/2222-4459-2026-3-444-454

Section: Finance, Money Circulation and Credit

Article is written in Ukrainian
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UDC 33.336.7.368

Abstract:
The scientific article thoroughly and comprehensively examines contemporary methods of financial risk assessment in the context of the relentless growth of global instability in world financial markets, the rapid digitalization of all economic processes, and the significant complexity of the strategic behavior of modern economic agents. The work provides a detailed analysis of traditional quantitative approaches to risk management, which for decades have formed a reliable foundation for the financial stability of institutions, including classical statistical models such as Value at Risk (VaR), Conditional Value at Risk (CVaR), the complex family of econometric GARCH models, as well as fundamental Markowitz portfolio methods. During the conducted research, the key advantages of these approaches were clearly identified, as well as the critical limitations that inevitably arise during their practical application in conditions of deep crisis phenomena and extreme market fluctuations, when standard theoretical assumptions about the normal distribution of asset returns no longer correspond to market realities. Special attention within the scientific work is given to innovative behavioral models for assessing financial risks. These models allow going beyond the narrow limits of the conception of a purely «rational investor», fully taking into account real psychological factors, cognitive biases, heuristics, and subjective perception of probabilities, which quite often becomes the main catalyst for market panics, irrational pessimism, or the formation of speculative bubbles. The feasibility of deeply integrating classical financial metrics with the most modern methods of machine learning and deep learning has been scientificaly substantiated. A separate research focus has been placed on the high efficiency of using reinforcement learning algorithms, specifically policy gradient methods and deep Q-networks, which are capable of independently and extremely quickly adapting to new input data in real time, revealing hidden nonlinear relationships. It has been proved that the systematic implementation of intelligent and hybrid models significantly increases the accuracy of predicting potential financial losses and forms flexible, adaptive risk management systems of the new generation. Such systems are capable not only of proactively identifying threats but also of responding to the slightest changes in market conditions as rapidly as possible, effectively minimizing the negative impact on the institution’s capital and liquidity. The conclusions obtained as a result of the study have high practical significance and can be directly implemented in the daily activities of commercial banks, investment funds, insurance companies, and other financial institutions. This will make it possible to significantly increase the soundness of strategic management decisions, optimize the asset structure, improve stress-testing procedures, and strengthen the overall financial stability of the organization in the long term.

Keywords: financial risks; risk assessment; VaR; GARCH; behavioral finance; machine learning; artificial intelligence.

Tabl.: 5. Bibl.: 10.

Makarenko Yuliia P. – Doctor of Sciences (Economics), Professor, Professor, Department of Finance, Banking and Insurance, Oles Honchar Dnipro National University (72 Nauky Ave., Dnіpro, 49045, Ukraine)
Email: [email protected]
Avrakhov Leonid A. – Student, Oles Honchar Dnipro National University (72 Nauky Ave., Dnіpro, 49045, Ukraine)
Email: [email protected]

List of references in article

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