INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 02,2025
Journal:
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EQUITY RISK MANAGEMENT IN COMMERCIAL BANKS OF UZBEKISTAN:
REGULATION AND AUTOMATION WAYS
O.F. Aliqoriev
PhD in Economics, Assoc. Prof., “Business Management and Entrepreneurship (MBA)” of the
Higher School of Business and Entrepreneurship under the Cabinet of Ministers of the Republic
of Uzbekistan
D.A.Djalilov
Head of Risk Management Service of JSC “YANGI BANK”; MA in Economics “Program on
economic development”, Ritsumeikan University; Master of “International Economic Relations”,
University of World Economy and Diplomacy
Annotation:
Equity risk is one of the key market risks faced by commercial banks. Changes in
prices of securities can have a significant impact on the financial performance of banks and their
sustainability. This article examines in detail the methods of equity risk management, regulatory
requirements of the Central Bank of Uzbekistan (CBU), as well as modern approaches to
automating the management of this risk. Particular attention is paid to the use of algorithmic
trading, artificial intelligence (AI) and automated risk management systems (RMS), which help
minimize potential losses of banks from fluctuations in the value of securities.
Introduction
Equity risk is associated with potential losses due to changes in prices of shares, bonds and other
financial instruments included in the bank's investment portfolio. The level of this risk depends
on market volatility, the structure of the investment portfolio and the bank's ability to effectively
manage market changes [1].
With the development of the stock market of Uzbekistan, banks increasingly include securities in
their assets, seeking to increase profitability and diversify sources of income. However, this
process is associated with a number of problems: low liquidity of the country's stock market,
limited availability of derivative instruments for hedging and the lack of a single automated
system for managing stock risk [2]. In this regard, commercial banks need effective strategies for
managing stock risk and integrating them into the bank's risk management system.
1. Methods of equity risk management
1.1 Portfolio diversification
Diversification is one of the main methods of reducing equity risk. Its essence lies in the
distribution of investments between different asset classes, economic sectors, regions and
currencies, which reduces dependence on changes in the value of individual instruments. In the
event of a fall in the value of one type of asset, other instruments can compensate for possible
losses [3].
In the case of banks in Uzbekistan, diversification is carried out through the purchase of bonds,
shares of state and private companies, as well as by placing funds on foreign markets. However,
limited liquidity and the small size of the local stock market create obstacles to effective
diversification [4].
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 02,2025
Journal:
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1.2. VAR ( Value - at - Risk ) analysis
The VAR (Value at Risk) method allows estimation of the possible losses of an investment
portfolio over a certain period with a given probability. It is calculated using the formula:
VAR = Z ∗ σ ∗ t
Where:
- Z is the coefficient corresponding to the confidence level (for example, 1.65 for 95%);
-
σ
— standard deviation of asset returns;
- t - time horizon of risk measurement.
Banks use three main approaches to calculating VAR:
-
Historical modeling
based on past price movement data.
-
Monte Carlo model
, which involves simulating a variety of possible scenarios of market
changes.
- The variance-covariance method, which takes into account the volatility of assets and the
correlation between them [5].
This tool allows banks to assess the maximum level of equity risk and develop strategies to
minimize it.
1.3 Hedging with Derivatives
Derivatives such as futures, options and swaps allow banks to hedge their investments against
adverse market movements. For example, hedging using futures contracts can be expressed
through the formula:
h
∗
=
ρσ
S
σ
F
Where:
-
h
∗
: is optimal hedging ratio,
-
ρ
: is the correlation coefficient between the underlying asset and the futures,
-
σ
S
: is standard deviation of the spot price,
-
σ
F
: is the standard deviation of the futures price.
However, the derivatives market in Uzbekistan is in its infancy, and banks do not yet have wide
opportunities to use these instruments. The introduction of derivatives and the development of a
secondary securities market could significantly improve the efficiency of stock market risk
management [6].
2. Regulation of stock market risk in Uzbekistan
The Central Bank of Uzbekistan has established a number of regulatory requirements for
managing equity risk in commercial banks. According to the Regulations approved by the
Central Bank of the Republic of Uzbekistan - “Regulations on the maximum amount of risk per
borrower, including persons associated with the bank or a group of related borrowers” ,
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
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“ Regulations on the requirements for the risk management system in banks and banking groups”,
banks are required to:
- Conduct regular assessment of market risk using the VAR method [7].
- Limit the maximum amount of investment in individual asset classes.
- Conduct stress testing of the securities portfolio at least once per quarter [8].
- Implement internal risk assessment models that take into account macroeconomic factors.
3. Automation of stock risk management
3.1 Algorithmic Trading and Machine Learning
Modern banks use algorithmic trading and machine learning to quickly respond to market
changes. Algorithms allow for automatic analysis of quotes, identification of trends, and making
trading decisions without human intervention, which reduces risks [9].
3.2. Automated risk management systems (RMS)
RMS systems provide real-time monitoring of stock market risk. They integrate with banking IT
infrastructures, assess the impact of macroeconomic changes, and suggest adjustments to
investment strategy [10].
3.3. Using Blockchain and Big Data
Blockchain and Big Data technologies allow for high-precision risk analysis. Blockchain
provides transparency of transactions, and Big Data helps to predict market trends [11].
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1. Hull, J. C. Options, Futures, and Other Derivatives. Pearson Education, 2022.
2. Basel Committee on Banking Supervision. International Convergence of Capital
Measurement and Capital Standards. Bank for International Settlements, 2004.
3. Markowitz, H. Portfolio Selection. The Journal of Finance, 1952.
4. Sharpe, W. F. Capital Asset Prices: A Theory of Market Equilibrium under Conditions of
Risk. The Journal of Finance, 1964.
5. Jorion, P. Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill,
2007.
6. Bodie, Z., Kane, A., Marcus, A. Investments. McGraw-Hill, 2021.
7. Central Bank of the Republic of Uzbekistan. Resolution No. 3283-1 dated 11/14/2024.
8. International Monetary Fund. Risk Management in the Banking Sector. Washington, DC,
2023.
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Economic Perspectives, 16 (12), December 2022. 20-31 Retrieved from
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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 02,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
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A . Iqtisodiyot tarmoqlari kesimida muammoli
creditlarni kaitmaslik darajalarini tagolili // The Multidisciplinary Journal of Science and
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