Macroeconomic Indicators Research Articles

Экономический рост и инфляция

The article examines economic growth, which characterizes the value increase in output in an economic system over a certain period of time. According to the authors, quantitative indicators of economic growth reflect only one aspect of the development of the economic system. To understand the ongoing economic processes, it is necessary to identify the qualitative characteristics of development, justifying the relationship of economic growth indicators with other macroeconomic indicators: inflation, the key rate of the Bank of Russia, unemployment, and others. This will allow us to assess the degree of balanced development of the entire economic system, its resistance to the disturbing effects of the external and internal economic environment. To ensure economic growth, a set of regulatory measures by the state is needed to support the activity of commercial structures. An analysis of various scientific and theoretical approaches of foreign and Russian economic scientists to the problem of achieving economic growth shows the relationship of this category with the instruments and methods of financial and monetary policy used by the executive authorities and the Bank of Russia. The article notes that the practical application of such methods does not fully meet the goals of economic development. The current model of the Russian economy is more consistent with the concept of oligarchic capitalism. This characterizes the increasing wealth of individuals and the social stratification of society. It is revealed that inflation occurs as a result of imbalance in commodity and foreign exchange markets. The objective basis and root cause of the inflationary process, according to the authors, is the devaluation of the ruble. Therefore, the inflationary process must be calculated taking into account the currency component (changes in the exchange rate).It is concluded that the problems of economic development have required drastic institutional changes in the country using a variety of financial instruments, special attention should be paid to the digitalization of economic processes, the implementation of major investment projects in the field of infrastructure.

Testing hypotheses about the impact of technology transfer on macroeconomic indicators using the least squares method

Testing hypotheses about the impact of technology transfer on macroeconomic indicators using the least squares method

Current aspects of small and medium en-trepreneurship development in the agro-industrial sector of Azerbaijan

The article discusses current aspects of small and medium entrepreneurship development in the agro-industrial sector of Azerbaijan. The problems of agro-industrial complex development are explained. The factors that determine the need for small and medium enterprises development in the agro-industrial complex are identified and systematized. The advanced world experience in the field of agribusiness is studied and summarized. The advantages of agribusiness development as one of the main elements of economic diversification are presented. A classification of business entities in the agro-industrial complex based on the criteria of legal status and size has been developed. The characteristics of small and medium-sized businesses have been identified. The organizational structure of small and medium-sized enterprises in the agricultural sector has been provided. The main macroeconomic indicators of small businesses in the Republic of Azerbaijan have been analyzed. The structure of the number of micro, small and medium-sized enterprises in the country by economic sectors has been prepared. The dynamics of investments in agriculture in Azerbaijan have been analyzed. Given the relevance of economic diversification, the fact that agro-industrial enterprises are one of the main components of the national economy has been scientifically substantiated. The factors hindering the competitiveness of enterprises in the processing industry of the agricultural sector of the Republic of Azerbaijan in foreign markets have been considered, and solutions to these problems have been provided. Taking into account new challenges and tasks for the revival of postconflict territories, priority areas of activity for the development of small and medium entrepreneurship in the agro-industrial sector of Azerbaijan have been identified and proposals have been prepared.

Dynamics of REIT Returns and Volatility: Analyzing Time-Varying Drivers Through an Explainable Machine Learning Approach

Abstract Real Estate Investment Trust (REIT) returns and volatility have been extensively studied, yet typically in isolation from each other. Given that returns and volatility are generally connected in the eyes of investors, we simultaneously analyze the drivers of REIT returns and volatility over the modern REIT era (1991–2022) using an eXtreme Gradient Boosting (XGBoost) machine learning algorithm. We enhance transparency and utility through the application of explainable artificial intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP) and Accumulated Local Effects (ALE), which unpack the decision-making process of the model. Our analysis reveals that while no single feature consistently dominates, the influence of various drivers fluctuates significantly over time. Notably, the importance of macroeconomic indicators generally diminishes, while REIT-specific characteristics become more influential during the sample period. Furthermore, market cycles (macroeconomic shocks) cause large deviations from otherwise long-run patterns. However, during these times of economic uncertainty, drivers of risk and return correlate more strongly in comparison to times of economic stability. Lastly, we find non-linearities in the way the drivers influence returns and volatility. These insights have significant implications for investors, policymakers, and researchers as they navigate the evolving landscape of real estate investments.

FORECAST OF THERMAL ENERGY CONSUMPTION CONSIDERING WARTIME REALITIES AND POST-WAR ECOVERY FOR THE MID-TERM PERSPECTIVE (UP TO 2030)

The forecast of heat energy consumption in the country and by sections of the economy is relevant for determining the amount of heat energy generation by various types of heat generating sources operating on organic fuel and renewable energy sources. The forecast of heat energy consumption is based on macroeconomic indicators. These indicators were estimated from open sources of information based on the actual state of the country’s economy, which suffered a reduction due to the destruction caused by the Russian aggressor. The purpose of the study is to develop methodical approaches to forecasting the consumption of thermal energy according to scenarios of economic development, taking into account the realities of wartime and limited statistical information. The article presents the macro-economic forecast of the gross domestic product of Ukraine and the gross added values of consolidated sections of the economy developed by the authors. It takes into account the realities of wartime based on the assessments of domestic economists and specialists of international financial organizations. A methodological approach to forecasting heat energy consumption at the level of the country and sections of the economy is presented. Forecasts of thermal energy consumption have been developed for two scenarios of economic development: 1) the scenario of moderate recovery under the conditions of revival of the country’s economy by 2030; 2) conservative, provided that existing trends in the development of the industrial sector of the economy are preserved and large destroyed enterprises are not restored. The need to modernize the infrastructure and reorient the economy to the latest technological solutions is taken into account. The prediction of thermal energy consumption in the article is based on a comprehensive analysis of the current state of the energy system and the industrial sector of the economy, their potential with the introduction of innovations and adaptation to changing conditions, taking into account international experience. Bibl. 70, Tab. 7.

THE CONCEPTUAL ROLE OF THE SHADOW ECONOMY IN ECONOMIC GROWTH

The article examines the conceptual role of the shadow and gray economies in economic growth and substantiates the need to take them into account in multifactor models of economic development. The general division of the economy into white (official), gray and shadow economies, their characteristics and peculiarities of influence on macroeconomic indicators are considered. The official economy operates within the framework of the law, provides tax revenues, promotes employment growth and investment attractiveness. The gray economy, although not illegal, partially escapes state control, which leads to tax evasion and distortion of economic processes. The shadow economy encompasses illegal activities that may contribute to the short-term survival of a business, but in the long run undermine economic stability. It is proposed to include indicators of the shadow and gray economies in multivariate economic growth models to increase the degree of adaptability of the models and to make more accurate forecasts. In economic growth models, the gray economy is taken into account by adjusting the savings parameters, while the shadow economy requires the introduction of a separate capital intensity equation based on its own production function. To correctly reflect its impact, expressions describing shadow savings and investment are added to the equation of aggregate savings.

Identification of risk factors: a comparison of conventional and Islamic stocks

The study identifies the difference in the long-run risk factors for Conventional Capital Market (CCM) and Islamic Capital Market (ICM) in the post-Shari’ah-screening era in an emerging market. The sample includes macroeconomic variables representing the real sector (industrial production), money market (interest rate), international market (exchange rate) and external sector (exports and workers’ remittances) and two market indexes for 164 Months (01/10–08/23). Johansen cointegration and Granger causality tests are applied to document the evidence. Results support the integration of market indexes with macroeconomic indicators; however, market indexes lack mutual integration in the long run. The integrated group of variables differs slightly for ICM (exchange rate and industrial production) and CCM (industrial production). The real sector activity is reflected in the market, while the monetary sector is missing. The behaviour of the Islamic market is in line with the theory – a reflection of the real sector and lack of integration with interest rates. We recommend three policy actions, including improved facilitation of industrial production, prudent management of exchange rate, and a balanced monetary policy, as theory suggests the usefulness of stock indicators for monetary policymaking. The comparative study on macroeconomic risk factors in an emerging market enhances the understanding of a market with dual indexes, including CCM and ICM. First published online 31 March 2025

Macroeconomic Enablers of Sustainable Development in G20 Countries: The Role of Tourism

The G20 countries present a congenial macroeconomic environment in terms of per capita GDP, gross fixed capital formation, labour-force participation, inflation, cross-border trade, financial sector development, and human development for the growth of travel and tourism. These countries have a strong potential for the development of travel and tourism in terms of international tourist arrivals, international tourism expenditure and international tourism receipts. As appropriately recognised by the members of G20 in 2012 for the first time, and as identified during the India’s Presidency in 2023, the potential of travel and tourism can optimally be utilized to make progress towards the SDGs by 2030. In this direction, this study is an attempt to empirically examine the impact of tourism development on sustainable development in G20 countries. The novelty of this study lies in estimating short-run and long-run effects of selected covariates on sustainable development in G20 nations in a panel framework. The results of the estimation of PMG based ARDL regression indicate a statistically significant positive contribution of the development of travel and tourism on sustainable development when macroeconomic indicators are the enablers. This finding contradicts the findings of a recent study by Destek & Aydin that tourism can be detrimental to sustainable development. Thus, the present study ushers a new direction for tourism-led sustainable development. The policy implication is that the effective and efficient implementation appropriate tourism development strategies in such a favourable macroeconomic environment can add to the progress of SDGs in G20 countries.

DETERMINANTS OF INVESTMENT ATTRACTIVENESS OF UKRAINE IN THE CONTEXT OF GLOBAL ECONOMIC TURBULENCE

The article analyzes the main factors that determine the investment attractiveness of Ukraine in the context of global economic instability. The study examines the key determinants of attracting foreign capital, including macroeconomic indicators, legal system, financial markets and the level of infrastructure development. Particular attention is paid to the role of political stability, the fight against corruption and the effectiveness of public administration in creating a favorable investment climate. The analysis shows that improving Ukraine’s investment attractiveness requires a comprehensive approach that includes legislative reform, stabilization of the financial sector, development of transport and energy infrastructure, and improvement of the business environment. Of particular importance is the protection of investors’ rights, including ensuring the transparency of the judicial system, the effectiveness of investment dispute resolution mechanisms, and the guarantee of property rights. The study emphasizes the importance of overcoming legal and administrative barriers that limit investment inflows. In particular, it is necessary to improve the regulatory environment, simplify business procedures, and create favorable conditions for entrepreneurship. Financial stability plays an important role, including controlling inflation, maintaining exchange rate stability, and effective public debt management. In addition, considerable attention is paid to the impact of global economic processes on the investment climate of Ukraine. In particular, the impact of changes in international trade relations, political sanctions, and transformations in global financial markets is analyzed. In this context, international economic cooperation and Ukraine’s integration into global financial mechanisms are of particular importance. The strategic role of post-war reconstruction as one of the key factors in attracting foreign capital is also considered. Rebuilding the destroyed infrastructure, creating modern production facilities, and introducing innovative technologies can become the basis for long-term economic growth and increasing Ukraine’s international competitiveness. In summary, the study confirms that creating a stable and predictable investment environment is a prerequisite for the recovery and further development of Ukraine’s economy. Implementation of comprehensive reforms, ensuring transparency of public administration, and active cooperation with international partners will help to create a competitive investment climate and attract the necessary resources for economic growth.

Econometric Analysis of Key Factors Affecting Domestic Tourism Development in Kazakhstan

Today, tourism plays a crucial role in Kazakhstan's economic diversification and regional development, especially in the post-pandemic era. This study aims to identify and quantify the key economic and infrastructural factors influencing tourism development in Kazakhstan. The research employs multiple regression analysis based on econometric modeling of macroeconomic indicators from 2013 to 2024. Based on the study of time series and correlation dependencies of indicators, a close relationship was found between the number of tourists and such variables as gross domestic product, the share of tourism in GDP, the number of organizations in the tourism sector, and income from accommodation facilities. The results indicate a strong positive relationship between domestic tourism growth and GDP per capita (β = 0.0508, p < 0.01) as well as the share of tourism in GDP (β = 447,484, p < 0.05). The number of tourism-related organizations also demonstrates a positive, though weaker, effect (β = 637.8, p<0.1). However, accommodation revenue and hotel occupancy were found to be statistically insignificant. Using methods and SWOT analysis, the strengths and weaknesses of the industry were identified, and opportunities and threats influencing its sustainable development were determined. The article proposes strategic measures to improve the competitiveness of the tourism industry, including the development of regional tourism, diversification of tourism products, the introduction of digital technologies, and environmentally responsible practices. These findings offer valuable insights for policymakers and industry stakeholders to support long-term tourism growth in Kazakhstan.

Modelling the Risks of the Exporter’s Company in Times of Crisis

Exporting companies face numerous risks generated both within the country and by importing countries. Therefore, early detection and analysis of national and international risks is relevant and urgent. The aim of the study is to analyse the relationship between the volume of Ukrainian exports and macroeconomic indicators of Ukraine and importing countries. This will reveal the potential impact of fluctuations in these indicators on changes in the level of major export risks. The study employed economic and statistical analysis, correlation and regression analyses. The study determined the key influencing factors on the activities of Ukrainian exporting companies depending on the importing country. The level of gross domestic product (GDP) per capita is an important factor influencing the volume of exports from Ukraine to Poland, Spain, and Germany. Exports to Poland are also closely correlated with fluctuations in the local currency. Exports to China and Turkey do not significantly depend on changes in local macroeconomic indicators. Macroeconomic indicators of Ukraine - GDP per capita, customs, as well as other import duties and taxes on international trade - significantly affect the total volume of exports. The obtained results and drawn conclusions are of practical importance for Ukrainian exporters in terms of raising awareness of the described risks, and the recommendations provided on the basis of the study will contribute to their mitigation. The prospect of further research is to identify differences in the exporters’ risks depending on the field of their activity.

PREDMAC A Stochastic Monte Carlo-Driven Predictive Analytics Framework Integrating Regression Modelling for Macroeconomic Factor Evaluation and Forecasting of WTI Crude Oil Price Volatility

Introduction: WTI crude oil prices are influenced by multiple macroeconomic factors, primarily related to supply. This study employs regression equations and Monte Carlo simulations to analyze these dependencies and predict price trends. Objectives: This research aims to establish the relationship between WTI crude oil prices and eight key macroeconomic indicators. Historical data from the past 15 months is compiled to support this analysis. Methods: A multiple regression model is used to quantify the impact of macroeconomic variables. Multicollinearity is assessed to ensure model robustness, and Monte Carlo simulations are applied for scenario generation. Results: Findings reveal that certain macroeconomic variables significantly affect crude oil price fluctuations. The model successfully captures dependencies and provides insights into potential price movements. Conclusions: This study enhances understanding of crude oil price dynamics, aiding investors and policymakers in decision-making. The proposed framework can be extended for further analysis of commodity markets.

МАКРОЕКОНОМІЧНІ АСПЕКТИ ВПЛИВУ КРИПТОВАЛЮТ НА ГРОШОВИЙ РИНОК

This article examines the macroeconomic aspects of the impact of cryptocurrencies on the money market, focusing on their relationship with traditional financial systems, monetary policy, and financial stability. The relevance of the study is due to the growing use of cryptocurrencies as a financial instrument and their integration into the global economy. In the last decade, digital assets have become widespread not only as a means of payment, but also as an element of an investment portfolio, which requires an in-depth analysis of their impact on economic processes. The purpose of the study is to assess the impact of cryptocurrencies on the money supply, monetary regulation mechanisms, and financial stability of states. The research methodology is based on the use of macroeconomic analysis, statistical methods, and a comparative analysis of different approaches to regulating the cryptocurrency market in different countries. Empirical data were used to identify the main trends in the interaction of digital assets with traditional financial systems and potential threats to the monetary policy of central banks. The results of the study indicate that cryptocurrencies can act as a factor that changes the traditional mechanisms of money market regulation. The decentralization of cryptocurrencies and their independence from state control pose new challenges to regulators. On the one hand, crypto-assets can promote financial inclusion and provide alternative methods of financing, on the other hand, they increase the level of volatility and create risks of financial instability. The article examines the role of stablecoins in international financial flows and their impact on the stability of the money supply. It is noted that stablecoins can act as an alternative to fiat currencies in the digital economy, which raises questions about their regulation and place in the monetary policy of states. Potential scenarios for the integration of cryptocurrencies into the modern financial system are investigated, in particular, through the development of central bank digital currencies (CBDCs), which can become an answer to the challenges posed to financial systems by the rapid development of blockchain technologies. Prospects for further research in this area include analyzing the effectiveness of regulatory approaches to controlling cryptocurrencies, studying the correlation between the Bitcoin exchange rate and macroeconomic indicators, and developing models for predicting the dynamics of the digital asset market. An extended study of the interaction of cryptocurrencies with the traditional banking system and their impact on international financial stability remains an important area of ​​scientific research in the future.

МЕТОДОЛОГІЯ НАУКОВОГО ОЦІНЮВАННЯ ДОБРОБУТУ НАСЕЛЕННЯ: СТРУКТУРНИЙ АНАЛІЗ МАКРОЕКОНОМІЧНИХ ІНДИКАТОРІВ

The article examines methodological approaches to assessing the well-being of the population. Three theoretical approaches are considered that have a significant impact on people's well-being. The economic approach focuses on financial indicators, such as income and consumption. The social approach analyzes access to education, health care, and social services. The multidisciplinary approach combines various aspects of life. Using the abstract-logical method, the essence of conceptual methodological approaches is revealed, with the help of which the quality of life of the population can be measured. The statement is proved that the considered approaches have both obvious differences and certain similar characteristics. In this regard, it is established that methodological approaches to assessing the well-being of the population include the use of objective indicators and subjective assessments. The aim of the article is to analyze the main methodological approaches to assessing the well-being of the population, identify their advantages and disadvantages, as well as assess modern challenges affecting the level of well-being. Macroeconomic indicators are analyzed to assess the potential impact of the proposed measures on the economy of Ukraine. It is established that measures to stimulate economic growth and employment lead to a decrease in unemployment, an inflow of investments and an increase in the average income of the population of Ukraine. Using the method of forecasting, it was found that, provided that the inflation control policy is effectively implemented, it is possible to stabilize the purchasing power of the population and reduce the level of poverty, which will positively affect the social component of the proposed measures. The theoretical and practical results of the study made it possible to formulate recommendations on the prospects for improving the methodological foundations of assessing the well-being of the population, related to improving the assessment by integrating subjective indicators and expanding environmental indicators. The results obtained emphasize the importance of the process of scientific assessment of the well-being of the population as an important prerequisite for the stability and economic development of the country.

Neural Network Model for Early Detection of Currency Crisis in Singapore: Optimization Comparison

This study aims to develop an early detection model for the currency crisis in Singapore using a neural network (NN) algorithm based on macroeconomic indicators. By utilizing the exchange market pressure (EMP) threshold approach, this study identifies currency crisis signals that can function as early warnings. In this study, a comparison of four NN optimization methods was conducted, namely stochastic gradient descent (SGD), Adam, Nadam, and AdaBound. The data used include 11 macroeconomic indicators from January 1990 to June 2021. The study results show that the NN model with Nadam optimization provides the best performance, with higher accuracy, sensitivity, and specificity compared to other optimization methods. This model successfully predicted crisis signals with an accuracy of 95.89%, a sensitivity of 98.36%, and a specificity of 83.33%. These findings can be used as a basis for decision-making in anticipating the currency crisis in Singapore.

Deep Learning vs. Gradient Boosting: Optimizing Transport Energy Forecasts in Thailand Through LSTM and XGBoost

Thailand’s transport sector faces critical challenges in energy management amid rapid economic growth, with transport accounting for approximately 30% of total energy consumption. This study addresses research gaps in transport energy forecasting by comparing Long Short-Term Memory (LSTM) neural networks and XGBoost models for predicting transport energy consumption in Thailand. Utilizing a comprehensive dataset spanning 1993–2022 that includes vehicle registration data by size category, vehicle kilometers traveled, and macroeconomic indicators, this research evaluates both modeling approaches through multiple performance metrics. The results demonstrate that XGBoost consistently outperforms LSTM, achieving an R-squared value of 0.9508 for test data compared to LSTM’s 0.2005. Feature importance analysis reveals that medium vehicles contribute 36.6% to energy consumption predictions, followed by truck VKT (20.5%), with economic and demographic factors accounting for a combined 15.2%. This research contributes to both methodological understanding and practical application by establishing XGBoost’s superior performance for transport energy forecasting, quantifying the differential impact of various vehicle categories on energy consumption, and demonstrating the value of integrating vehicle registration and usage data in predictive models. The findings provide evidence-based guidance for prioritizing policy interventions in Thailand’s transport sector to enhance energy efficiency and sustainability.

The Causal Relationship between Harmonized Consumer Price Index, Producer Price Index and Gross Domestic Product: Evidence from Kosovo

The authors have conducted an empirical study of the relevant relationship based on the assumption that the consumer price index (CPI) and producer price index (PPI), respectively, have an impact on the gross domestic product (GDP), the economic growth of a country, and vice versa. They also examined the development trend of these significant macroeconomic indicators in Kosovo as a developing country. Uddin and Rahman (2022) have shown that the emerging nations should address the issue of inflation by maintaining the inflation rate in single digits; this would lead to economic progress. The study was prepared based on quarterly time series data from 2010: Q1 to 2022: Q3. The data were analyzed using EViews ver10. The model was examined using the ADF test, Johansen cointegration test, VECM estimation, and Granger causality test. Several statistical techniques were applied to examine the existence of causal linkages, stationarity, and long-term relationships among the model’s variables. Positive results from the analysis imply that the model is stable. The results of the ADF test specifically show that GDP, HICP, and PPI remain stationary when the initial difference is considered. The Johansen cointegration test also supports a long-term relationship between these variables. The Granger analysis results demonstrate a bilateral relationship between GDP and HICP and that the alternative hypothesis is accepted. The findings for GDP and PPI indicate that there is no meaningful causal or unilateral relationship between them. This research complements prior studies in the disciplines of finance and macroeconomics by emphasizing that the findings of this study show that the government of Kosovo should try to control the influence of the consumer price index and the producer price index on GDP.

Impact of Macroeconomic Indicators on Stock Market Performance: Evidence from Nepal

Background: The stock market is a critical indicator of economic activity, reflecting both investor sentiment and macroeconomic conditions. Understanding the relationship between stock market performance and macroeconomic variables is essential for policymakers, investors, and financial analysts. Objective: This study aims to analyze the relationship between the Nepal Stock Exchange (NEPSE) and key macroeconomic variables, including deposits, reserve money, liquidity, and lending interest rates, to determine their short-term and long-term effects on market performance. Methods: Using time-series data from 2005 to 2024, this study applies the Auto-Regressive Distributed Lag (ARDL) model to examine both short-term and long-term equilibrium relationships. The model assesses the impact of macroeconomic indicators on NEPSE while controlling for external economic shocks. Findings: The results indicate that NEPSE exhibits strong momentum, with past values significantly influencing current market trends. Lending interest rates have a significant negative impact, suggesting that higher borrowing costs discourage stock market investment. Liquidity and deposits do not have an immediate effect on NEPSE but show significant lagged effects, implying delayed investor responses. Reserve money initially reduces NEPSE due to inflationary concerns but later contributes to market recovery. The model's high R-squared value and significant F-statistic confirm its predictive strength. However, external shocks, political instability, and global economic fluctuations remain unmodeled. Conclusion: The study highlights the complex interactions between macroeconomic variables and stock market performance, emphasizing the role of monetary policy in shaping investor behavior. The findings provide valuable insights for policymakers in designing strategies to stabilize and promote stock market growth. Novelty: This study contributes to the literature by utilizing the ARDL model to capture both short-term and long-term dynamics in NEPSE, offering a comprehensive analysis of market behavior. It also suggests future research directions, including incorporating foreign exchange rates and investor psychology to improve predictive accuracy.

Державне регулювання енергетичного сектору з урахуванням його потреб в інвестиціях

Introduction. State regulation of the energy sector is a key factor in ensuring its effective functioning. The main focus of state regulation should be to increase energy production, ensure fair pricing of energy resources in the domestic market, and reduce costs per unit of converted, distributed, and used energy. Problem Statement. Under martial law, a difficult situation in the energy system of Ukraine and the lack of its own resources for the restoration of damage by energy companies, the issue of attracting investment resources from both national and international investors is gaining special importance. One of the key factors that affects not only the return on investment, but also the willingness of investors to invest is the state regulation of the energy market, which ensures the protection of the interests of investors. Purpose. To research into the issues of state regulation and development of the energy sector, taking into account its investment needs. Methods. In the research process, a comprehensive approach was used, including the comparison method, content analysis, statistical method, tabular method, and theoretical generalization method. Results. Based on the analysis of the cause-and-effect relationships between macroeconomic indicators of the energy sector, several main factors were identified that affect both the direct effect of investments, which consists of budget revenues from the energy sector, and the indirect effect, which manifests itself through the impact of energy on other industries and sectors of the country’s economy. These factors include the dynamics of energy capacity development; domestic energy needs; demand and prices on the European energy market; volume of energy generation; level of generation costs; sales volumes on the domestic and European markets; level of energy revenues of the Ukrainian budget. Conclusions. The following problems of state regulation of the energy sector have been identified, which negatively affect its development and the level of investment attraction: increasing costs for the transformation, distribution and use of energy resources; the presence of cross-subsidization of the population; an opaque mechanism for forming electricity prices; growing tax pressure on energy enterprises; the existing state monopoly on nuclear generation and hydro generation, etc.

Deep Statistical Fusion of LSTM and ARIMA for ESG Based Financial Risk and Volatility Forecasting

In the era of sustainable finance, Environmental, Social, and Governance (ESG) factors have emerged as key indicators influencing market behavior and investor sentiment. This study presents a novel hybrid framework that integrates Long Short-Term Memory (LSTM) networks and Auto-Regressive Integrated Moving Average (ARIMA) models to forecast financial market volatility and risk while incorporating ESG signals. The proposed deep statistical fusion model leverages the strengths of ARIMA in capturing linear temporal dependencies and LSTM’s ability to model complex nonlinear patterns from sequential data. ESG scores, along with historical price movements and macroeconomic indicators, are used as primary inputs to enhance model sensitivity to sustainability-related risk. Experiments were conducted using real-world datasets from global stock indices (e.g., NSE, S&P 500) and third-party ESG rating providers. The model's performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and volatility clustering evaluation. The hybrid LSTM-ARIMA model achieved an RMSE of 1.92 and MAPE of 3.85%, outperforming standalone ARIMA (RMSE: 3.14, MAPE: 6.42%) and LSTM (RMSE: 2.41, MAPE: 5.12%). Additionally, the proposed model demonstrated better risk sensitivity by accurately flagging high-volatility periods linked to ESG controversies and macroeconomic disruptions. These results confirm that incorporating ESG factors within a deep statistical fusion framework enhances forecasting precision, offering a robust tool for financial institutions and ESG-conscious investors in proactive risk management and strategic decisionmaking.