The article explores the growth prospects for the Russian stock market capitalization, aiming for an increase from 24.7% in 2025 to 66% of GDP by 2030. The methodological framework includes an extensive literature review on capitalization dynamics and its determinants, a comparative and economicstatistical analysis of stock market and financial sector indicators, and proprietary approaches to estimating and forecasting the long-term equity risk premium and other financial market parameters. By benchmarking target development parameters against actual dynamics observed in 2020—2025, the study provides a quantitative assessment of the required growth rates for capitalization and related metrics for 2026—2030. The findings indicate that for achieving these objectives sustainable demand for equities from both institutional and retail investors is needed. The study argues that a critical condition for capitalization growth is a reduction in the real risk-free rate and the equity risk premium, resulting from macroeconomic policies focused on disinflation and a more balanced interest rate structure. Furthermore, the paper identifies structural constraints within the domestic savings system, including a high concentration of assets among a limited group of investors and the insufficient propensity of institutional investors toward equity allocations. The authors conclude that comprehensive measures are necessary to develop long-term savings mechanisms, enhance the accessibility of investment products for private investors, and foster long-term investment planning based on forecasting of asset returns.

The article examines interregional heterogeneity of inflation in Russia under inflation targeting. It shows that a monetary policy framework focused exclusively on the aggregate consumer price index does not fully capture the quality of price stabilization in a geographically vast and structurally differentiated economy. A common macroeconomic inflationary impulse reaches regional economies with varying intensity because they differ in sectoral composition, competition, infrastructure, tariff environments, logistics costs, and institutional conditions. Using Bank of Russia and Rosstat data for 85 Russian regions, the study analyzes consumer price growth in late 2025 and early 2026. The results show that substantial spatial asymmetry in price dynamics persists even as nationwide inflation slows, with services remaining one of the most persistent sources of price pressure. The article therefore proposes supplementing the existing monetary policy framework with an indicator of interregional inflation heterogeneity that defines permissible limits for territorial differentiation in inflation. The indicator is not an independent monetary policy target but an auxiliary analytical and communication benchmark that distinguishes formal attainment of the inflation target from substantively balanced price stability. Finally, the article develops a monetary policy decision matrix based on nationwide inflation, the nature of the inflation shock, and the degree of interregional differentiation in price dynamics.

Under sanctions, implementing effective economic policy and mitigating associated costs is particularly important. In particular, the interaction between fiscal and monetary authorities is reflected in the concept of the fiscal r-star (fiscal real interest rate) and the fiscal-monetary gap, as described in a study by experts at the International Monetary Fund. The purpose of this study is to adapt this concept to Russian data and analyze the results. Various statistical analysis and economic-mathematical modeling methods were used. The relevance of this research topic stems from the recent trend toward increasing domestic borrowing under the current tight monetary policy. This divergence could negatively impact both the sustainability of public debt and the achievement of the inflation target. In 2024-2025, a significant increase in the long-term real neutral rate (monetary r-star) to 8-9% was observed, leading to the development of a high (over 5%) and prolonged fiscal-monetary gap.

This article examines the network architecture of the national economic system in order to develop and test an integrated approach to measuring regional connectivity. The relevance of the study is driven by the increasing role of domestic interregional linkages under conditions of geo-economic transformation, sanctions pressure, and the need to enhance the resilience of Russia’s spatial economic organization. The methodological framework combines spatial analysis, composite index construction, logarithmic rank-size distribution, cartographic visualization, and a network-based interpretation of regional structure. The empirical basis of the study covers data for 85 constituent entities of the Russian Federation. At the first stage, an index of economic connectivity is calculated by aggregating the production, market, transport-mobility, and network-innovation characteristics of regions. At the second stage, a matrix of interregional distances is constructed for administrative centers of the constituent entities of the Russian Federation. At the third stage, the two dimensions are combined into a composite index that makes it possible to evaluate a region’s position within the country’s spatial economic system. The findings show that the spatial organization of the Russian economy is characterized by a pronounced hierarchical structure and persistent center—periphery polarization. In comparison with the United States and China, the Russian model displays a higher degree of monocentricity, lower infrastructural redundancy, and a trunk-line organization of its eastern segment. The proposed index can be used as a public policy tool for diagnosing spatial disproportions, prioritizing infrastructure and logistics investment, and reducing vulnerability to sanctions. The scholarly contribution of the study lies in integrating the economic and spatial dimensions of connectivity into a single analytical model and in interpreting Russia’s regional structure as a multilayered hierarchical network.

Official estimates of the gross regional product (GRP) in Russia are published annually and with a significant lag, which critically limits the analysis of regional heterogeneity and the effects of macroeconomic policy at business-cycle frequencies. This paper constructs real-time indicators of economic activity for 79 constituent entities of the Russian Federation at a monthly frequency. The indicators are estimated with a Bayesian dynamic factor model that combines low-frequency GRP data with available monthly and quarterly statistics characterizing the production, income and expenditure sides of the regional economies. The resulting indicator (“proxy GRP”) successfully identifies key macroeconomic episodes, including the 2014—2016 commodity-market downturn, the COVID-19 pandemic shock, and the structural transformation of the economy after 2022. The aggregated proxy GRP is highly correlated with the Rosstat index of output of basic industries and the composite index of regional economic activity (REA) proposed by Smirnov et al. (2017). In a pseudo out-of-sample exercise, the forecasts and nowcasts of GRP growth produced by the model are on average 50% more accurate than a naive random walk forecast and 30% more accurate than estimates based on the index of output of basic industries. The resulting estimates can be used for real-time monitoring of regional economies and for analyzing the transmission mechanisms of monetary and fiscal policy.

This paper compares the forecasting performance of advanced machine learning and econometric models: two types of gradient boosting on decision trees (classical and natural), TabNet, one of the most effective neural network architectures for tabular data, a dynamic factor model (DFM), and a Bayesian mixed-frequency vector autoregression (MFBVAR). The paper asks which family of models and which particular models are the most accurate for relatively short time series, which approaches should be developed further, and why machine learning still yields no substantial gain over advanced econometric methods. Accuracy is compared using historical data within a fair experimental setup. Each model is required to forecast non-seasonally adjusted series and to use monthly predictors as they become available rather than quarterly regressors alone, i.e., to forecast in real time. Each model is also required to select the relevant predictors itself rather than rely on a factor set fixed by theory and must therefore be robust to the curse of dimensionality and include a built-in feature-selection mechanism. The data set contains 63 monthly predictors used to forecast key Russian macroeconomic indicators: GDP, household consumption, gross capital formation, and gross fixed capital formation in constant prices. The paper also proposes and tests the method for constructing predictors that use moving averages to capture the dynamics of several months when forecasting quarterly values. Gradient boosting models slightly outperform MFBVAR, whereas TabNet and the dynamic factor model demonstrate relatively weak performance. The proposed predictors yield accurate longer-horizon forecasts for GDP and gross fixed capital formation.

This article examines the trade in Russian securities on Europe’s major stock exchanges. Sustained interest in Russian funds and corporate securities developed outside Russia several decades before the 20th century. These securities attracted both European financial circles and ordinary investors who could afford to commit funds to such instruments. By the early 20th century, Russian securities were routinely listed on the Paris, London, Berlin, and New York exchanges. They could be freely traded alongside domestic instruments. Moreover, Russian public interest in the performance of these securities abroad grew year by year, prompting the domestic press to cover the price movements of Russian securities on foreign exchanges. The Russian press not only documented the entry of these securities into global financial markets but also tracked their price movements.