http://ej.hse.ru en-us Copyright 2025 Mon, 22 Dec 2025 12:22:00 +0300 https://ej.hse.ru/en/2025-29-4/1112867279.html The dynamics of money and debt are modelled as a time-invariant process of economic homeostasis sustained by financial clearing mechanisms. The macrofinancial oscillator model elucidates the behaviour of the aggregate creditor, who compensates for expected losses arising from the expansion of total borrowing. Aperiodic mass sell-offs of debt instruments, known infinance as the "bond vigilante" hypothesis, represent an empirical manifestation of this process. Such debt market reactions can induce significant stress and substantial costs, particularly in the contemporary phase of global economic financialization.The cyclical dynamics of debt emerge from the interconnected processes of oscillation and rotation within the macrofinancial system. Causal relationships among money, loans, debt, and wealth are unveiled through the solution of the oscillator's ordinary differential equation (ODE) using the Green's function method. Money issuance and its subsequent transformation into new borrowings are modelled via the Dirac delta function. The monetary impulse elicits a fundamental debt market response, enabling the computation of financial system trajectories under diverse monetary and macroprudential conditions. This framework has notably resolved the paradox of "unlimited" money issuance, a phenomenon observed since the 16th century.Numerical simulations of the macrofinancial oscillator have accurately replicated the global debt market's response to the money issuance spurred by the COVID-19 pandemic. Further refinement of the model will facilitate the calculation of the total debt volume that can be reliably redeemed by the money supply, consistent with the coherent value of goods and services. https://ej.hse.ru/en/2025-29-4/1112868177.html Incoming foreign direct investments (FDI) are considered as a crucial driver of economic development, innovation, and infrastructure improvement. Creating favorable conditions and eliminating obstacles to FDI remain top priorities for go- vernments worldwide. Critical FDI determinants incorporate cost competitiveness, market size, institutional quality, and proximity metrics – alongside emerging environmental considerations. In today’s context, climate change and states’ inten- tions to mitigate its adverse effects have the potential to shape the dynamics and composition of FDI streams. The research investigates the impact of environmental regulations on FDI inflow on a global scale. This study constructs databases with a panel structure containing statistics on developed and developing nations during the period of 2000–2020. The empirical investigation employs the dynamic common correlated effects (DCCE) and Driscoll – Kraay fixed effects standard error (D-K FE) approaches, enabling to control for cross-sectional dependency, heteroscedasticity, autocorrelation, and endogeneity problems. The empirical findings indicate that environmental regulation, when considered in isolation, may constrain FDI inflow. On the other hand, in economically advanced settings, regulation can signal institutional quality and market maturity, thereby enhancing investment appeal despite higher compliance costs. This evidence suggests that emerging countries trying toalign foreign investment flows with environmental conservation efforts are at heightened risk. These nations must direct their efforts toward enhancing economic capacity and designing environmental regulations that can further boost it. https://ej.hse.ru/en/2025-29-4/1112881011.html This study investigates the effects of macroprudential policy (MPP) on constraining the expansion of consumer lending in Russia, drawing on quarterly data for 591 banks over the period 2015–2021. The research contributes to the broader discourse on financial stability in emerging economies, where empirical evidence on the effectiveness of macroprudential regulation remains inconclusive. By providing new empirical evidence for the Russian banking sector – an underexplored context in the existing literature – this paper adds to the growing body of research on the effectiveness of macroprudential frameworks in developing financial systems.The core research question addresses the extent to which MPP effectively constrains consumer credit growth in Russia, particularly under shifting macroeconomic and monetary conditions. The empirical approach employs a dynamic panel data model estimated using the Generalized Method of Moments (GMM). A composite macroprudential policy index, constructed on the basis of the database developed by [Kozlovtseva et al., 2020] and extended to include measures adopted after 2019, serves as the key instrument for quantifying the policy stance.The analysis explicitly accounts for bank-level heterogeneity, interactions between macroprudential and monetary policy, and sensitivity to different phases of the business cycle. The results indicate that macroprudential measures exert a statistically significant and economically meaningful dampening effect on consumer lending, with the impact materializing approximately two quarters after policy implementation and persisting for around six months. Moreover, the findings reveal asymmetric effects of policy tightening and easing, and demonstrate that the influence of MPP is more pronounced for banks characterized by lower deposit funding, smaller size, and a relatively modest share of consumer loans in total assets.The study yields both theoretical and policy-relevant insights. Methodologically, it advances the empirical understanding of macroprudential transmission in Russia, extending prior work by domestic scholars and researchers affiliated with the Bank for International Settle ments. From a policy perspective, it offers practical recommendations for the design and coordination of macroprudential oversight, and highlights promising avenues for future research. https://ej.hse.ru/en/2025-29-4/1112909139.html Social Accounting Matrices being an integral part of the System of National Accounts (SNA) are a convenient tool for analyzing resource flows of the national economy and its social processes, as well as for calibrating Computable General Equilibrium models and constructing various multiplier models in macroeconomics. This paper shows a method for constructing Financial Social Accounting Matrices (FSAM) for the Russian Federation for 2012–2021 based on data from the integrated table of national accounts of Rosstat and statistics of the Central Bank of the Russian Federation on financial accounts of the System of National Accounts (SNA). The proposed method, given the existing limitations in the SNA statistics published in the Russian Federation, makes maximum use of the available data without information loss. Based on the constructed FSAM, a 10-year series of FSAM accounting multiplier matrices was calculated under the assumption of exogeneity of accounts for transactions with the Rest of the World. This set of exogenous accounts (selected only for reasons of simplicity of interpretation of results when demonstrating the capabilities of FSAM) allows using the multipliers obtained in this way to assess the effects on transactions of redistribution of value added within the Russian economy from shocks of foreign trade, capital and finance flows of the Russian Federation with the rest of the world. To understand the contribution of various transactions between FSAM accounts, the decomposition of the found multipliers was carried out in order to identify direct, indirect and cross effects according to the methodology of Nobel laureate Sir J.R.N. Stone. The resulting multipliers and their decompositions into the three components mentioned demonstrate their stability throughout the decade 2012–2021, which inspires cautious optimism regarding their predictive power. https://ej.hse.ru/en/2025-29-4/1112910600.html Forecasting mathematical models of macroeconomic indicators that rely on traditional explanatory variables become less effective under structural economic transformations. In this regard, news indices, which reflect current events, changes in economic policy, market sentiment, and other real-time factors influencing economic activity, are attracting increasing interest. Modern natural language processing (NLP) methods and big data analytics technologies have significantly enhanced the ability to extract relevant information from news sources. This paper explores the application of large language models (LLMs) in a retrieval-augmented generation (RAG) system to analyze large volumes of news data while accounting for contextual significance.The proposed approach is compared with traditional text processing models used in the construction of news indices. To evaluate effectiveness, we assess the predictive power of the generated indices within econometric forecasting models for several macroeconomic indicators. https://ej.hse.ru/en/2025-29-4/1112911854.html Forecasting and analyzing the volatility of financial instruments is one of the fundamental tasks in stock market operations. The literature most often employs linear models for predicting market volatility. However, this tool may not be the most suitable for the stated objective, as the market is inherently non-constant, with its volatility exhibiting distinct periods of high and low values. One method that allows for accounting of this instability is the Markov regime-switching model, which permits the market to exist in at least two states: high and low volatility. When combined with regularization techniques that guard against overfitting, the Markov-switching model can demonstrate superior forecasting performance compared to traditional linear models.The present study is dedicated to demonstrating this very fact. We model and forecast stock market volatility using both simulated and real-world data. For real-world examples, data from the Moscow Exchange (MOEX) and the NASDAQ exchange were taken. Simulations demonstrate that the Markov-switching model with the application of LASSO regularization forecasts at least as accurately as the linear model on linear data and significantly outperforms it on nonlinear data. The results on real data reveal that for the Russian stock market, characterized by nonlinear dependencies in the data, a model assuming a linear relationship possesses low predictive power. The Markov-switching model enhances the accuracy of volatility forecasts in the presence of nonlinear data relationships. Conversely, for the NASDAQ exchange, where the data linkages are predominantly linear, the Markov model does not yield substantial advantages over its linear counterpart.