Interactions of money and debt that generate monetary impulses and financial stress were investigated using a model given by a non-homogeneous second-order ordinary differential equation. The financial stress trajectory, which represented the "trace" of monetary impacts on the global economy, was calculated via the behavior of inertia, damping, and recovery factors operating on financial markets. These factors, together with the macrofinancial driver, were following the process of global economic homeostasis, and their mutual balances over the period 2000–2025 were validated by empirical data.In the model financial stress was formed under the influence of a monetary impulse, which was defined by the Dirac’s delta function. The financial market response was evaluated as the convolution of the Green's function with the system’s driver, which reflected the characteristics of monetary and macroprudential policies. The financial stress model being embedded within the hierarchy of interactions between real and financial markets has led to pulsations in the debt-to-GDP ratio even under ideal economic conditions. Over-all, information obtained via analysis of financial reactions to monetary impulses significantly expanded our understanding of money and debt interactions.
This paper examines whether ownership structure moderates the relationship between market-timing incentives and the likelihood of seasoned equity offerings (SEOs). Using a panel of Australian listed firms and logit specifications, market-timing incentives are proxied by market-to-book ratios and pre-issue abnormal returns, while ownership structure is measured by the largest shareholder’s stake and institutional ownership. The results show that the positive association between market-timing proxies and SEO likelihood is weaker when ownership is more concentrated in the hands of the largest shareholder. Institutional ownership, by contrast, does not exhibit a robust moderating effect. These findings suggest that concentrated ownership may reduce firms’ sensitivity to market-timing signals, contrary to the initial expectation that large shareholders have stronger incentives to exploit overvaluation.
This study compares two estimation approaches for the stochastic volatility (SV) model, namely quasi-maximum likelihood (QML) and Markov chain Monte Carlo (MCMC). A simulation exercise shows that MCMC delivers markedly more accurate parameter estimates than QML. However, QML remains substantially faster and far less computationally demanding, which makes it attractive in large-scale empirical applications. To the best of our knowledge, this is the first study to estimate an SV model using Russian stock market data. To fill this gap, we examine securities included in the Moscow Exchange IMOEX index over the period from 1 January 2015 to 1 February 2022. For each stock that was a constituent of the IMOEX as of 1 February 2022 and had at least three years of trading history, SV model parameters are estimated using both QML and MCMC methods. The resulting estimates are used to rank stocks by volatility and to classify them into low-, medium-, and high-volatility groups. Economic sectors are also grouped according to their volatility profiles. The empirical results further indicate that QML-based estimation systematically understates firm-level risk, which may lead to sizable losses in portfolio management. The work may be of interest to specialists in financial econometrics, as well as macroeconomists and hedge fund employees.
This paper presents a methodology for estimating population income distribution and income inequality in Russia based on combining data from a sample survey of household survey and tax data. Tax data are used to adjust income distribution at the top where, since it better reflects incomes of the wealthiest strata than survey. We use data from Rosstat’s Statistical Survey of Population Income (SSIPSP) and tax data aggregated by percentile groups of individual taxpayers. The tax data includes the main components of taxable income: from employment, self-employment and property. Top incomes in SSIPSP are replaced by higher averages from corresponding groups of taxpayers taking into account the region of residence. It allows us to derive more accurate characteristics of income inequality at the national and regional levels.We present income inequality estimates calculated using the proposed methodology, empirical estimates based on SSIPSP data only and official Rosstat’s estimates. The distinctive characteristics of the current official methodology and the proposed approach, that may explain the differences in the estimates, are discussed. The result of adjusting income distribution in the survey is SSIPSP microdata containing adjusted personal and per capita incomes in households. This makes it possible to estimate income inequality and other characteristics of income distribution for different social strata. We demonstrate examples of calculating estimates of income inequality on the regional level based on SSIPSP on incomes before and after the adjustment by the tax data.
The paper continues the research tradition of the public sector of economics from a New Public Management perspective. We examine data on all federal executive bodies in the Russian Federation over approximately 30 years, including 1994, 1998, 2004, 2014–2019, and 2023. We propose an assessing model for the economic effects of bureaucracy. Each agency was correlated with certain field of economic activity, allowing us to link agency with sphere its governing. The relationship between bureaucracy and economic growth assessed through changes in the structure of value added by economic sector. The models considered both agency performance indicators (the number and salaries of civil servants, the number of functions) and control variables influencing the independent variable (the number and average salary of employees in economic sectors). Fixed time effects for year, type, subordination, and sector of agencies were also taken into account. The hypothesis that civil servant salaries are positively correlated with the dynamics of gross value added was confirmed. The growth of functions and the number of civil servants is positively correlated with their salaries. In turn, civil servant salaries are positively correlated with wage levels in economy, and the growth of bureaucracy is virtually unaccompanied by displacement of employment from the private sector. The findings clarify our understanding of the economic role of bureaucracy and adapt the hypotheses of international studies to Russian conditions. The results may be of interest to both economic scholars and public administration practitioners.
