2023. vol. 27. No. 1

The relationship between the economies of various countries and their dependence on the world markets indicate that for econometric analysis of the impact of external shocks on a particular economy, it is necessary to use a model of the global economy. The aim of this paper is to build a global vector autoregression model (GVAR), including Russia as one of the regions, and to obtain the impact of some external economic shocks on Russian macroeconomic indicators. We build a model that includes 41 of the world's major economies, including Russia, and the oil market. The special features of our model are structural shifts in the dynamics of Russian output and the new specification of oil supply and oil demand. Impulse response functions are used to obtain quantitative estimates. In this paper, we analyze the reaction of outputs, oil production volumes and oil prices in response to the output shocks of China and the United States. In response to the negative shock of output in the world's leading economies, outputs in the rest of the world declined for at least the first year after the shock. There was also a significant decline in oil prices and no significant change in oil production volumes in most countries. In addition, as part of the conditional forecast, we estimated the impact of the decline in global demand due to the Covid-19 pandemic on the Russian GDP as 1,3% drop. The rest of the de cline in Russian GDP can be attributed to the internal effects of the pandemic (lockdown). We also obtained a scenario forecast of the dynamics of Russian GDP depending on a decrease in trade and Russian oil price discount, within which the fall in Russian output could reach 3.3% in 2022.

In this article we propose the method of obtaining forecasts in stochastic terms for deterministic models. The proposed method is computationally simpler than the one used in dyna­mic stochastic general equilibrium (DSGE) models. The method is based on the estimation of parameters in the deterministic paradigm and the estimation of the vector of sample means and the covariance matrix for the increments of exogeneous variables on the in-sample period. For every realization of exogeneous variables, the trajectories of endogenous variables is calculated. The methods of mathematical statistics such as moments calculation, construction of confidence intervals, testing various hypotheses can be applied to them. The approach is illustrated on the model of the Russian banking system, which successfully reproduces the wide set of its indicators. Several interesting properties of the obtained stochastic forecasts were found, including the violation of their normality and the nontrivial dynamics of confidence intervals. Several scenarios of key rate and exchange rate resembling their actual dynamics in the beginning of the 2022. Several conclusions on the influence of key rate and exchange rate on the basic indicators of the banking systems are made. In particular, several effects are found which could not be discovered in the purely deterministic modelling paradigm.

The article compares GARСH and HAR models for 1 day ahead forecasting performance of the realized volatility of financial series. As an example, the cryptocurrency with the largest capitalization, Bitcoin, was chosen. Its realized volatility is calculated from intraday (24 hours) data, using the closing values of five-minute trading intervals. The paper proposes a method for calculating realized volatility for the case of gaps in 5-minute intraday data. This makes it possible to achieve comparability of the daily values of the realized volatility of assets with different trading times. All days of the week are almost equally present among the days selected for forecasting. For comparison, a stock market asset was chosen, E-mini S&P 500, a futures contract that is traded 23 hours a day. We use data from 01/01/2018 to 12/29/2021. Since there could be (and were) structural changes in the markets in this interval, the models are evalua­ted in rolling windows 399 days long. For each series 810 GARCH models and 46312 HAR models are compared. The MCS test is used to select the best models (at the significance level of 0,01). It is shown that GARCH models are inferior to HAR models in the accuracy of forecasting both the realized volatility of Bitcoin and the E-mini S&P 500. At the same time, the relative accuracy of the forecast of the realized volatility of Bitcoin is higher than the accuracy of the forecast of the realized volatility of the E-mini S&P 500 futures. The smallest relative errors for Bitcoin and E-mini S&P 500 realized volatility forecasts are 29,51% and 36,12%, respectively.

This work examines the impact of expected and unexpected illiquidity of Russian stockstraded on the Moscow Exchange on their ex ante and simultaneous excess returns. Following quantitative predictions of the market microstructure invariance hypothesis, I calculate the ex­pected ruble costs of executing a bet in the Russian stock market. This estimate is used to compute the invariance-implied low-frequency illiquidity measure for individual stocks. The expec­ted market illiquidity is estimated by a first-order autoregressive model, and the surprise illiquidity is the residuals from this model. We use two weighting methods (equal-weighting and value-wei­ghting) to calculate market returns, market illiquidity, as well as returns on size-based portfolios. According to the empirical analysis over the period from January 2010 to December 2020, the market premium for expected illiquidity in the Russian equity market was insignificant in most specifications, unlike the effect of unexpected market illiquidity. The negative effect of market illiquidity shocks on market returns is insignificant only in the case of using equal-weighted procedure over the period from January 2010 to June 2015. The weak form of the hypothesis that illiquidity effects are stronger for small-cap stocks is confirmed only in the case of using equal-weighting method over the period from July 2015 to December 2020.

This paper analyzes the existence of relationship between credit risk and the geographical diversification of financial institutions, originating from emerging countries. Due to economic unstable situation in the world caused by Covid-19, credit portfolios of banks and MFIs caused negatively which in some situations can lead to default. In the current situation, it became necessary to discover new approaches to credit risk management and new researches to be done. For this purpose, financial indicators of MFIs operating in Armenia were evaluated and Pearson analysis of MFIs data, risks & profitability efficiency calculation was made to take out impact of diversification of MFIs on credit risk reduction. Both international literature and practical data of MFIs operating in Armenia were identified. Another research was made for taking out the number of branches and credit risk correlation. Our findings show that geographic diversification is statistically significant with the expansion of gross loans. In contrast, empirical results suggest that the geographical diversification of MFIs does not have a significant correlation with the size of the credit risk reserve, which means that the representation of MFIs in different regions in the form of branches will not always lead to credit risk reduction, and in some cases may lead to operational risks and additional costs. We adopt cost funding and assets size variables impact assessment evaluation through instrumental variables method. Our results confirm the endogenous nature of those variables with risk level of MFIs.

This study attempts to analyze the influence of technological infrastructure development and the spill-over effect of dynamic growth of technological innovation on the Asian and European economies. Compared to European countries, the economic infrastructure of Asian countries has transformed significantly during the last three decades. Technological progress and higher growth of available engineers and researchers have become pivotal endogenous determinants in the aggregate production function and eventually became the key drivers of economic growth. Compared to European countries, rapid investment in technology import and a higher number of technologically competent available workforce galvanized uplifted and speedy productivity rates, causing positive economic growth in the Asian economy. The interrelation between technological progress and economic growth is summarized and analyzed by using quantitative methods. The paper stu­dies the nexus between technological progress, the availability of engineers and researchers, and economic growth by applying the dynamic Generalised Method of Moments (GMM) method to the available quantitative data (2013–2017) of chosen Asian and European countries. The econometric results show a significant effect of technological progress and innovation on economic growth. The empirical insight is of particular interest to policymakers as it helps to enhance internal and external technology and innovation development policies for sustainable economic growth in Asian and European countries.