Valentin Voytenkov’s research while affiliated with National Research University Higher School of Economics and other places

We explore the impact of institutional factors on bilateral migration among the EU, CIS, and the US by applying the gravity model of migration. We employ instrumental variables methodology (IV-PPML, IV-GMM) and a non-linear estimation approach (NLS) to test our assumptions about the spurring effect of institutional indicators on migration. Empirical results demonstrate a significantly positive effect of economic development and legislative system on migration flows in the sample countries. However, we find that government regulation and political stability decrease migration. We also find that Russia attracts CIS migrants due to cultural and institutional reasons, while the EU and the US accommodate migration due to economic reasons. The crucial role of institutional development as a determinant of international migration is often overlooked in contemporary literature. We generate new insight into the contribution of control for corruption and law, governmental regulation, political stability and democracy, and ease of doing business to migration. Based on the results, we provide some policy implications.

The purpose of the research is to empirically assess the impact of geopolitical risks and economic policy uncertainty on economic activity in Russia. We implement business and consumer confidence index, shares of foreign trade with BRICS countries and western economies and economic growth rates to describe the domestic activity. We use monthly data ranged January 2010 till December 2021 for econometric modelling. The empirical strategy exploits vector error correction model to estimate the impact of external shocks on Russian economic activity. The results demonstrate that the enhancement of economic policy uncertainty and geopolitical risks do not decrease the Russian producers’ confidence in the short run. However, the consumer expectations are found rather negative in the short and long term. The foreign trade of Russia with western countries significantly decreased over the investigated period, while trade turnover with BRICS countries enhanced. Meanwhile, the dynamics of trade with BRICS partners responds less to the changes in economic policy. Coincidently, geopolitical risks enhanced the trade flows between Russia and BRICS, which in turn promoted the Russian economic growth. The trade dynamics with European Union and United States shows a negative trend affected by external factors. However, it has minor effect on Russian economy in the long run. In this respect, we find evidence that economic activity in Russia is subject to external shocks. At the same time, the economy of Russian Federation can adopt to economic policy uncertainty and geopolitical risks, therefore their negative influence in the short run smooth in the long-term perspective

Given the importance of stock market synchronization for international portfolio diversification, we estimate the degrees of co-movements among US, Chinese and Russian markets. By applying the TVP-VAR approach, we measure total and bivariate synchronization indices utilizing daily data from 1998 to 2021. Our analysis demonstrates that the total connectedness index (TCI) is 26.15% among the three markets. We find that the US market is the highest volatility contributor, whereas the Russian market is the highest receiver. Since stock market synchronization is exposed to geopolitical risk, at the second stage, we apply the Quantile-on-Quantile framework to measure the response of total and bilateral connectedness indices to geopolitical risk (GPR). The findings affirm our proposition that GPR impedes TCI when it has a bullish state and a higher quantile of GPR. The response of bilateral connectedness is negative towards GPR concerning US–China and US–Russian pairs. However, the degree of connectedness between Russian and Chinese stock markets is less responsive to GPR.

Currently, financial markets are growing rapidly, which increases the necessity to examine the financial sector. Considering the Russian Federation, the amount of private investors has doubled in Russia since the beginning of 2020 (Finam, 2020). It is important to realize how cash flows between the largest stock market indices. The main hypothesis of the research suggests that the U.S., Germany, and China markets result in significant changes in the Russian stock market. The research objective is to determine the degree of the Russian stock market dependence on the markets of developed and developing countries using methods of econometric analysis. Daily data on S&P500, DAX30, Hang Seng, and Moscow Exchange Index from January 1, 2015, to December 31, 2019, were taken. The research method chosen is a cointegration approach, including the construction of vector autoregression and vector error-correction models and the application of Impulse Response Functions. The results of the Granger causality test reveal no significant interconnection between the Dax30 and the Moscow Stock Exchange Index; the S&P500 affects the Moscow Exchange Index, whereas the Russian stock market affects the Chinese one. According to the cointegration analysis, there is a strong positive influence of the American stock market on the Russian stock market, which does not decrease during the researched period. The stock indices of China and Germany show a weak quantitative influence and mixed dynamics for a long time. The results of the research could be used as recommendations for making management decisions by private investors, hedge funds and managers of large companies.

Relevance. The coronavirus pandemic has both negative and less obvious positive effects on the world economy. In order to better understand these processes, it is necessary to examine the sectors that have shown growth against the general decline in production. Such sectors include the Internet and telecommunication services. Research objective. The purpose of this study is to model the impact of the pandemic and foreign companies on the value of shares of Russian tech companies. Data and methods. The study involves daily share price data of such American corporations as Google, PayPal, Netflix, Adobe, and the Russian company Yandex. Moreover, we used the dummy variable Covid-19. The econometric analysis was conducted by using vector autoregression (VAR). The direction of cause-and-effect relationships was investigated with the help of the Granger test, and the effect of single shocks, through impulse response functions (IRF). Results. A stable VAR model was built. The IRF graphs were used to describe the impact of the pandemic and the value of US. companies on Russian companies. Conclusions. The study shows that the 2020 pandemic has proven to be a positive shock for companies in the ICT sector, contributing to increased demand for their services and market capitalization. The pandemic has affected both Russian and foreign companies. The study has also found the influence of the American stock market on share prices in Russia. Russian companies reacted to changes in the American stock market with a lag of up to 10 days.