Abstract
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.
Highlights
The first exchange in the world was founded in 1602, since various approaches to asset value analysis had been implemented (Tenshin, 2019)
The most common econometric methods applied to the analysis and forecast of financial time series are the vector autoregression (VAR) (Taveeapiradeecharoen et al, 2019) and the global vector autoregression (GVAR) modelling (Pesaran et al, 2009), the vector error correction model (VECM) (Kularatne, 2002), autoregressive moving average (ARMA) (Taylor, 2007), autoregressive integrated moving average (ARIMA) (Alwadi et al, 2011), generalized autoregressive conditional heteroskedasticity (GARCH) (Lin, 2018), autoregressive distributed lag (ARDL) (Shrestha & Chowdhury, 2005)
In order to work with the value of assets in the stock market, it is necessary to bring them to the form of white noise, which is a stationary process with constant mathematical expectation, constant variance, and a zero autocovariance function for all but zero lag
Summary
The first exchange in the world was founded in 1602, since various approaches to asset value analysis had been implemented (Tenshin, 2019). The number of individual investors in Russia doubled in 2020: from 3.6 million people in January to 7.5 million in October (Finam, 2020). This fact brings special relevance to the topic of this study, namely the interaction of the largest financial markets in the world, which are the United States of America, China, Germany, and the Russian Federation. The most common econometric methods applied to the analysis and forecast of financial time series are the vector autoregression (VAR) (Taveeapiradeecharoen et al, 2019) and the global vector autoregression (GVAR) modelling (Pesaran et al, 2009), the vector error correction model (VECM) (Kularatne, 2002), autoregressive moving average (ARMA) (Taylor, 2007), autoregressive integrated moving average (ARIMA) (Alwadi et al, 2011), generalized autoregressive conditional heteroskedasticity (GARCH) (Lin, 2018), autoregressive distributed lag (ARDL) (Shrestha & Chowdhury, 2005)
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