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Although there is a number of researchers studying innovations in general specifically pointing out the risky nature of financial innovations, and the necessity to study their impact the empiric studies describing the effect of the innovations in financial technologies upon capital markets are scarce. There is no credible evidence leading to an unambiguous conclusion as to the impact of innovation in financial technologies upon the development of capital markets in Central and East European countries (CEECs). Thus, the main scientific problem of this article is the lack of reliable quantitative evidence of the impact of innovations in financial technologies on capital market development in CEECs. According to that, the aim of this article is to assess the impact of the financial technologies innovations upon the capital markets of the CEECs on the basis of the most recent available data (2006-2017). The study methods employed by the authors included an analysis of the relevant scientific literature, comparative dynamic analysis, panel data models (constant, fixed and random effects) and other statistical methods. As a result, the panel model of the impact upon the capital markets in CEECs is developed based on the analysis of the data from 9 Central and Eastern European countries for a period of 12 years (2006-2017). The model suggested by the authors explains almost four fifths of the changes in the capital market expressed in the capitalisation indicator. The panel models of constant, fix and variable effects is an evidence of a negative statistically significant impact of the summary innovation index reflecting the level and the scope of financial technologies upon the development of capital markets in the Central European States when the other selected macroeconomic variables remain in control. The panel model of development of capital markets also showed that a statistically significant positive impact upon the capital market development in CEECs was produced by forecasted GDP growth, inflation and capital investment factors (variables). On the other hand, foreign direct investment, unemployment, interest rate, public debt, government budget deficit do not have a statistically significant impact upon the development of capital markets in the selected CEECs. The model constructed by the author is characterised by fairly high reliability and determination indicators, therefore, may be used for the purpose of a further assessment and projecting of the impact of financial technologies and other actions upon the development of the capital markets. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and pandemic-induced lockdowns and quarantine establishments have inevitably affected individuals, businesses, and governments. At the same time, the spread of the COVID-19 pandemic had a dramatic impact on financial markets all over the world and caused an increased level of uncertainty; the stock markets were no exception either. Most of the studies on the impact of the COVID-19 pandemic on stock markets are based either on the analysis of a relatively short period (the beginning of pandemic) or a longer period, which, in turn, is very heterogeneous in terms of both the information available on the COVID-19 virus and the measures taken to contain the virus and address the consequences of the pandemic. However, it is very important to assess the impact not only at the beginning of the pandemic but also in the subsequent periods and to compare the nature of this impact; the studies of this type are still fragmentary. Therefore, this research aims to investigate the impact of the COVID-19 pandemic on stock markets of two of the most severely affected European countries—Italy and Spain. To reach the aim of the research OLS regression models, heteroscedasticity-corrected models, GARCH (1,1) models, and VAR-based impulse response functions are employed. The results reveal that the stock market reaction to the spread of the COVID-19 pandemic differs depending on the country and period analyzed: OLS regression and heteroscedasticity-corrected models have not revealed the statistically significant impact of the spread of the COVID-19 pandemic, while impulse response functions demonstrated the non-zero primary response of analyzed markets to the COVID-19 shock, and GARCH models (in the case of Spain) confirmed that the COVID-19 pandemic increased the volatility of stock market return. This research contributes to the literature by providing a comprehensive impact assessment both during the whole pre-vaccination period of the pandemic and during different stages of this period. [ABSTRACT FROM AUTHOR]