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The purpose of this paper is to empirically evaluate risk spillovers between selected CESEE (Central, Eastern and South-Eastern Europe) stock markets in order to evaluate the possibilities of an international diversification of a portfolio.

The VAR model and the Diebold and Yilmaz (2009, 2012) spillover index are used, with rolling indices estimation over time in order to observe dynamics, which is important for investment strategies. Data are monthly and include selected CESEE stock market indices which were available to the researcher.

The empirical analysis for the period of January 2012–June 2019 indicates that some country risks were the net emitter of shocks in the system (Slovenia and Czech Republic), whereas some were net receivers (Croatia and Ukraine). The results are robust with respect to changing the length of the rolling window analysis, which means that investors could utilize such an approach in a dynamic portfolio selection.

Observing only selected markets due to data (un)availability.

The paper shows how international investors can utilize the aforementioned methodology in order to make a more detailed analysis of the dynamics of stock markets connectedness so that international portfolios can be rebalanced according to the results and investors’ preferences.

This is the first such research which focuses on CESEE countries, since existing research is focused on more developed stock markets. Moreover, the empirical analysis extends to commenting the pairwise net indices over time, which is important for the dynamic portfolio rebalancing over time.

This paper aims to analyze the effects of investors’ sentiment, return and risk series on one to another of selected exchange rates. The empirical analysis consists of a time-varying inter-dependence between the observed variables, with the focus on spillovers between the variables.

Monthly data on the index Sentix, exchange rates EUR–USD, EUR–CHF and EUR–JPY are analyzed from February 2003 to December 2019. The applied methodology consists of vector autoregression models (VAR) with Diebold and Yilmaz (2009, 2011) spillover indices.

The results of the empirical research indicate that using static analysis could result in misleading conclusions, with dynamic analysis indicating that the financial of 2007-2008 and specific negative events increase the spillovers of shock between the observed variables for all three exchange rates. The sources of shocks in the model change over time because of variables changing their positions being net emitters and net receivers of shocks.

The shortfalls of this study include using the monthly data frequency, as this was available for the authors, namely, investors are interested to obtain new information on a weekly and daily basis, not only monthly. However, at the time of writing this research, we could obtain only monthly data.

As the obtained results are in line with previous literature and were found to be robust, there exists the potential to use such analysis in the future when forecasting risk and return series for portfolio management purposes. Thus, a basic comparison was made regarding the investment strategies, which were based on the results from the estimation. It was shown that using information about shock spillovers could result in strategies that can obtain better portfolio value over time compared to basic benchmark strategies.

First, this paper allows for the spillovers of shocks in variables within the VAR models in all directions. Second, a dynamic analysis is included in the study. Third, the mentioned spillover indices are included in the study as well.

This chapter empirically investigates the main drivers of the circular economy (CE) and sustainable development (SD) of European countries. The European Union (EU) legislation imposes equal rules for the members who should be followed to achieve CE and SD. This chapter gives a critical overview of the related literature on this topic. The second part focuses on measuring the efficiency of EU countries in achieving CE and SD via a nonparametric approach. Furthermore, the results from the efficiency evaluation are used as a dependent variable in determining which economic, social, institutional, and other factors have the greatest influence on CE and SD achievements. The nonparametric approach consists of selected models of data envelopment analysis (DEA), as this is a methodology useful in constructing a ranking system based on selected criteria. The results indicate that on average, the most efficient countries were (besides Malta and Luxembourg) the Netherlands, Poland, Germany, Sweden, Denmark, France, and the United Kingdom. The worst performing ones were Cyprus, Spain, Greece, Belgium, Portugal, and Croatia. The second part of the research indicates that the resource production and corruption perception index has the greatest effect on the efficiency scores, followed by education attainment. The research and development (R&D) variable is not significant in the observed sample. Based on these results, specific policy recommendations are given at the end of this chapter.

This chapter analyses potentials of including online search volume data in modeling the demand series of consumer products. Forecasting future demand for products of a company represents one of the important parts of planning and conducting business in general. Thus, the purpose of this chapter is twofold. The first purpose is to give a critical overview of the existing research on the topic of forecasting and nowcasting demand and consumption. The other purpose is to fill the gap in the literature by empirically comparing several approaches of modeling and forecasting demand and consumption on real data. Results of the empirical analysis show that including online search volume data can enhance modeling and forecasting of demand series, especially in times of economic downturns. Thus, it is advised to use such an approach in modeling of consumer demand in a business so that better business performance in terms of profits could be obtained.