Research background: Covid-19 has affected the global economy and has had an inevitable impact on capital markets. In the week of February 24?28, 2020, stock markets crashed. The index FTSE 100 decreased 13%, while the indices DJIA and S&P 500 fell 11?12%, the biggest drop since the 2007?2008 financial and economic crisis. It is therefore of interest to test the random walk hypothesis in developed capital markets, European and also non-European, in order to understand the different predictabilities between them. Purpose of the article: The aim is to analyze capital market efficiency, in its weak form, through the stock market indices of Belgium (index BEL 20), France (index CAC 40), Germany (index DAX 30), USA (index DOW JONES), Greece (index FTSE Athex 20), Spain (index IBEX 35), Ireland (index ISEQ), Portugal (index PSI 20) and China (index SSE) for the period from December 2019 to May 2020. Methods: Panel unit root tests of Breitung (2000), Levin et al. (2002) and Hadri (2002) were used to assess the time series stationarity. The test of Clemente et al. (1998) is used to detect structural breaks. The tests for the random walk hypothesis follows the variance ratio methodology proposed by Lo and MacKinlay (1988). Findings & Value added: In general, we found mixed confirmation about the EMH (efficient market hypothesis). Taking into account the conclusions of the rank variance test, the random walk hypothesis was rejected in the case of stock indices: Dow Jones, SSE and PSI 20, partially rejected in the case indices: BEL 20, CAC 40, FTSTE Athex 20 and DEX 30, but accepted for indices: IBEX 35 and ISEQ. The results also show that prices do not fully reflect the information available and that changes in prices are not independent and identically distributed. This situation has consequences for investors, since some returns can be expected, creating opportunities for arbitrage and for abnormal returns, contrary to the assumptions of random walk and information efficiency.

Research background: Covid-19 pandemic had a strong impact on the economy and capital market. In times of crisis, it is important for investors to be able to diversify their investment portfolio in order to mitigate risk. However, the growing trend towards capital market integration may make it ineffective. Research on financial integration, during the Covid-19 period, has started to develop, mainly in major global capital markets. It is, therefore, important to extend this research to other capital markets. The purpose of the article: This contribution aims to analyze financial integration in the stock indexes of the capital markets of Austria (ATX), Slovenia (SBITOP), Hungary (BUDAPEST SE), Lithuania (OMX VILNIUS), Poland (WIG), the Czech Republic (PX PRAGUE), Russia (MOEX) and Serbia (BELEX 15), in the context of the global pandemic (COVID-19). Methods: To measure the unit roots in the time series, we used ADF, PP, and KPSS tests, and Clemente et al. (1998) test to detect structural breaks. To ana-lyse financial integration, we applied the Gregory and Hansen integration test, and to validate the robustness of results, we use the impulse-response function (IRF) methodology, with Monte Carlo simulations, as they provide a dynamic analysis generated from the VAR model estimates. Findings & Value added: The results suggest very significant levels of integration, which decreases the chances of portfolio diversification in the long-term. Evidence shows 47 pairs of integrated stock market indexes (out of 56 possible). The stock indexes ATX, BUDAPESTE SE, BELEX 15 show financial integration with all other indexes. On the contrary, the index of OMX VILNIUS shows only 3 integrations. Results also show that most of the significant structural breaks occurred in March 2020. The analysis of the relationship between markets, in the short term, shows positive/negative co-movements, with statis-tical significance and with a persistence longer than one week.

In response to the COVID-19 pandemic, single-use disposable masks saw a dramatic rise in production. Facial masks that are not properly disposed of will expose the environment to a form of non-biodegradable plastic waste that will take hundreds of years to degrade. Therefore, recycling such waste in an eco-friendly manner is imperative. Fibered or shredded waste masks can be used to make green concrete that is an environmentally friendly solution to dispose the facial masks. This study prepared six classes of concrete samples, three of which contained fibers from masks and three of which contained shredded masks at the ages of seven days and 28 days. The results show that in the seven-day and 28-day samples, mask fiber added to the mixes resulted in increased compressive strength. For seven-day and 28-day samples, the compressive strength increased by 7.2% and 10%, respectively. Despite that, the results of the shredded mask addition to concrete indicate that the increase in shredded mask volume has a minor impact on the compressive strength of the seven-day samples. An increase in shredded mask from 0.75 to 1% increased 28-day compressive strength by 14%. However, the compressive strength of the mask fiber decreased by 8 after 1% volume. According to a thermal analysis of 28-day concrete samples, as the fiber percentage increases, the mass loss percentage increases. The mass loss rate for samples containing fibers is higher than that for samples containing shredded mask pieces. In general, based on the results mentioned above, the use of fiber in concrete in its fiber state enhances its strength properties. As a result, using shredded mask pieces in concrete leads to better curing due to the reduction of residual capillary pore water loss in construction materials.

Green investors have expressed concerns about the environment and sustainability due to the high energy consumption involved in cryptocurrency mining and transactions. This article investigates the safe haven characteristics of clean energy stock indexes in relation to three cryptocurrencies, taking into account their respective levels of “dirty” energy consumption from 16 May 2018 to 15 May 2023. The purpose is to determine whether the eventual increase in correlation resulting from the events of 2020 and 2022 leads to volatility spillovers between clean energy indexes and cryptocurrencies categorized as “dirty” due to their energy-intensive mining and transaction procedures. The level of integration between clean energy stock indexes and cryptocurrencies will be inferred by using Gregory and Hansen’s methodology. Furthermore, to assess the presence of a volatility spillover effect between clean energy stock indexes and “dirty-classified” cryptocurrencies, the t-test of the heteroscedasticity of two samples from Forbes and Rigobon will be employed. The empirical findings show that clean energy stock indexes may offer a viable safe haven for dirty energy cryptocurrencies. However, the precise associations differ depending on the cryptocurrency under examination. The implications of this study’s results are significant for investment strategies, and this knowledge can inform decision-making procedures and facilitate the adoption of sustainable investment practices. Investors and policy makers can gain a deeper understanding of the interplay between investments in renewable energy and the cryptocurrency market.

Given the importance of the financial markets in the global context, data analysis and new statistical approach are always welcome, especially if we are referring to G-20 group (the world's richest countries). As we know, the pandemic outbreak of COVID-19 has affected the global economy, and its impact seems to be inevitable (as it was in 2020). From the perspective of what was raised above, this paper aims to analyze the stock market efficiency in 21 indexes of G-20. We are going to do our analysis with intraday scale (of hour), from May 2019 to May 2020. In order to be successful in this analysis, we applied the DFA and the DCCA methods, to identify or not two points: i) Are G-20 stock market efficient in their weak form? ii) With open/close values, it is possible to identify some type of memory in G-20 group? The answer to these points will be given throughout this paper. For this purpose, the entire analysis will be divided into two different time-scale: Period I, time-scale less than five days and Period II, with time-scale greater than ten days. In the pandemic times of COVID-19, our results show that taking into account the DFA method, for time-scale shorter than 5 days, the stock markets tend to be efficient, whereas for time-scale longer than 10 days, the stock market tend to be inefficient. But, with DCCA method for cross-correlation analysis, the results for open/close indexes show different types of behaviors for each stock market index separately.