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This paper aims to analyse the connectivity of 13 stock markets, between 1998 and 2019, with a time-varying proposal, to evaluate evolution of the linkage between these markets over time. To do so, we propose to use a network built based on the correlation coefficients from the Detrended Cross-Correlation Analysis, using a sliding windows approach. Besides allowing for analysis over time, our approach also enables us to verify how the network behaves for different time scales, which enriches the analysis. We use two different properties of networks: global efficiency and average grade, to measure the network’s connectivity over time. We find that the markets under analysis became more connected before the subprime crisis, with this behavior extending even after the Eurozone crisis, showing that during extreme events there is an increase in financial risk, as found in the international literature.

Considering the importance of continuously analysing stock market integration, and based on an earlier study, this paper adopts a sliding windows approach, jointly with the Detrended Cross-Correlation Analysis correlation coefficient, in order to assess the evolution of integration in Central and Eastern European stock markets. With this approach, we are able to analyse stock market integration in a dynamic way. Our results show that the stock markets of the Czech Republic, Hungary, Croatia, Poland and Romania are most integrated, while those of Bosnia, Montenegro, Serbia and Slovakia are less so. Moreover, we found that during crises, levels of integration increased, while the Brexit referendum seems to have had the contrary effect on markets. This is important information for investors, for example, when wanting to build portfolios, but also for authorities, for whom the information about correlations could be important in detecting potential price crashes. [ABSTRACT FROM AUTHOR]

In this research work, we propose to assess the dynamic correlation between different mobility indices, measured on a daily basis, and the new cases of COVID-19 in the different Portuguese districts. The analysis is based on global correlation measures, which capture linear and non-linear relationships in time series, in a robust and dynamic way, in a period without significant changes of non-pharmacological measures. The results show that mobility in retail and recreation, grocery and pharmacy, and public transport shows a higher correlation with new COVID-19 cases than mobility in parks, workplaces or residences. It should also be noted that this relationship is lower in districts with lower population density, which leads to the need for differentiated confinement policies in order to minimize the impacts of a terrible economic and social crisis.

Abstract The relationship between oil prices and stock markets is commonly studied, in order to understand how financial markets are influenced by this important real asset. The evidence of any kind of interdependence is important, because investors could have additional information about the evolution of those kinds of assets. In this paper, we analyse the detrended cross-correlation coefficient between oil price and 20 different stock markets. We split our sample, in order to analyse the behaviour of that correlation before and after the 2008 crisis, allowing us to use the Δ ρ DCCA , searching for a possible increase in the connection between both variables. Based on the existing literature, which used simulated and real time series to define the critical values for Δ ρ DCCA , we study the statistical significance of this variable Our results show some evidence that before the crisis the correlations were low, but increased after the crisis, which could be understood as an increase in the relationship between oil and stock markets. This is an interesting result because it shows that stock markets are now more exposed to oil price fluctuation than before the crisis. Highlights • We analyse the relationship between oil prices and 20 different stock markets. • We use the Δ ρ DCCA to analyse the long-range correlation before and after the 2008 crisis. • Results show that stock markets are now more exposed to oil price fluctuation. [ABSTRACT FROM AUTHOR]

Abstract We present a novel approach in the research field of price–trading in stock markets. We consider the effect of trading volume (related with liquidity) on the dynamic dependence of time series, using data from the Portuguese stock market, with both listed and non-listed firms in the main index. We use detrended fluctuation analysis, with a sliding windows approach, to estimate the dynamic dependence of shares. We found two interesting and related features: firms listed in the main index have results more centred at the 0.5 level; exponents' volatility is less in the second half of the sample, after the most severe period of the Eurodebt crisis. Then, we relate this dependence with trading volume, with detrended cross-correlation analysis and the respective correlation coefficient. We found that for firms listed in the main index, some show evidence of negative correlations while for non-listed firms, most of the shares have the same results. We conclude that the lack of liquidity could be related to increased dependence. An interesting feature found is that some firms have a positive correlationship between the variables, but all those firms work with natural resources in their activities, which could mean that other factors influence their dependence patterns. Highlights • A novel approach is presented to analyse the relationship between price and trading. • We used a sliding windows approach, to evaluate dependence evolution. • We make the correlation between (dynamical) dependence and trading volume. • Results show the importance of lack of liquidity in stock market dependence. [ABSTRACT FROM AUTHOR]

Abstract When considering an investment, agents are interested in obtaining returns but always considering the risk of that investment. Obviously, they intend to manage their risk, preferring less risky assets. Still, there are other factors, such as the case of corporative image and the international position regarding environmentally friendly policies that might influence this selection. Retrieving information for 26 shares of firms producing renewable energy, in this paper we use the DCCA correlation coefficient to see how those shares behave with the main indices. If shares and indices are not correlated, different factors affect them, which could be seen as a possibility of risk diversification. The results show that in the short run correlation exists (in scales under 100 days) but in the long run most shares are uncorrelated with the indices of their countries (in scales greater than 200), meaning that those shares, associated with more sustainable principles, could be used to manage portfolio risks. Highlights • The DCCA correlation coefficient among renewable energy firms and main indices is calculated. • In the short run the shares are related with the main indices. • In the long run, correlations are not statistically significant. • The use of shares of renewable energy firms could be interesting for portfolio diversification. [ABSTRACT FROM AUTHOR]

Football is a sport that moves thousands of people and millions of euros. Since 1983, several clubs entered the stock markets with shares, and now twenty two clubs are listed in the Stoxx Football Index. In this study, we analyse the behaviour of the return rates of such shares, with Detrended Fluctuation Analysis and Detrended Cross-Correlation Analysis (and its correlation coefficient). With Detrended Fluctuation Analysis, we are able to observe that the shares of several clubs are far from the behaviour of a random walk, which is expected by the theory. Using Detrended Cross-Correlation Analysis, we calculate the cross correlations of clubs’ returns with national indexes and then with the Stoxx Football Index. Although almost all of them are positive, they do not seem to be strong. [ABSTRACT FROM AUTHOR]

The objective of this paper is to analyze if the Euro crisis, which started in 2009, changes the cross-correlation pattern of bank shares with the national stock indexes in both in Eurozone and non-Eurozone countries. We study all banks listed in the main stock indexes of European Union countries, applying the detrended cross-correlation coefficient. An increase in the correlation indicates that the banking sector now has a greater influence in the national index, while a decrease in the correlation means the opposite. Our results show that 19 of the 39 banks analyzed in the Eurozone increased their correlation with national indexes, whilst in the non-Eurozone countries this happened with 14 of the 24 studied banks. While some authors argue that the crisis may have been aggravated by the weight of banks in the economy, Eurozone policy makers should pay attention to this feature. [ABSTRACT FROM AUTHOR]

The present crisis in the Euro is one of the most serious crises reported in history. The fact that different countries that adopted the Euro have different conditions to support asymmetric shocks in their economies could explain some of the consequences currently affecting the Eurozone. In this paper we apply detrended cross-correlation analysis and its cross correlation coefficient to evaluate the degree of financial integration of the first set of countries to adopt the common currency. Since time series used in these studies are known to be non-stationary, DCCA is suited to study it. It is the first time this methodology has been applied to study financial integration. We conclude that the degree of financial integration is unequal in several countries using the common currency. The fact that countries like Greece, Ireland or Portugal are the ones facing most problems in verification of the parity used in this paper could help to explain the present instability in the Eurozone. [ABSTRACT FROM AUTHOR]

In this research work, we propose to assess the dynamic correlation between different mobility indices, measured on a daily basis, and the new cases of COVID-19 in the different Portuguese districts. The analysis is based on global correlation measures, which capture linear and non-linear relationships in time series, in a robust and dynamic way, in a period without significant changes of non-pharmacological measures. The results show that mobility in retail and recreation, grocery and pharmacy, and public transport shows a higher correlation with new COVID-19 cases than mobility in parks, workplaces or residences. It should also be noted that this relationship is lower in districts with lower population density, which leads to the need for differentiated confinement policies in order to minimize the impacts of a terrible economic and social crisis. [ABSTRACT FROM AUTHOR]

Oil is one of the most important products in the world, being used for fuel production but also as an input in several industries. After the oil shocks of the 1970s, which caused great turbulence, the interest in the analysis of this particular product grew. The analysis of the comovements between oil and other assets became a hot topic. In this study, we propose an analysis of how oil price correlates with several industry indexes. The detrended cross-correlation analysis coefficient ( ρ DCCA ) is used, with data from 1992 to 2019, and we analyze not only the correlation between oil and several Euro Stoxx indexes during the whole sample, but also how that correlation evolved for the different decades (1990s, 2000s and 2010s). Naturally, oil and gas are the sectors that correlate the most with crude oil, with correlation coefficients reaching levels higher than 0.6 in some cases. However, the results also indicate that all sectors are now more exposed to oil price variations than in the past, with the financial sector as one of the sectors with the greatest increase in correlation. [ABSTRACT FROM AUTHOR]

The study of how financial markets behave continues to be interesting. The existence of more and more data and the development of statistical techniques are some reasons for the increase in research in finance. However, the difficulty in understanding some markets' behavior is a continuous challenge. In this context, a new research area called Econophysics has emerged, which is constantly increasing in size. We propose in this work to use methodologies related to Econophysics to analyze one stock index composed of firms producing clean energy (S&P Global Clean Energy Index) and compare it with the New York Stock Exchange (NYSE) as a stock market benchmark and with the price of crude oil. In a context where environmental issues are on the agenda, this is an important area of research, because it could help investors to make their decisions. Our results show that the clean energy index seems to have higher time serial dependence than the others, and is less exposed to oil price than the NYSE. [ABSTRACT FROM AUTHOR]