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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.

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This work investigates the possibility of suppressing chaos in a fractional-nonlinear macroeconomic dynamic model. The system generalizes a model recently reported in the literature in which chaos is strongly present. This description involves the inclusion of the public sector deficit and its coupling with other variables. The system is simulated for integer and non-integer orders that produce a complex dynamics. The time histories and the phase diagrams are presented. The main contribution of this work refers to the adoption of the largest Lyapunov exponent (LLE) criteria based on Wolf's algorithm. This approach improves the response of the system, suppressing, at least partially, the strong presence of chaos reported in previous studies. c

The energy markets have recently undergone important transformations (e.g. deregulation, technological progress, renewable energy deployment and changing energy consumer behaviour) and witnessed a variety of crisis periods, affecting the relationships among energy commodities and their interactions with clean energy indices. This has implications for price discovery, asset allocation and risk management, which requires in-depth analysis to uncover and identify which energy indices (or forms of energy) lead others or are the most influential, while accounting for asymmetry and non-linearity characteristics. To uncover the complex structure of the relationship across the returns of seven different energy commodities and two clean energy stock indices, we apply Granger causality and transfer entropy in both static and dynamic approaches. The results from the Granger causality analysis identify the influence of the other energy products on natural gas, whereas the transfer entropy analysis reveals the importance of WTI oil and the influence of clean energy indices. Diesel is the most influenced energy commodity. A rolling windows analysis confirms those findings and shows evidence of a time-variation that reflects the impacts of crisis periods, especially the pandemic, on the dynamics of relationships.

Abstract:In this work, an investigation and analysis are carried out in order to observe the relationship between ethanol spot and futures prices in Brazil. We adopted the Engle and Granger co-integration approach. Also, we consider the information share method proposed by Hasbrouck in order to examine the market efficiency in price discovery and information transmission. Results show that although the futures market is efficient in price discovery and information transmission, the cash market leads the long-run price discovery process. This suggests that the underlying cause of the dominance of the available market over the futures market can be attributed to the market's relative concentration in wholesale ethanol distribution due to the formation of marketing pools by the ethanol mills, as well as the small number of distributors that control a significant portion of the market share.

Bitcoin’s evolution has attracted the attention of investors and researchers looking for a better understanding of the efficiency of cryptocurrency markets, considering their prices and volatility. The purpose of this paper is to contribute to this understanding by studying the degree of persistence of the Bitcoin measured by the Hurst exponent, considering prices from the Brazilian market, and comparing with Bitcoin in USD as a benchmark. We applied Detrended Fluctuation Analysis (DFA), for the period from 9 April 2017 to 30 June 2018, using daily closing prices, with a total of 429 observations. We focused on two prices of Bitcoins resulting from negotiations made by two different Brazilian financial institutions: Foxbit and Mercado. The results indicate that Mercado and Foxbit returns tend to follow Bitcoin dynamics and all of them show persistent behavior, although the persistence in slightly higher for the Brazilian Bitcoin. However, this evidence does not necessarily mean opportunities for abnormal profits, as aspects such as liquidity or transaction costs could be impediments to this occurrence.