Nicole Horta

Research background: The global interdependence of financial markets due to globalization has resulted in standardized trading conditions for agricultural commodities, reducing the advantages of portfolio diversification. Recent events between 2020 and 2023 underscore the growing importance of real-time information for investors to make informed decisions in this interconnected financial landscape. Purpose of the article: This article aims to conduct a bibliometric review of the literature about market interdependence. We investigate the contagion effect on agricultural commodities and identify commodities and methods used in the most cited publications from 1997 to 2022. Methods: A bibliometric analysis was developed, for this, the SCOPUS database was used, sorting with Rayyan, Excel, and finally, the Bibliometrix/R-project to extract bibliometric information from the database. Findings & value added: The analysis highlights the prominent role of certain countries in contributing to scientific research on this topic, with China and the United States being leaders, collectively producing 24.57% of all publications in the examined periods. The research underscores the global concern for sustainable development, emphasizing the scientific growth linked to this topic and its intersection with energy sources. Unlike other bibliometric studies, this research consolidates relevant methodologies employed in analyzing interdependence and contagion effects in agricultural commodities over the past decades. Additionally, it identifies the most studied commodities in these works. As the world grapples with the challenges of market interdependence, particularly in the wake of recent events between 2020 and 2023, this study underscores the importance of real-time information for informed decision-making. The study suggests a shift towards cleaner and renewable energy sources in the coming years, anticipating increased investments in research and development.

Climate change, the scarcity of fossil fuels, advances in clean energy, and volatility of crude oil prices have led to the recognition of clean energy as a viable alternative to dirty energy. This paper investigates the multifractal scaling behavior and efficiency of green finance markets, as well as traditional markets such as gold, crude oil, and natural gas between 1 January 2018, and 9 March 2023. To test the serial dependency (autocorrelation) and the efficient market hypothesis, in its weak form, we employed the Lo and Mackinlay test and the DFA method. The empirical findings showed that returns data series exhibit signs of (in)efficiency. Additionally, there is a negative autocorrelation among the crude oil market, the Clean Energy Fuels Index, the Global Clean Energy Index, the gold market, and the natural gas market. Arbitration strategies can be used to obtain abnormal returns, but caution should be exercised as prices may increase above their actual market value and reduce the profitability of trading. This work contributes to the body of knowledge on sustainable finance by teaching investors how to use predictive strategies on the future values of their investments.

Climate change, the scarcity of fossil fuels, technical advances in clean energy, and the volatility of crude oil prices are just a few of the factors that have prompted the world to recognize clean energy as a viable alternative to dirty energy. As part of the Paris Climate Accord of 2015, many countries agreed to change their economies to be more sensitive to climate change. Due to this Accord, which increased interest from investors and decision-makers, investments in clean energy companies have benefited [1,2]. Clean energy stocks, which are a part of the larger world of tradable reserves, might experience pricing inefficiencies. In this paper, we investigate the multifractal scaling behavior and efficiency of green finance markets, as well as traditional markets like gold, crude oil, and natural gas between January 1, 2018, and March 9, 2023, which covers periods of low volatility and financial instability (2020 and 2022 events). To test the serial dependency (autocorrelation) and the efficient market hypothesis, in its weak form, we employed the Lo and Mackinlay test and the DFA method. The empirical findings demonstrated that both periods exhibit severe multifractal and significant asymmetry, indicating that the price indices under study are not at all efficient.

This research aims to provide evidence for investors and regula­tors of the MENA stock markets, including Bahrain (BASI), Egypt (EGX 30), Abu Dhabi (FTSE ADX), Pakistan (KSE 100), Morocco (MASI), Oman (MSM 30), Qatar (QSE), Saudi Arabia (TADAWUL ALL), and Tunisia (TUNNIDEX), from March 1, 2018, to February 23, 2023. Because variance ratios are less than one, the results show that indexes do not follow the random walk hypoth­esis (RWH), suggesting autocorrelation in returns over time and average re­versal in all indexes. These findings refute both the RWH and the financial market information efficiency hypothesis. According to the study, market regulators should take initiatives to improve information in these regional markets.

The objective of this paper is to explore the factors that stimulate trade among EU countries and pinpoint areas that require..

The study of the changes between the Energy Fuels Index, S&P Global Clean Energy Index, iShares Global Clean Energy ETF, iShares ...

The study of the changes between the Energy Fuels Index, S&P Global Clean Energy Index, iShares Global Clean Energy ETF, iShares...

The purpose of this study is to examine the synchronism between the US capital markets (DJ, S&P 500), the United Kingdom (FTSE 100), Canada (S&P/TSX), Germany (DAX 30), France (CAC 40), Japan (Nikkei 225), Italy (Italy Ds Market and major cryptocurrencies such as Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and the Crypto 10 index, from February 2018 to November 2021. Based on the findings, we found that BTC and ETH cryptocurrencies drastically reduced their level of integration with their peers over the 2020 worldwide pandemic era, whereas LTC maintained. We also discovered that the Dow Jones, S&P 500, and DAX 30 stock indexes lowered their level of integration when compared to the pre-covid subperiod. For the UK capital market (FTSE 100), Canada (S&P/TSX), Japan (Nikkei 225), France (CAC 40), and Italy (Italy Ds Market) the level of integration increased significantly. These findings support, in part, our research question, that during periods of stress and uncertainty in the global economy capital markets tend towards integration, thus calling into question the hypothesis of efficient portfolio diversification.

The objective of this article is to analyze the co-movements in the G7 stock markets, such as DJ index, S&P500 (representing the USA stock market), FTSE 100 (United Kingdom), S&P/TSX (Canada), DAX 30 (Germany), CAC 40 (France), Nikkei 225 (Japan), Italy Ds market (Italy) and the cryptocurrencies Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH) and Crypto 10, during the period of February of 2018 to November of 2021. The results show that the cryptocurrencies BTC, ETH, and LTC increase the co-movements between their pairs, while the Crypto 10 index reduces the number of shocks when compared with the sub-period before COVID-19. Regarding the stock markets, DJ index kept the same level of shocks, whereas the Nikkei 225 decreased. For Germany (DAX), EUA (S&P500), Canada (S&P/TSX), United Kingdom (FTSE 100), France (CAC40), and Italy (Italy Ds Market) markets the results show an increase in movements during the global pandemic period. It is then possible to conclude the existence of evidence regarding synchronization and high co-movements, the results put at risk the implementation of efficient portfolio diversification strategies. These conclusions also open space for the market regulators to take steps to ensure better information on the dynamics of the international financial markets.