Are ESG Shares a Safe Haven during COVID-19? Evidence from the Arab Region

The world experienced significant changes in its social and economic lives in 2020–21. Major stock markets experienced an immediate decline. This paper attempts to examine the impact of COVID-19 on stock market performance as well as to identify the differences between the responses of ESG stocks and normal stocks to pandemic conditions in the Arab region. Daily time series for three years between March 2019 and March 2021 were collected for the S&P Pan Arab Composite index and S&P/Hawkamah ESG Pan Arab Index. We used a generalized autoregressive conditional heteroscedasticity (GARCH) model to measure market shocks and a non-linear autoregressive distributed lagged (NARDL) regression model to display the relationship between COVID-19 measurements and the performance of stock indexes. The findings suggest that the volatilities of ESG portfolios and conventional ones were equally affected in the pre-COVID period. However, in the post-COVID period, the magnitude of volatility in the ESG stock index was significantly less compared to that of the conventional stock index. The results also revealed that in the ESG market, shock tended to remain for a shorter period. Furthermore, the ESG index was not affected by the number of confirmed cases and deaths. However, evidence of asymmetric long-run cointegration existed between the S&P index and number of cases and deaths. Increases in the numbers of cases and deaths caused a decline in market index, whereas the reverse trends were observed in the retreat of the pandemic.

Efficient or Fractal Market Hypothesis? A Stock Indexes Modelling Using Geometric Brownian Motion and Geometric Fractional Brownian Motion

In this article, we propose a test of the dynamics of stock market indexes typical of the US and EU capital markets in order to determine which of the two fundamental hypotheses, efficient market hypothesis (EMH) or fractal market hypothesis (FMH), best describes market behavior. The article’s major goal is to show how to appropriately model return distributions for financial market indexes, specifically which geometric Brownian motion (GBM) and geometric fractional Brownian motion (GFBM) dynamic equations best define the evolution of the S&P 500 and Stoxx Europe 600 stock indexes. Daily stock index data were acquired from the Thomson Reuters Eikon database during a ten-year period, from January 2011 to December 2020. The main contribution of this work is determining whether these markets are efficient (as defined by the EMH), in which case the appropriate stock indexes dynamic equation is the GBM, or fractal (as described by the FMH), in which case the appropriate stock indexes dynamic equation is the GFBM. In this paper, we consider two methods for calculating the Hurst exponent: the rescaled range method (RS) and the periodogram method (PE). To determine which of the dynamics (GBM, GFBM) is more appropriate, we employed the mean absolute percentage error (MAPE) method. The simulation results demonstrate that the GFBM is better suited for forecasting stock market indexes than the GBM when the analyzed markets display fractality. However, while these findings cannot be generalized, they are verisimilar.

Long Memory Modeling: Evidence From Mediterranean Stock Indexes

We study in this paper the presence of long memory of four Mediterranean stock markets namely Morocco, Turkey, Spain, and France, over the period 2000-2020. The presence of long memory propriety has tested by using the R/S analysis approach. Results show that the four processes have a long memory. furthermore, ARFIMA-FIGARCH, under different distribution assumptions as Normal, Student-t, and Skewed Student- t, was estimated in order to test the feature of long memory in the return and volatility of the stock markets simultaneously. Results show strong evidence of long memory in both returns and volatility for the Moroccan and French stock markets and only in volatility for The Spanish and Turkish ones. The long memory in returns indicates that their behavior is predictable implying the rejection of the efficient market hypothesis. The long memory in volatility shows that risk is an important parameter of the behavior of the future returns in the four stock markets.

Determining the Return Volatility of Major Stock Markets Before and During the COVID-19 Pandemic by Applying the EGARCH Model

With this study, we aim to determine the effect of the Covid-19 pandemic on the return volatility of the DJI, the DAX, the FTSE100 and the CAC40 stock indexes. We take return volatility between 1st January 2019 and 17th July 2020 and split it into two separate periods - before the Covid-19 pandemic outbreak and the first wave of the ‘In-Pandemic’ period. Only the so-called first wave of the pandemic was chosen to avoid the influence of knowledge of possible vaccines and antiviral solutions. Data were analysed by using the exponential GARCH (EGARCH) model. Findings show excessive volatility in the major stock markets with short volatility persistence and the presence of leverage in returns during the first wave of the Covid-19 pandemic outbreak. Moreover, during the pandemic period, positive shocks have been observed to have a greater effect than negative socks on the stock index return volatility.

An Empirical Study on Movement of Stock Market of BRIC Economies- Are they Co-Integrated?

The major objective of this article is to assist the BRICS nations’ foreign investment decisionmaking process, as well as the creation or changes in policies by these nations’ characteristics. The context is crucial for foreign investors considering diversification advantages internationally, as well as policymakers responding to the aforesaid economies’ growth. This study examines the interconnections between the stock indexes of the BRIC economies. The goal of the research is to look at the long-term link between stock market indexes. From January 2010 to December 2020, the researcher utilized the index’s monthly closing price. To get the ADF at the first-order difference, all of the data is utilized in its raw form. The co-integration method is employed to determine the connection between stock indexes. The causal influence on stock market indices is studied using Granger causality. The sample considers countries such as Brazil, Russia, India, and China. The goal of the research is to look at the long-term link between stock market indexes. It is found that Sensex has the highest return among others, followed by SHCOMP, MOEX and BOVESPA. It is also found that the standard deviation of MOEX is high, followed by SENSEX, SHCOMP and BOVESPA. From the causality analysis, it is found Bi-directional relationship between India and China stock market. Whereas in the case of the other two markets, i.e., Brazil and Russia, the relationship with the Indian stock market are neither Uni-directional nor Bi-directional.

Climatic Change and Financial Stability: Natural Disaster Impacts on Global Stock Markets

This paper aims to provide a comprehensive study of the impacts of worldwide climatic change and consequent natural disasters on international stock markets. By means of a suited event study methodology, we investigate the effects of biological, climatological, geophysical, hydrological and metereological disasters occurred in 104 countries across the world on 27 global stock market indexes over the period 8 February 2001 to 31 December 2019. We find diverse stock market responses to natural hazard shocks depending on the type of event under consideration, as well as on the location in which the event has occurred. We discover that climatological and biological calamities are the disaster types which induce the most extreme reactions of international financial markets, followed by geophysical ones. Furthermore, the examined stock indexes are, on average, considerably responsive to shocks occurring in countries belonging to the European continent, which, overall, tend to affect in a negative way their performances. Finally, our empirical investigation sheds light on the diversification opportunities arising from the mitigation of natural catastrophe risks, by providing evidence on the sensitivity of stock indexes to disaster-specific and country-specific natural hazards. A natural disaster risk hedging strategy highlights the diversification opportunities arising from the mitigation of natural catastrophe risks, by providing evidence on the profitability of trading stock indexes hedging for specific natural hazard sources, and particularly climatological and biological ones.JEL codes: G15, G18, G41, Q54

Fractional differentiation and its use in machine learning

This article covers the implementation of fractional (non-integer order) differentiation on real data of four datasets based on stock prices of main international stock indexes: WIG 20, S&P 500, DAX and Nikkei 225. This concept has been proposed by Lopez de Prado [5] to find the most appropriate balance between zero differentiation and fully differentiated time series. The aim is making time series stationary while keeping its memory and predictive power. In addition, this paper compares fractional and classical differentiation in terms of the effectiveness of artificial neural networks. Root mean square error (RMSE) and mean absolute error (MAE) are employed in this comparison. Our investigations have determined the conclusion that fractional differentiation plays an important role and leads to more accurate predictions in case of ANN.

COVID-19 and Islamic Stock Index: Evidence of Market Behavior and Volatility Persistence

The aftermath of the COVID-19 pandemic is not limited to human lives and health sectors. It has also changed social and economic aspects of the world. This study investigated the Islamic stock market’s reaction and changes in volatility before and during this pandemic. The market model of event study methodology was employed to analyze Islamic stock market reactions in nine different markets around the globe. To examine changes in volatility and persistence of risk, the generalized autoregressive conditional heteroscedasticity (GARCH) method was used. Nine Islamic stock indices were selected for this study from the Thomson Reuters data stream. The results suggest that, in the short run, the Islamic Australian stock index and Islamic GCC stock index remained stable for the first 15 days following news of the pandemic. The Islamic stock indexes of Qatar, UAE, ASEAN, MENA, MENASA, and Bahrain were significantly affected by the outbreak in the short-term. On the other hand, the volatility of Islamic stock indices was substantially amplified after the global health crisis was declared by the WHO. Moreover, volatility shocks tended to persist for a longer period after COVID-19.

Do Cryptocurrencies Offer Diversification Benefits for Equity Portfolios?

Cryptocurrencies are becoming an exciting topic for legislative bodies, practitioners, media, and scholars with diverse academic backgrounds. The work identifies diversification benefits when cryptocurrencies are combined with the equity instruments from Visegrad Stock Exchanges. Furthermore, the results of the study explore financial and economic benefits for the investors of combining cryptocurrencies with equity stocks on the mixed portfolio. Three different independent experiments were conducted to observe diversification benefits generated from cryptocurrencies. Results from the two experiments show that cryptocurrencies employ higher portfolio risk and generate higher returns when they are involved with equity stocks portfolios. The first experiment indicates that cryptocurrencies reduce the risk level of the equity portfolios while increase average returns. Providing the equity portfolios with additional equity stocks lower the portfolio risk which is in line with the theoretical paradigms. Results indicate that cryptocurrencies must be seriously considered by the portfolio managers as an essential aspect of the portfolio diversification benefits. Future studies might raise the samples of selected portfolios with stocks from different stock indexes, to identify the problem from a broader perspective.