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The study aims to investigate the risk transmission from COVID-19 to global agriculture, energy, natural resources/mining and environmentally/socially responsible investments. Additionally, it explores the connectedness of global energy indices with global agriculture, natural resources/mining and environmentally/socially responsible investments. The study develops a new COVID-19-based Global Fear Index (GFI) to achieve the objectives, thus contributing to the prevailing literature.

The data of Global indices are selected from January 2020 to December 2021. The study uses multivariate BEKK-GARCH and TVP-VAR models to explore COVID-19 risk transmission and connectedness between global indices.

Significant shock and volatility transmissions from COVID-19 to all global indices are observed. Results show that global agriculture, natural resource/mining markets and environmentally and socially responsible investments are safe havens during COVID-19. Furthermore, these global investment choices are barely connected with global energy indices.

Portfolio managers and investors should invest in global indices to gauge the risk-adjusted return during the pandemic and upcoming health-related risks. Investors in energy sectors are advised to diversify the risk by adding safe-haven assets to their portfolios.

The findings shed light on the importance of environmentally and socially responsible investments as a separate asset class where ecologically friendly and socially sentimental investors could invest in diversifying the risk of their portfolios.

The paper offers valuable insights to policymakers and investors regarding asset pricing, risk management and financial market stability during pandemic-type emergencies.

This paper investigates potential safe haven assets for Middle East and North Africa (MENA) stock markets during the uncertainty period of the COVID-19 pandemic.

This study applies the dynamic conditional correlation–generalized autoregressive conditionally heteroskedastic (DCC-GARCH) model and the Diebold–Yilmaz spillover index for ten MENA stock markets, three precious metals and Bitcoin for the period 2013–2021.

Empirical results show, on the one hand, that the COVID-19 crisis risk has been transmitted to MENA stock markets through volatility spillover across markets. This has increased the conditional volatility for all markets. On the other hand, findings point out that the dynamic correlation between the precious metals/Bitcoin and stock markets is not stable and switches between low positive and negative values during the period under studies. Extending analysis to portfolio management, results reveal that investors should include precious metals/Bitcoin in their portfolio of stocks in order to reduce the risk of the portfolio. Finally, for the period of COVID-19, the analysis concludes that gold preserves its traditional role as a safe haven for MENA stock markets during the pandemic, while Bitcoin fails to provide this property.

These results have several implications for international investors, risk managers and financial analysts in terms of portfolio diversifications and hedging strategies. Indeed, the exploration of the volatility connectedness between financial, commodity and cryptocurrency markets becomes an essential task for all market participants during the COVID-19 outbreak. Such analysis can help investors and portfolio managers to evaluate the risk of investments in the MENA stock markets during the crisis period and to achieve the optimal diversification strategy and hedging instruments.

The paper interests MENA stock markets that experienced the last decade a substantial development in terms of market capitalization and number of listed firms. To the author’s knowledge, this is the first study that investigates the dynamic correlation between MENA stock markets and four potential safe haven assets, including three precious metals and Bitcoin. In addition, the paper employs two types of models, namely the DCC-GARCH model and the Diebold-Yilmaz spillover index.

This paper aims to examine the direct impacts of marketing resources and marketing activities on several business performance indicators in the banking industry and the indirect effects through customer-based brand equity.

We use a holistic empirical approach based on resource-based view and marketing productivity chain. The main study consists of a secondary analysis using quarterly data of fourteen banks over four years. We analyze the data using fixed-effect panel data regression, namely seemingly unrelated regressions.

We find that customer-based brand equity is one of the most influential factors on business performance. Moreover, the indirect effect through customer-based brand equity should be considered in improving business performance. Marketing-related financial resources positively impact customer-based brand equity and business performance. Regarding marketing activities, pricing strategies affect the bank preferences of customers, which in turn affect the growth of deposit volumes and churn rates. Additionally, the number of bank branches positively impacts business performance. Advertising spending on different media has differentiated impacts on the performance indicators; thus, the allocation of advertising budget and advertising planning are critical.

This study examines the inter-relationships among marketing resources, marketing activities, consumer response through brand equity and marketing performance. This study contributes to the literature by integrating the resource-based view and the marketing productivity chain to analyze the inter-relationships using panel data and several sector-related metrics. This study provides valuable insights to decision-makers in the banking industry.

This paper examines the extent to which exchange rate volatility (ERV) is crucial for small island economies. These economies by their very nature and size tend to be net importers and highly dependent on trade for their economic survival. The island of Mauritius is used as a case study.

A GARCH model has been utilized using yearly data for the period 1993–2022. The ARDL bounds cointegration approach has been used to determine the long run relationship between exchange rate volatility and the performance of exports. The ECM-ARDL model has been used to estimate the short-run relationships, that is the speed of adjustments between the variables under consideration.

The findings reveal that exchange rate volatility has a positive and significant effect on exports in the short run as well as in the long run. The study also finds out that export has a long-term relationship with world GDP per capita. Both the presence and degree of exchange rate volatility are important aspects for consideration in policy making.

The literature gap that this study attempts to close is one related to global impacts within the recent time horizon. Recently, numerous important events shaped the financial and economic landscape globally, including but not limited to the financial crisis of 2008 and the COVID-19 pandemic in 2019. Both these events stressed the global volume of trade and the exchange rate markets, and these events affects small islands comparatively more given their heavy dependence on international trade for economic development, albeit economic survival.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

We examined the dynamic volatility connectedness and diversification strategies among US real estate investment trusts (REITs) and green finance indices.

The DCC-GARCH dynamic connectedness framework and he DCC-GARCH t-copula model were employed in this study.

Using daily data from 2,206 observations spanning from 2 January 2015 to 31 January 2023 this paper presents the following findings: (1) cross-market spillovers exhibited a high correlation and significant fluctuations, particularly during extreme events; (2) our analysis confirmed that REIT acted as net receivers from other green indices, with the S&P North America Large-MidCap Carbon Efficient Index dominating the in-network volatility spillover; (3) this observation suggests asymmetric spillovers between the two markets and (4) a portfolio analysis was conducted using the DCC-GARCH t-copula framework to estimate hedging ratios and portfolio weights for these indices. When REIT and the Dow Jones US Select ESG REIT Index were simultaneously added to a risk-hedged portfolio, our findings indicated that no risk-hedging effect could be achieved. Moreover, the cost and performance of hedging green assets using REIT were found to be comparable.

We first examined the dynamic volatility connectedness and diversification strategies among US REITs and green finance indices. The outcomes of this study carry practical implications for market participants.

This study aims to evaluate the resource curse hypothesis in an oil-dependent economy, Saudi Arabia, through examining the impact of oil price volatility on foreign ownership among Saudi listed firms.

The study analyzes a unique data set of firm-level data on foreign ownership for the period 2009–2015. A multivariate regression model was applied to analyze the relationships under study.

The analysis reveals a negative association between oil price volatility and foreign ownership in firms with high leverage and low stock volatility.

Policymakers are encouraged to develop policies to control shocks in the supply and demand of oil and enforce economic diversification. Investors can better understand the dynamics of an oil-based economy stock market based on the investment behavior of foreign investors and their response to oil price shocks.

This study adds to the literature by analyzing the relationship understudy in an oil-rich and oil-dependent emerging economy, where its critical economic parameters are influenced by oil price volatility and it has the largest and the most liquid stock exchange in the MENA region.

The current research aims to investigate the impact of coronavirus 2019 (COVID-19) evolution, government stringency measures and economic resilience on stock market volatility in the Middle East and North African (MENA) emerging markets. Other macroeconomic factors were also taken into account.

Based on financial data from 10 selected MENA countries, we tested an integrated framework that has not yet been explored in prior research. The exponential generalized autoregressive conditional heteroskedasticity (E-GARCH) was adopted to analyze data from March 2020 to February 2022.

Our research illustrates the direct and indirect effects of the virus outbreak on stock market stability and reports that economic resilience could alleviate the volatility shock. This finding is robust across the various proxies of economic resilience used in this study. We also argue that the negative impact of the pandemic on equity market variation gets more pronounced in countries with higher level of stringency scores.

Policymakers ought to strengthen their economic structures and reinforce the economic governance at the national level to gain existing and potential investors’ trust and ensure lower stock market volatilities in times of crisis. Our study also recommends some key economic factors to consider while establishing efficient policies to tackle unexpected shocks and prevent financial meltdowns.

Our findings add to the evolving literature on the reaction of economic and financial markets to the sanitary crisis, particularly in developing countries where research is still scarce. This study is the first of its kind to investigate the stock market reaction to stringency measures in the understudied MENA region.

This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.

We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.

Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.

There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.

The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.

The current study immerses itself in the realm of diversification prospects within a select group of preeminent global stock exchanges. Specifically, the study casts its discerning gaze upon the financial hubs of the United States, Hong Kong, Germany, France, Amsterdam and India. In this expansive vista of international financial markets, the present analytical study aims to unravel the multifaceted opportunities that lie therein for astute portfolio management and strategic investment decisions.

The study encompasses daily time series data spanning from 2019 to 2022. To assess the interconnectedness among these stock indices, advanced statistical techniques, including Johansen cointegration methods and vector autoregressive (VAR) models, have been applied.

The research outcomes reveal both unidirectional and bidirectional relationships between the Indian, Hong Kong and US stock exchanges, encompassing both short-term and long-term time frames. Interestingly, the empirical findings indicate the presence of diversification opportunities between the Indian stock exchange and the stock exchanges of Germany, France and Amsterdam.

These insights hold significant value for both Indian and international investors, including foreign institutional investors (FIIs), domestic institutional investors (DIIs) and retail investors, as they can utilize this knowledge to construct more effective and diversified investment portfolios by understanding the intricate interconnections between these prominent global stock exchanges.

This research undertaking aspires to bring coherence to a landscape rife with divergent interpretations and methodological divergences. We are poised to offer a comprehensive analysis, a beacon of clarity amidst the murkiness, to shed light on the intricate web of interconnections that underpin the world's stock exchanges. In so doing, we seek to contribute a seminal piece of scholarship that transcends the existing ambiguities and thus empowers the field with a deeper understanding of the multifaceted dynamics governing international stock markets.