This work aims to study the time-frequency relationship between the recent COVID-19 pandemic and instabilities in oil price and the stock market, geopolitical risks, and uncertainty in the economic policy in the USA, Europe, and China. The coherence wavelet method and the wavelet-based Granger causality tests are applied to the data (31st December 2019 to 1st August 2020) based on daily COVID-19 observations, oil prices, US-EPU, the US geopolitical risk index, and the US stock price index. The short- and long-term COVID-19 consequences are depicted differently and may initially be viewed as an economic crisis. The results illustrate the reduced industrial productivity, which intensifies with the increase in the pandemic’s severeness (i.e., a 10.57% decrease in the productivity index with a 1% increase in the pandemic severeness). Similarly, indices for oil demand, stock market, GDP growth, and electricity demand decrease significantly with an increase in the pandemic severeness index (i.e., a 1% increase in the pandemic severeness results in a 0.9%, 0.67%, 1.12%, and 0.65% decrease, respectively). However, the oil market shows low co-movement with the stock exchange, exchange rate, and gold markets. Therefore, investors and the government are recommended to invest in the oil market to generate revenue during the sanctions period.

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This study described an empirical link between COVID-19 fear and stock market volatility. Studying COVID-19 fear with stock market volatility is crucial for planning adequate portfolio diversification in international financial markets. The study used AR (1) -GARCH (1,1) to measure stock market volatility associated with the COVID-19 pandemic. Our findings suggest that COVID-19 fear is the ultimate cause driving public attention and stock market volatility. The results demonstrate that stock market performance and GDP growth decreased significantly through average increases during the pandemic. Further, with a 1% increase in COVID-19 cases, the stock return and GDP decreased by 0.8%, 0.56%, respectively. However, GDP growth demonstrated a slight movement with stock exchange. Moreover, public attention to the attitude of buying or selling was highly dependent on the COVID-19 pandemic reported cases index, death index, and global fear index. Consequently, investment in the gold market, rather than in the stock market, is recommended. The study also suggests policy implications for key stakeholders.

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The study estimates the long-run dynamics of a cleaner environment in promoting the gross domestic product of E7 and G7 countries. The recent study intends to estimate the climate change mitigation factor for a cleaner environment with the GDP of E7 countries and G7 countries from 2010 to 2018. For long-run estimation, second-generation panel data techniques including augmented Dickey-Fuller (ADF), Phillip-Peron technique and fully modified ordinary least square (FMOLS) techniques are applied to draw the long-run inference. The results of the study are robust with VECM technique. The outcomes of the study revealed that climate change mitigation indicators significantly affect the GDP of G7 countries than that of E7 countries. The GDP of both E7 and G7 countries is found depleting due to less clean environment. However, green financing techniques helps to clean the environment and reinforce the confidence of policymakers on the elevation of green economic growth in G7 and E7 countries. Furthermore, study results shown that a 1% rise in green financing index improves the environmental quality by 0.375% in G7 countries, while it purifies 0.3920% environment in E7 countries. There is a need to reduce environmental pollution, shift energy generation sources towards alternative, innovative and green sources.The study also provides different policy implications for the stakeholders guiding to actively promote financial hedging for green financing. So that climate change and envoirnmental pollution reduction could be achieved effectively. The novelty of the study lies in study framework.

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This paper investigates the relationship between political influences and earnings manipulations because little has been known about the relationship between both variables using multiple proxies. The authors measure earnings manipulation using models developed by Bhattacharya et al. (2003) and McNichols (2002), for a large sample of 129 listed firms in Pakistan Stock Exchange over the period 2009–2013. This study finds that politically influenced firms are involved in accruals earnings management and lack transparency, implying lower earnings quality. Our findings are consistent with prior studies, which show the positive relationship between political influences and earnings manipulations. However, the authors add contribution by using three proxies of political influences. The findings are useful for regulators to monitor earnings manipulations activities among public listed companies. In addition, the findings add to the growing literature in the field of corporate governance.