Search results

This study aims to empirically analyze the market response of energy companies to the Russian-Ukrainian invasion. Additionally, it examines the comparison of market reactions between companies in NATO member countries and non-member countries.

This study utilizes a sample of 1,511 energy sector companies. To achieve the research objectives, two methods are employed. First, an event study is used to analyze the market reaction using Cumulative Abnormal Return (CAR) to the announcement of Russia's invasion of Ukraine on February 24, 2022 (event day) within an event window of (−30, +30). Second, a cross-sectional analysis is conducted to compare the responses of companies in NATO member countries with those in non-member countries.

The findings of this study reveal that energy companies worldwide reacted positively both before and after the announcement of the invasion, with significant reactions observed in companies from the Americas, Europe, and Asia & Pacific regions. However, the Middle East and Africa markets did not show significant reactions. Furthermore, the study indicates that most developed and emerging markets responded positively, likely due to the increase in energy commodity prices during the war. Moreover, the market reaction of companies in NATO member countries was stronger compared to other markets.

This study contributes to the existing literature by being the first to examine the impact of the Russian invasion of Ukraine on the energy sector, while categorizing markets as developed, emerging, and frontier. It also specifically explores the market reaction of energy companies in NATO member countries, providing unique insights into the differential responses within the energy sector.

研究目的: 本研究擬以經驗及觀察為依據, 去分析能源公司對俄羅斯–烏克蘭侵略行為的市場反應。研究亦擬進行關於北約成員國內的能源公司及非成員國內的能源公司的市場反應的比較研究。

研究設計/方法/理念: 研究使用的樣本為1511間能源領域內的公司。研究人員為能達到研究目標, 採用了兩個方法。首先, 他們使用事件研究法進行有關的研究。具體地說, 他們以累積異常報酬率, 來分析在 (−30, +30) 的事件視窗之內, 能源公司對俄羅斯於2022年2月24日 (事發日) 入侵烏克蘭的公告的市場反應。其次, 研究人員以橫向分析法, 就北約成員國內的能源公司及非成員國內的能源公司的反應進行比較研究。

研究結果: 研究結果顯示, 全球的能源公司於侵略行為公告前後均有正面的反應;而反應較為顯著的公司均來自美洲、歐洲和亞洲及太平洋地區。唯中東和非洲市場均沒有顯著的反應。研究結果亦顯示, 大多數已發展市場和新興市場, 均有正面的反應, 這很可能是因為於戰爭期間, 能源商品價格上升所致。再者, 北約成員國內的公司的市場反應較其他市場強烈。

研究的原創性: 本研究率先以已開發市場、新興市場和邊境市場的市場分類, 去探討俄羅斯入侵烏克蘭對能源部門的影響;就此, 本研究對現有文獻作出了貢獻。研究亦特意探索了北約成員國內能源公司及非成員國內的能源公司兩者的市場反應, 這給我們獨特的啟示, 以能了解能源領域內各種不同的反應。

Sustainable development hinges on a crucial shift to renewable energy, which is essential in the fight against global warming and climate change. This study explores the relationships between artificial intelligence (AI), fuel, green stocks, geopolitical risk, and Ethereum energy consumption (ETH) in an era of rapid technological advancement and growing environmental concerns.

This research stands at the forefront of interdisciplinary research and forges a path toward a comprehensive understanding of the intricate dynamics governing green sustainability investments. These objectives have been fulfilled by implementing the innovative quantile time-frequency connectedness approach in conjunction with geopolitical and climate considerations.

Our findings highlight coal market dominance and Ethereum energy consumption as critical short- and long-term market volatility sources. Additionally, geopolitical risks and Ethereum energy consumption significantly contribute to volatility. Long-term factors are the primary drivers of directional volatility spillover, impacting green stocks and energy assets over extended periods. Additionally, SHapley Additive exPlanations (SHAP) findings corroborate the quantile time-frequency connectedness outcomes.

This study highlights the critical importance of transitioning to sustainable energy sources and embracing digital finance in fostering green sustainability investments, illuminating their roles in shaping market dynamics, influencing geopolitics and ensuring the long-term sustainability required to combat climate change effectively.

The study offers practical sustainability implications by informing green investment choices, strengthening risk management strategies, encouraging interdisciplinary cooperation and fostering digital finance innovations to promote sustainable practices.

The implementation of the quantile time-frequency connectedness approach, in line with considering geopolitical and climate factors, marks the originality of this paper. This approach allows for a dynamic analysis of connectedness across different distribution quantiles, providing a deeper understanding of variable interactions under varying market conditions.

The study tests risk-taking and risk-aversion capabilities while distinguishing between business group firms and stand-alone firms and considering oil price volatility. Second, this attempt to study the linkage between risk-taking during market down movements and when the firms have established themselves as product market leaders. Third, this study analyses the “sentiment” state, where it explores the reaction of corporations when the market is in the negative direction, and lastly, it explores the linkage between product market competition and risk-aversion.

This study uses financial information for 1,273 non-financial companies and other required data from various sources. The study employs panel data and utilizes different empirical methodologies, including the generalized method of moments (GMM) estimator, to test the stated hypotheses.

We find that the business group firms have more risk-taking proficiencies compared with the stand-alone firms. Moreover, this study discovers that the corporates avoid taking risks when the market is not performing well. Also, when the market is down and crude prices are high, the management expects high earnings in the future, willingly takes risks and shows that product market leaders do not follow the risk-aversion strategy.

The empirical results indicate that oil price movement can restrict management’s behaviour when choosing a risky investment project. Management should develop a robust policy that follows the group of firms. In the policy, the management should describe the level of risk that may be taken by the firm and implement it when required.

Since we do not find any studies in this context, then there is a major and essential gap in the literature that this study should fill.

This paper aims to conduct a comparative study on the impact of income diversification and the main non-interest components on banks’ financial performance and risk-adjusted profitability in China and Egypt.

This study uses both static and dynamic panel regression analyses on a sample of Egyptian and Chinese banks from 2009 to 2022.

Income diversification yields positive effects on bank profitability in Egypt and China. Trading income consistently exhibits a significant positive influence on bank profitability in both nations. Conversely, fee-based income positively impacts bank profitability in China, whereas in Egypt, this effect is observed under dynamic-based regression models. On the contrary, income diversification does not consistently increase risk-adjusted profitability in both countries, especially Egypt.

To the best of the authors’ knowledge, this is the first study to examine the impact of income diversification on Egyptian bank performance while identifying the most significant non-interest income components. In addition, the comparative analysis conducted in this study reveals the positioning of China, the largest economy among emerging countries, in terms of the degree of income diversification, its impact on bank profitability and the extent to which non-interest income components contribute to bank profitability when compared with Egypt, representing an emerging country characterised by different levels of bank market power, financial infrastructure and expertise. Findings hold significant implications, suggesting that bank managers and policymakers should prioritise diversifying income sources, particularly through fee-based services and trading activities in China, and trading activities in Egypt, to enhance financial profitability.

This paper aims to investigate the multifaced aspects and consequences of the EU Carbon Border Adjustment Mechanism (CBAM) from an Australia-China Relationship perspective.

This paper leverages the SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis to examine both the internal and external factors that affect Australia and China in the context of the CBAM. In addition, we employ the PEST (Political, Economic, Social and Technological) framework to identify effective strategies for Australia-China cooperation following the implementation of the CBAM.

Our analysis reveals numerous mutual interests and opportunities for bilateral collaboration, despite challenges and threats, positioning the CBAM as a potentially significant catalyst for joint initiatives.

This paper proposes 10 potential areas for Australia and China cooperation from the political economic social and technological PEST dimensions.

This paper makes a pioneering attempt to explore potential strategies, both individually, and together, that Australia and China can adopt to manage the impact and consequence of CBAM.

The purpose of this study is to analyze the price discovery and market efficiency of energy futures traded in India. The study also examines the volatility spillover effect between the cash and futures markets of energy commodities.

The study uses crude oil and natural gas spot and futures series traded at Multi Commodity Exchange (MCX), India. To evaluate the objectives, the paper employs the cointegration test, causality check, dynamic ordinary least squares (DOLS) method and Baba, Engle, Kraft and Kroner (BEKK) GARCH Model.

The study supports the long-run association between the selected markets. Unlike natural gas, in the case of crude oil bidirectional, flow of information is observed. The study rejects the unbiasedness and efficient market hypothesis of the energy futures market in India. Further, the study confirms that the selected energy commodities indicate bidirectional shock transmission between their respective cash and futures markets.

The study will assist the commodity market participants in designing their trading strategy. The volatility signal will be used by investors and portfolio managers for risk management and portfolio adjustment. Regulators will be able to anticipate future spillover and can design policies to strengthen the market.

The paper evaluates the three aspects of the energy futures market, namely price discovery, market efficiency and volatility slipover. To the best of the authors’ knowledge, studies on efficacy and shock transmission in the context of the energy futures market in India are rare. Further, the study also contributes by investigating the price discovery process of the energy futures market.

This study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement.

This study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023.

The outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic.

The analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality.

Most of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.

The increasing awareness and adoption of technology, particularly artificial intelligence (AI), reshapes industries and daily life, fostering a proactive approach to risk management and leveraging advanced analytics, which may affect the stock price crash risk (SPCR). The main objective of the current study is to explore how AI adoption influences SPCR.

This study employs an Ordinary Least Squares (OLS) fixed-effect regression model to explore the impact of AI on SPCR in Chinese A-share listed companies from 2010 to 2020. Further, number of robustness analysis (2SLS, PSM and Sys-GMM) and channel analysis are used to validate the findings.

The primary findings emphasize that AI adoption significantly reduces SPCR likelihood. Further, channel analysis indicates that AI adoption enhances internal control quality, contributing to a reduction in firm SPCR. Additionally, the observed relationship is notably more pronounced in non-state-owned enterprises (non-SOEs) compared to state-owned enterprises (SOEs). Similarly, this distinction is heightened in nonforeign enterprises (non-FEs) as opposed to foreign enterprises (FEs). The study finding also supports the notion that financial analysts enhance transparency, reducing the SPCR. Moreover, the study results consistently align across different statistical methodologies, including 2SLS, PSM and Sys-GMM, employed to effectively address endogeneity concerns.

Our study stands out for its distinctive focus on the financial implications of AI adoption, particularly how it influences firm-level SPCR, an area that has been overlooked in previous research. Through the lens of information asymmetry theory, agency theory, and the economic implications of integrating AI into financial markets, our study makes a substantial contribution in mitigating SPCR.

This study underscores the pivotal role of AI adoption in influencing stock markets for enterprises in China. Embracing digital strategies, fostering transparency and prioritizing talent development are key for reaping substantial benefits. The study recommends regulatory bodies and service providers to promote AI adoption in strengthening financial supervision and ensure market stability, emphasizing the importance of investing in technologies and advancing talent development.

We analyze the role of monetary policy shocks on food inflation in Hungary from January 2007 to March 2023, including the period of the COVID-19 crisis and the Russo–Ukrainian war.

We use quantile regression with three different specifications. The structural breaks in the time series and the monetary policy’s lag in response are also taken into account. We use the M0 money supply and the three-month Hungarian National Bank (MNB) deposit rate as monetary measures to check the robustness of our findings.

We find that neither the money supply nor the exchange rate affected food inflation across quantiles. In the case of high food price inflation, reducing short-term government bond yields may be an effective solution. Household final consumption affected food prices in the lower quantiles, and the global food price index similarly affected Hungarian food inflation. The results are robust to different specifications.

This research has limitations as follows: while Hungary’s food prices provide a valuable case study, expanding to more countries is advisable; although quantile regression captures details, its reliability for non-linear relationships is questionable; additionally, considering various global food price indicators can enhance result robustness.

The paper contributes to the longstanding political debate regarding the effectiveness of monetary policy in stabilizing food inflation. The findings emphasize the importance of considering both domestic and global factors in formulating policy responses to food price dynamics.

This paper aims to examine the impact of United States (US) financial sanctions on the international dominance of the US dollar.

The survival analysis technique, which incorporates survival and hazard probabilities to determine the probability of central banks' reserve recalibration, is adopted for analysis.

The result shows that the probability of central banks recalibrating the dollar share of their official reserve currencies would increase by 60% for every ten additional financial sanctions by the United States. This could imply that more sanctions might have unintended consequences on the international reserve currency dominance of the US dollar.

To the best of the authors’ knowledge, this study may be a novel attempt to use survival analysis to examine the impact of financial sanctions on the US dollar’s international reserve currency dominance.