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This study investigates the impacts of the Russia–Ukraine conflict on the cross-correlation between agricultural commodity prices and crude oil prices.

The authors used MultiFractal Detrended Fluctuation Cross-Correlation Analysis (MF-X-DFA) to explore the correlation behavior before and during conflict. The authors analyzed the price connections between future prices for crude oil and agricultural commodities. Data consists of daily futures price returns for agricultural commodities (Corn, Soybean and Wheat) and Crude Oil (Brent) traded on the Chicago Mercantile Exchange from Aug 3, 2020, to July 29, 2022.

The results suggest that cross-correlation behavior changed after the conflict. The multifractal behavior was observed in the cross correlations. The Russia–Ukraine conflict caused an increase in the series' fractal strength. The study findings showed that the correlations involving the wheat market were higher and anti-persistent behavior was observed.

The study was limited by the number of observations after the Russia–Ukraine conflict.

This study contributes to the literature that investigates the impact of the Russia–Ukraine conflict on the financial market. As this is a recent event, as far as we know, we did not find another study that investigated cross-correlation in agricultural commodities using multifractal analysis.

This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models.

DCC-GARCH and ADCC-GARCH models.

The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks.

Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war.

This study aims to examine the dynamic bidirectional causality between oil price (OIL) and stock market indexes in net oil-exporting (Russia) and net oil-importing (China) countries.

The authors use monthly data for the period starting from October 1995 to October 2021. In this study, the bootstrap rolling-window Granger causality approach introduced by Balcilar et al. (2010) and the probit regression model are performed in order to identify the bidirectional causality.

The results show that the causal periods mainly occur during economic, financial and health crises. For oil-exporting country, the results suggest that any increase (decrease) in the OIL leads to an appreciation (depreciation) in the stock market index. The effect of the stock market on OIL is more relevant for the oil-importing country than that for the oil-exporting one. The COVID-19 consequences are demonstrated in the impact of oil on the Russian stock market. The probit regression shows that the US financial instabilities increase the probability of causality between OIL and stock market indexes in Russia and China.

The dynamic relationship between the variables must be taken into account in investment decisions. As financial instabilities in the USA drive the relationship between oil and stocks, investors should consider geopolitical, economic and financial elements when constructing their portfolios. Shareholders are required to include other assets in their portfolios since oil–stock relationship is highly risky.

This study provides further evidence of the bidirectional oil–stock causal link. Additionally, it examines the impact of financial instabilities on the probability that the OIL and the stock market index cause each other through the Granger effect.

This paper attempts to investigate the impact of the Russia–Ukraine war on the returns and volatility of the United States (US) natural gas futures market.

The study uses secondary data of 996 trading day provided by the US Department of Energy and investing.com websites and applies the event study methodology in addition to the generalized autoregressive conditional heteroscedastic (GARCH) family models.

The findings from the exponential EGARCH (1,1) estimate are the best indication of a significant positive effects of the Ukraine–Russia war on the returns and volatility of the US natural gas futures prices. The cumulative abnormal returns (CARs) of the event study show that the natural gas futures prices reacted negatively but not significantly to the Russian–Ukraine war at the event date window [−1,1] and the [−15, −4] event window. CARs for the longer pre and post-event window display significant positive values and coincides with the standard finance theory for the case of the US natural gas futures over the Russia–Ukraine conflict.

This is the first study to examine the impact of the Russia–Ukraine war on natural gas futures prices in the United States. Thus, it provides indications on the behavior of investors in this market and proposes new empirical evidence that help in investment analyses and decisions.

The conflict in Russian Ukraine is a problem for the world economy because it hinders growth and drives up inflation when it is already high. The trade route between India and Russia is also impacted by the Russia-Ukraine crisis. This study aims to compile the most recent data on how the present global economic crisis is affecting it, with particular emphasis on the Indian economy.

This research develops a mathematical forecasting model to evaluate how the Russia-Ukraine crisis would affect the Indian economy when perturbations are applied to the major transport sectors. Input-output modeling (I-O model) and interval programing (IP) are the two precise methods used in the model. The inoperability I-O model developed by Wassily Leontief examines how disruption in one sector of the economy spreads to the other. To capture data uncertainties, IP has been added to IIM.

This study uses the forecasted inoperability value to analyze how the sectors are interconnected. Economic loss is used to determine the lowest and highest priority sectors due to the Russia-Ukraine crisis on the Indian economy. Furthermore, this study provides a decision-support conclusion for studying the sectors under various scenarios.

In future studies, other sectors could be added to study the Russian-Ukrainian crises’ effects on the Indian economy. Perturbation is only applied to transport sectors and could be applied to other sectors for studying the effects of the crisis. The availability of incomplete data is a significant concern in this study.

Russia-Ukraine conflict is a significant blow to the global economy and affects the global transportation network. This study discusses the application of the IIM-IP model to the Russia-Ukraine conflict. It also forecasts the values to examine how the crisis affected the Indian economy. This study uses a variety of scenarios to create a decision-support conclusion table that aids decision-makers in analyzing the Indian economy’s lowest and most affected sectors as a result of the crisis.

The study investigates the inter-linkages between geopolitical risk (GPR) and food price (FP).

By employing the bootstrap full- and sub-sample rolling-window Granger causality tests.

The empirical results show that there is a time-varying bidirectional causality between GPR and FP. High GPR leads to a rise in FP, suggesting that geopolitical events usually may disrupt supply and demand conditions in food markets, and even trigger global food crises. However, the negative effect of GPR on FP does not support this view in certain periods. This is mainly because GPR is also related to the global economic situation and oil price, which together have impacts on the food market. These results cannot always be supported by the inter-temporal capital asset pricing model, which states that GPR affects FP in a positive manner. Conversely, there is a positive impact of FP on GPR, indicating that the food market is an effective tool that can reflect global geopolitical environment.

In the context of the Russia–Ukraine conflict, these analyses can assist investors and policymakers to understand the sensitivity of FP to GPR. Also, it will provide significant revelations for governments to attach importance to the role of food price information in predicting geopolitical events, thus contributing to a more stable international environment.

This study aims to investigate the interrelationships and elasticities between the production of renewable energy (RE) and three key variables: oil prices, gross domestic product (GDP) and carbon dioxide (CO₂) emissions.

The research uses panel data and time-series analyses for 10 developed and 16 emerging countries for the period 1976–2018, to identify panel and country-specific elasticity of RE production and dynamic causal relationships between these variables. The study uses an autoregressive distributed lag model to determine the long- and short-run dynamics between RE production and the three variables in each country.

Results show a long-run elasticity between RE and GDP, and short-run dynamics between RE and oil prices and CO₂ emissions in the developed countries. Whereas in the emerging countries category, there were long-run relationships between RE and GDP, CO₂ emissions and oil prices.

Results of this study are in fact crucial and can be applied in the drafting of resilience policies to tackle energy vulnerability as well as sustainable growth. The study results will inform and guide governments on the right policies to stimulate RE production in their own countries in the interests of both their national security and sustainable development globally.

This paper attempts to contribute to the literature in at least two ways. First, research on identifying common determining factors, including socioeconomic factors, in both emerging and advanced economies is considerably scarce. Most of the previous research in this field has focused only on the absolute value of RE production in a particular geographical area. Second, many studies have focused on RE consumption. This research differs from them by focusing on the production of RE. Thus, the main contribution of this study is to fill these gaps. The study also presents novel empirical evidence to determine RE production elasticity from 26 countries.

This study aims to examine the impact of the beginning of the Russia–Ukraine war and the Wagner Group’s attempted military coup against Putin’s regime on the European defense sector, consisting of weapons manufacturers.

The authors use the event study methodology to quantify the impact. That is, the authors assume that markets are efficient, and abnormal stock returns around the event dates capture the magnitudes of the impacts of the two events studied on European defense sector companies. The authors use the capital asset pricing model and two different multifactor models to estimate expected stock returns, which serve as the benchmark necessary to obtain abnormal returns.

The start of the war on February 24, 2022, when the Russian forces invaded Ukraine, was followed by high positive abnormal returns of up to 12% in the next few days. The results are particularly strong if multiple factors are used to control for the risk of the defense stocks. Conversely, the authors find a negative impact of the rebellion initiated by the mercenary Wagner Group’s chief, Yevgeny Prigozhin, on June 23, 2023, on the abnormal returns of defense industry stocks on the first trading day after the event.

To the best of the authors’ knowledge, this is the first study of the impact of the Russia–Ukraine war on the defense sector. Furthermore, this is the first study to measure the financial implications of the military coup initiated by the Wagner Group. The findings contribute to a rapidly growing literature on the financial implications of military conflicts around the world.

This study examines the impact of CSR (corporate social responsibility) on stock price volatility of oil and gas firms and, then identifies the moderating role of tax avoidance.

To achieve the study's purposes, 330 observations are extracted from 30 oil and gas firms for the period between 2010 and 2020, and the estimation method of the Generalized Least Squares (GLS) is used. Actually, the CSR is proxied using the ESG (environmental, social, and governance) score, and the stock price volatility is measured by the degree of stock price variations over 12 months, according to the last 52 week's price.

The main findings indicate that CSR negatively impacts the stock price volatility. Nonetheless, this negative relationship is moderated positively by tax avoidance. This result is robust to the variation in the measure of volatility, namely the systematic risk.

This research is helpful for investors to manage their portfolio risk as this article highlights the importance of engaging in sustainable development to reduce financial risk. This study also helps regulators and policymakers, such as environmental agencies and tax authorities, to reassess their control with oil and gas firms and record them according to their CSR practices, because this article emphasizes that it is not fair to pay taxes and engage in CSR practices at the same time.

The impact of CSR on stock price volatility is widely treated for firms. Nevertheless, the mechanisms that may affect this relationship are still seldom discussed. This study attempts to examine the impact of tax avoidance on the CSR–stock price volatility relationship for the oil and gas industry.

India is not geographically close to either Russia or Ukraine. However, India's trade relations with them make it vulnerable to the consequences of the war between these countries. Thus, the present study aims to examine the impact of the Russia–Ukraine war on various sectoral indices of the Indian economy.

Event study methodology has been used in this study for analysis. The date of the war announcement is the event day. The sample studied includes ten sectors of the Indian economy listed on the National Stock Exchange (NSE). Results correspond to the period of −167 days to +20 days of the announcement of the war, i.e. from June 25, 2021, to March 28, 2022.

Almost all the sample sectors earned significantly positive abnormal returns in the post-event period. The metal industry has led this group by showcasing the highest abnormal returns. Though Indian sectors made overall positive returns, the market soon corrected itself and abnormal returns were wiped out.

These results can benefit portfolio managers, analysts, investors and policymakers in hedging risks and selecting suitable investments during increased global uncertainty. The study's conclusions help policymakers establish an institutional and supervisory framework that will make it easier to spot systematic risks and reduce them by putting countercyclical measures in place.

India has no geographical proximity or trade relations with Russia or Ukraine, as strong as any other European country. However, Russia has remained a strong ally to India in the trade of defense equipment. Similar is the case with Ukraine, a significant global partner for India. Thus, the impact of conflict between these two countries has not been limited to Europe only but has also engulfed related economies. Hence, the present study is one of the first attempts to examine the burns sustained by the Indian economy due to this war.