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This paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers.

With daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets.

The empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What’s more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to “same fall but different rise” scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors.

The MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What’s more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices.

The crude oil supply chain (COSC) is one of the most complex and largest supply chains in the world. It is easily vulnerable to extreme events. Recently, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (often known as COVID-19) pandemic created a massive imbalance between supply and demand which caused significant price fluctuations. The purpose of this study is to explore the influential factors affecting the international COSC in terms of consumption, production and price. Furthermore, it develops a model to predict the international crude oil price during disease outbreaks using Random Forest (RF) regression.

This study uses both qualitative and quantitative approaches. A qualitative study is conducted using a literature review to explore the influential factors on COSC. All the data are extracted from Web sources. In addition to COVID-19, four other diseases are considered to optimize the accuracy of predictive results. A principal component analysis is deployed to reduce the number of variables. A forecasting model is developed using RF regression.

The findings of the qualitative analysis characterize the factors that influence international COSC. The findings of quantitative analysis emphasize that production and consumption have a higher contribution to the variance of the data set. Also, this study found that the impact caused to crude oil price varies with the region. Most importantly, the model introduced using the RF technique provides a high predictive ability in short horizons such as infectious diseases. This study delivers future directions and insights to researchers and practitioners to expand the study further.

This is one of the few available pieces of research which uses the RF method in the context of crude oil price forecasting. Additionally, this study examines international COSC in the events of emergencies, specifically disease outbreaks using machine learning techniques.

Using a GED-GARCH model to estimate monthly data from January 1990 to February 2022, we test whether gold acts as a hedge or safe haven asset in 10 countries. With a downturn of the stock market, gold can be viewed as a hedge and safe haven asset in the G7 countries. In the case of inflation, gold acts as a hedge and safe haven asset in the United States, United Kingdom, Canada, China, and Indonesia. For currency depreciation, oil price shock, economic policy uncertainty, and US volatility spillover, evidence finds that gold acts as a hedge and safe haven for all countries.

This study examines the information transmission (return and volatility spillovers) among energy commodities (crude oil, natural gas, Brent oil, heating oil, gasoil, gasoline) and Asian stock markets which are net importers of energy (China, India, Indonesia, Malaysia, Korea, Pakistan, Philippines, Taiwan, Thailand).

The information transmission is investigated by employing the spillover index of Diebold and Yilmaz, using daily data for the period January 2000 to May 2021.

A Strong connectedness is documented between the two classes of asset, especially during crisis periods. Our findings reveal that most of the energy markets, except gasoil and natural gas, are net transmitters of information, whereas all the stock markets, excluding Indonesia and Korea, are net recipients.

The findings are helpful for portfolio managers and institutional investors allocating funds to various asset classes in times of crisis.

All data is original.

The purpose of this study is to explore the intricate relationship between oil prices, house prices in the UK and Norway, and the mediating role of gold and stock prices in both the short- and long-term, unraveling these complex linkages by employing an empirical approach.

This study benefits from a comprehensive set of econometric tools, including a multiequation vector autoregressive (VAR) system, Granger causality test, impulse response function, variance decomposition and a single-equation autoregressive distributed lag (ARDL) system. This rigorous approach enables to identify both short- and long-run dynamics to unravel the intricate linkages between Brent oil prices, housing prices, gold prices and stock prices in the UK and Norway over the period from 2005:Q1 to 2022:Q2.

The findings indicate that rising oil prices negatively impact house prices, whereas the positive influence of stock market performance on housing is more pronounced. A two-way causal relationship exists between stock market indices and house prices, whereas a one-way causal relationship exists from crude oil prices to house prices in both countries. The VAR model reveals that past housing prices, stock market indices in each country and Brent oil prices are the primary determinants of current housing prices. The single-equation ARDL results for housing prices demonstrate the existence of a long-run cointegrating relationship between real estate and stock prices. The variance decomposition analysis indicates that oil prices have a more pronounced impact on housing prices compared with stock prices. The findings reveal that shocks in stock markets have a greater influence on housing market prices than those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

This study may have several limitations. First, the model does not include all relevant macroeconomic variables, such as interest rates, unemployment rates and gross domestic product growth. This omission may affect the accuracy of the model’s predictions and lead to inefficiencies in the real estate market. Second, this study does not consider alternative explanations for market inefficiencies, such as behavioral finance factors, information asymmetry or market microstructure effects. Third, the models have limitations in revealing how predictors react to positive and negative shocks. Therefore, the results of this study should be interpreted with caution.

These findings hold significant implications for formulating dynamic policies aimed at stabilizing the housing markets of these two oil-producing nations. The practical implications of this study extend to academics, investors and policymakers, particularly in light of the volatility characterizing both housing and commodity markets. The findings reveal that shocks in stock markets have a more profound impact on housing market prices compared with those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

These findings could also serve as valuable insights for future research endeavors aimed at constructing models that link real estate market dynamics to macroeconomic indicators.

Using a variety of econometric approaches, this paper presents an innovative empirical analysis of the intricate relationship between euro property prices, stock prices, gold prices and oil prices in the UK and Norway from 2005:Q1 to 2022:Q2. Expanding upon the existing literature on housing market price determinants, this study delves into the role of gold and oil prices, considering their impact on industrial production and overall economic growth. This paper provides valuable policy insights for effectively managing the impact of oil price shocks on the housing market.

The purpose of this paper is to examine the effects of several structural shocks in oil prices on the Vietnamese economy and answer three key research questions: Is there a relationship between oil price shocks and macroeconomic indicators in Vietnam? How do different types of oil price impulses affect Vietnamese inflation and economic performance? To what extent do structural shocks in oil prices explain variations in Vietnam’s macroeconomic indicators?

Lower triangular Cholesky decomposition is performed on a short-term impact matrix in a two-block structural vector autoregressive model. The data set is defined monthly, from January 2000 to December 2021. The contributions of structural shocks in oil prices to the domestic variances are analysed using variance decomposition methods. In this study, both forecast error variance decomposition and historical decomposition are used.

The consequences of oil price fluctuations on Vietnamese output and inflation depend on different sources of oil price shocks. In comparison, oil supply shocks have an insignificant effect on both domestic industrial output and consumer price index inflation; however, positive shocks in aggregate and precautionary oil demands increase these domestic indicators substantially and sustainably. An analysis of variance decompositions reveals that supply-side oil shocks have very limited explanatory power for variations in domestic variables. Nevertheless, the contributions of unanticipated demand-side booms to domestic variations in the past and projected forecasts are considerable.

The findings from this research uncover potential risks for Vietnam’s economic prospects if the consequences of oil price shocks are not managed effectively.

Given the lack of economic sensitivity to supply-side oil shocks and the strong response to shifts in oil demands, greater pressure on the domestic economy is likely when Vietnam increases its dependence on oil imports.

In this study, the authors analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.

The study used a mix of wavelet-based approaches, including continuous wavelet transformation and discrete wavelet transformation. The analysis used data from the Geopolitical Risk index (GP{R), Dow Jones Sukuk index (SUKUK), Dow Jones Islamic index (DJII), Dow Jones composite index (DJCI), one of the top crude oil benchmarks which is based on the Europe (BRENT) (oil fields in the North Sea between the Shetland Island and Norway), and Global Gold Price Index (gold) from May 31, 2012, to June 13, 2022.

The results of the study indicate that during the COVID-19 and Russia–Ukraine conflict period geopolitical risk (GPR) was in the leading position, where BRENT confirmed the lagging relationship. On the other hand, during the COVID-19 pandemic period, SUKUK, DJII and DJCI are in the leading position, where GPR confirms the lagging position.

The present study is unique in three respects. First, the authors revisit the influence of GPR on global asset markets such as Islamic stocks, Islamic bonds, conventional stocks, oil and gold. Second, the authors use the wavelet power spectrum and coherence analysis to determine the level of reliance based on time and frequency features. Third, the authors conduct an empirical study that includes recent endogenous shocks generated by health crises such as the COVID-19 epidemic, as well as shocks caused by the geopolitical danger of a war between Russia and Ukraine.

1. We analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.

2. The results of the wavelet-based approach show that Dow Jones composite and Islamic indexes have observed the highest mean return during the study period.

3. GPR and BRENT are estimated to have the highest amount of risk throughout the observation period.

4. Dow Jones Sukuk, Islamic and composite stock show similar trend of volatility during the COVID-19 pandemic period and comparatively gold observes lower variance during the COVID-19 pandemic and Russia–Ukraine conflict.

We analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.

The results of the wavelet-based approach show that Dow Jones composite and Islamic indexes have observed the highest mean return during the study period.

GPR and BRENT are estimated to have the highest amount of risk throughout the observation period.

Dow Jones Sukuk, Islamic and composite stock show similar trend of volatility during the COVID-19 pandemic period and comparatively gold observes lower variance during the COVID-19 pandemic and Russia–Ukraine conflict.

This paper aims to investigate the relationship between oil price shocks and world food prices between 1974 and 2018.

The authors use the SVAR model to disentangle the oil price into supply, aggregate demand and oil-specific demand shocks and apply the detrended cross-correlations analysis to measure the association between oil price shocks and food returns/volatility and analyze contagion effects between oil and food markets.

The results show that the correlations between oil and food prices depend on whether oil prices changes are driven by supply or demand shocks. Particularly, food returns (volatility) are positively (negatively) more dependent on the oil price changes driven by aggregate demand (oil specific demand) shocks. Further analysis dealing with contagion analysis between oil and food markets shows a contagion effect during the food crisis of 2006–2008. Oil-specific demand shocks are the main source of this phenomenon.

This study differentiates itself from the previous literature by simultaneously disentangling oil price into supply, aggregate demand and oil-specific demand-driven shocks and evaluating the cross-correlations between each shock type and food returns/volatility. Specifically, this study has the originality of detecting the main source of contagion effects between oil and food markets over the food crisis of 2006–2008.

The results of this study are important for policymakers and investors. They should account for the oil price fluctuations differently depending on whether the oil price shocks are driven by the demand or supply side. Moreover, they should anticipate an increase (decrease) in food prices due to a positive (negative) oil shock. In addition, special attention should be accorded to the world oil demand. Finally, when a food crisis occurs, markets operators should focus more on the specific oil-demand shocks, as it is the most contributor to possible contagion effects between oil and food markets.

This study differentiates itself from the previous literature by simultaneously disentangling oil price into supply, aggregate demand and oil-specific demand-driven shocks and evaluating the cross-correlations between each shock type and food returns/volatility. Specifically, this study has the originality of detecting the main source of contagion effects between oil and food markets over the food crisis of 2006–2008.

The paper investigates the dynamic relationship between oil prices, the USA dollar exchange rate and the Saudi stock market index.

The authors perform a novel dynamic simulated the autoregressive distributed lag (ARDL) on weekly data from 2010 to 2021.

The authors' work reveals three main results: First, a cointegration relationship exists between oil prices and the Saudi stock market index. Second, the Saudi stock market is strongly affected by fluctuations in oil prices in both the short and long run. Third, the exchange rate of the USA dollar has a slight influence on the movements of the Saudi stock market. The simulations show that the Saudi stock market index has a long-run upward trend after an oil price shock, while the dollar index rises moderately after a similar shock. Moreover, the first months of the COVID-19 pandemic coincided with a significant decline in the Saudi stock market index, particularly the substantial drop in oil prices.

These findings encourage domestic and foreign investors to benefit from an upward trend in oil prices, especially after the opening of the Saudi market to foreign investment. On the other hand, it raises questions about the Saudi economy's dependence on oil as the sole vehicle for output growth. It highlights the urgent need for diversification and productivity growth in the non-oil sector and other renewable natural resources to increase Saudi competitiveness.

The novelty of the research lies in the following. First, the authors apply one of the latest developments in time-series modeling techniques. This dynamic ARDL simulation model provides a worthwhile alternative way to explore dynamic correlations in the short and long run and assess the choc effects. Secondly, the study would enable us to track the impact of the COVID-19 health crisis on the Saudi stock market.

The study measures the total systemic risks and connectedness across commodities, stocks, exchange rates and bond markets in Africa during the Covid-19 pandemic.

The study uses the Diebold-Yilmaz spillover and connectedness measures in a generalized VAR framework. The author calculates the net transmitters or receivers of shocks between two assets and visualizes their strength using a network analysis tool.

The study found low systemic risks across all assets and countries. However, we found higher systemic risks in the forex market than in the stock and bond markets, and in South Africa than in other countries. The dynamic analysis found time-varying connectedness return shocks, which increased during the peak periods of the first and second waves of the pandemic. We found both gold and oil as net receivers of shocks. Overall, over half of all assets were net receivers, and others were net transmitters of return shocks. The network connectedness plot shows high net pairwise connectedness from Morocco to South Africa stock market.

The study has implications for policymakers to develop the capacities of local investors and markets to limit portfolio outflows during a crisis.

Previous studies have analyzed spillovers across asset classes in a single country or a single asset across countries. This paper contributes to the literature on network connectedness across assets and countries.