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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.

Rising crude oil prices are likely to have an asymmetric and nonlinear negative impact on GDP growth. The purpose of this paper is to ask the following questions: Does the effect of a crude price shock depend on the position of crude price cycle, i.e. is the effect of price shock larger/smaller in periods of already elevated crude price? And, does the effect of crude price shock depend on the position of the economy in the business cycle, i.e. does the crude price shock affect growth differentially in periods of low/high growth?

The authors use a local linear projection (LLP) model to examine the asymmetric impact of crude price on GDP growth in an environment of high crude price. Next, a quantile regression model is used to account for differential impact on growth around high and low growth periods.

Results from the LLP model show that when oil price is above $70, each additional percentage point of increase in oil price results in a 20 basis point (bps) drop in quarterly GDP growth rate on average. The impact is felt between the third and sixth quarters. When oil prices rise above $80, the impact is similar, with a sharper drop in growth (30 bps). The exercise with quantile regression shows that the impact of an increase in crude prices on growth is almost double at lowest quantiles of growth compared with the median.

There is a growing literature that evaluates the impact of oil price in developing economies. However, nonlinearities in crude price-GDP growth dynamics have not received enough attention, especially during phases of elevated crude price or a growth downcycle. The authors believe that accounting for such effects is especially relevant in the present economic scenario of high oil prices because of geopolitical crises and a period of vulnerable growth because of supply chain issues arising out of the pandemic. Using recent data from oil-importing emerging market economies such as India, this paper fills a crucial gap in the literature.

Crude oil, gold and interest rates are some of the key indicators of the health of domestic as well as global economy. The purpose of the study is to find the shock volatility and price volatility effects of gold and crude oil market on interest rates in India.

This study finds the mutual and directional association of the volatility of gold, crude oil and interest rates in India. The bi-variate GARCH models (Diagonal VEC GARCH and BEKK GARCH) are applied on the sample data of gold price, crude oil price and yield (interest rate) gathered from November 30, 2015 to November 16, 2020 (weekly basis) to investigate the volatility association including the volatility spillover effect in the three markets.

The main findings of the study focus on having a long-term conditional correlation between gold and interest rates, but there is no evidence of volatility spillover from gold and crude oil on the interest rates. The findings of the study are of great importance especially to the policymakers, as they state that the fluctuations in prices of gold and crude oil do not adversely impact the interest rates in India. Therefore, the fluctuations in prices of gold and crude may generally impact the economy, but it has nothing to do with interest rate in particular. This implies that domestic and foreign investments in the country will not be affected by gold and crude oil that are largely driven by interest rates in the country.

Gold and crude oil are two very important commodities that have their importance not only for domestic affairs but also for international business. They veritably influence the economy including forex exchange for any nation. In addition to this, the researchers believe the findings will provide insights to policymakers, stakeholders and investors.

Gold and crude oil undoubtedly influence the exchange rates but their impact on the interest rates in an economy is not definite and remains ambiguous owing to the mixed findings of the studies. The lack of studies related to the impact of gold and crude oil on the interest rates, despite them being essentials for the health of any economy is the main motivation of this study. This study is novel as it investigates the volatility impact of crude oil and gold on interest rates and contributes to the existing literature with its findings.

The purpose of this study is to reveal the lead–lag structure between international crude oil price and stock markets.

The methods used for this study are as follows: empirical mode decomposition; shift-window-based Pearson coefficient and thermal causal path method.

The fluctuation characteristic of Chinese stock market before 2010 is very similar to international crude oil prices. After 2010, their fluctuation patterns are significantly different from each other. The two stock markets significantly led international crude oil prices, revealing varying lead–lag orders among stock markets. During 2000 and 2004, the stock markets significantly led international crude oil prices but they are less distinct from the lead–lag orders. After 2004, the effects changed so that the leading effect of Shanghai composite index remains no longer significant, and after 2012, S&P index just significantly lagged behind the international crude oil prices.

China and the US stock markets develop different pattens to handle the crude oil prices fluctuation after finance crisis in 1998.

The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times.

The stock price volatility is partly explained by volatility in crude oil price. The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries.

The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

The purpose of this paper is to re‐examine the structural origins of international crude oil price fluctuation.

The paper establishes a structural vector autoregression model based on the generalized supply and demand analysis of crude oil price fluctuation and performance the structural decomposition of price shocks with impulse response analysis of those factors.

It is found that four kinds of structural shocks derived from the generalized supply and demand analysis are the essential determinants of crude oil prices fluctuation. On one hand, similar to Kilian's results, the supply side shocks – both the exogenous geopolitical ones and other oil supply shocks have little influence. Whereas, the demand side shocks – both the aggregate demand shock and the oil market specific demand shock have prominent effects. On the other hand, with the expanded sample range, it is found that the dynamic characteristic of the impulse response of oil price to demand side factors is not only incompatible with the basic economic theory, but also clashes with Kilian's statement based upon his research. It is conjured that the incompatibility comes from the ignorance of the finer decomposition of demand side factors. To decompose those demand side factors further, the US dollar liquidity was added into the model. The results show that the impact of US dollar liquidity on the fluctuation of oil prices cannot be ignored. The argument that ascribes the soaring international crude oil price to China's economic growth lacks theoretical and empirical evidence.

The paper contributes marginally to the research on the structural origins of international crude oil price fluctuation and sheds light on the possibility of finer decomposition of demand side oil shocks.

The purpose of the study is to investigate the long-run and short-run dynamic relationship between crude oil prices and the movement of Sensex for the period of 2000–2018.

The study uses the augmented Dickey–Fuller test for the presence of unit root, Johansen cointegration test for estimating the cointegration among the variables. Further, in the case of no cointegration found, the study employed the vector autoregression (VAR) model to estimate the long-run relationship and the Granger causality/Wald test for short-run relationship. The study also conducted tests for the prerequisites of the model: serial correlation, heteroskedasticity and normality of data.

The study found that both the variables, crude oil prices and Sensex are integrated of order 1, that is, I (1), and there is no cointegration between them. Further, the results proliferated from the VAR model unfold the marked effect of previous month crude oil prices (lag 1) on the movement of Indian stock market represented by Sensex considered as the benchmark index. Furthermore, VAR–Granger causality/block exogeneity Wald tests results indicated that there is a causal relationship between the crude oil prices and Sensex under the VAR environment. The model does not have any serial correlation and heteroskedasticity indicating toward the unbiased and robust estimates.

The study is conducted till the year 2018, and data for the present period (post-2018) is excluded due to ongoing trade issues between the USA and oil-exporting countries such as Iran. The current COVID-19 outbreak has also put serious issues. Due to limited time and availability of standardized data, researchers have considered Sensex as equity index only, but for more generalized research outcome few other equity indexes could have been taken for study.

The study is completely original in nature and is an extensive study of the relationship between the crude oil price and Indian stock market with reference to causality between the variables.