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Changes in the oil price directly affect production costs, and subsequently, the general price level of products. With Indonesia observing an inflation targeting policy, this study applies the nonlinear autoregressive distributed lag (NARDL) technique to investigate the effect of oil price fluctuations in Indonesia. The relationship is important for the central bank to gauge the effectiveness of the inflation targeting policy in immunizing the country from oil price fluctuations. Our findings have revealed that there was an asymmetric behavior between oil price and the inflation rate (producer price index), thus questioning the effectiveness of the inflation targeting policy. More specifically, in the long run, an increase in the oil price will tend to lead to an increase in the rate of inflation with a greater deviation, while an oil price reduction will lead to a decrease in the inflation rate with a lower deviation. This suggests that the benefits of an oil price reduction are not passed down to the consumer.

With the rapid development of the Internet and big data technologies, a rich of online data (including news releases) can helpfully facilitate forecasting oil price trends. Accordingly, this study introduces sentiment analysis, a useful big data analysis tool, to understand the relevant information of online news articles and formulate an oil price trend prediction method with sentiment. Three main steps are included in the proposed method, i.e., sentiment analysis, relationship investigation and trend prediction. In sentiment analysis, the sentiment (or tone) is extracted based on a dictionary-based approach to capture the relevant online information concerning oil markets and the driving factors. In relationship investigation, the Granger causality analysis is conducted to explore whether and how the sentiment impacts oil price. In trend prediction, the sentiment is used as an important independent variable, and some popular forecasting models, e.g., logistic regression, support vector machine, decision tree and back propagation neural network, are performed. With crude oil futures prices of the West Texas Intermediate (WTI) and news articles of the Thomson Reuters as studying samples, the empirical results statistically support the powerful predictive power of sentiment for oil price trends and hence the effectiveness of the proposed method.

We observe a positive correlation between an oil price factor and the All Ordinaries Index of the Australian stock market. Furthermore, an asymmetrical effect is observed when the sample is divided into sub-periods. A more pervasive stock market response is observed when the price of oil displays a positive trend. We also study the influence of oil shocks on the stock returns of specific Australian industries. As expected, the energy and material sectors exhibit a positive response to oil disturbances, whereas the financial and industrial sectors show a negative relation to oil shocks. The utility and consumer discretionary sectors exhibit a lower sensitivity to oil shocks.

The aim of this study is to analyze how oil price shocks affect the economic growth of floating exchange rate regimes and fixed exchange rate regimes in oil-exporting countries with a ratio of oil exports to total exports exceeding 70%. Also, this study seeks to determine what monetary and fiscal policies both regimes apply in order to curb business cycles and reduce inflationary and recessionary gaps. The analytical study uses panel data for the period from 1991 to 2019, covering 24 oil-exporting countries, from the World Economic Outlook (WEO) database and World Bank. The econometric model is estimated by applying a panel VECM to examine the short- and long-term interdependencies in the macroeconomic variables. The results demonstrate that when there is a negative shock to the oil price, the exchange rate of the floating exchange rate regimes depreciates, money supply increases, and government spending decreases. In contrast, the exchange rate of the fixed exchange rate regimes fluctuates slightly; the money supply slightly decreases in the near, medium, and long term; and government spending decreases.

The original objective of this paper is to study the impact of the exchange rate on the trade balance for Kazakhstan and Russia. Baseline empirical results indicate that an exchange rate depreciation actually decreases exports and deteriorates the trade balance for these countries; this is the opposite of the traditional theory of the J-curve and Marshall–Lerner condition. The paper then explores why these major oil exporters do not conform to the theory. It argues that it is the oil price that affects both the exchange rate and the trade balance, thereby masking the true impact of the exchange rate on the trade balance. More formally, the paper augments the traditional theory to take into account the behavior of oil prices in the export and trade balance equations. The augmented theory posits that a decrease in the oil price leads to a real exchange rate depreciation and a decline in exports, thereby creating the positive correlation seen in the baseline results. In accordance with the augmented theory, the regression results suffer from omitted variable bias; when the oil price is taken into account, the results again support the theory.

This paper examines the influence of oil prices on stock market time-varying correlation. Five stock market indices from both oil-importing (US, UK and Germany) and oil-exporting economies (Canada and Norway) are considered for the period 1988-2011. The findings from the DCC-GARCH framework suggest that the effects of oil price changes on stock market correlation are not constant over time and they depend on the status of the economy, i.e. whether it is oil-importing or oil-exporting. In addition, utilising the identification of oil price shocks in [1], [2] and [3] it is found that the aggregate demand shocks and precautionary demand shocks tend to exercise a negative effect on stock market correlation, whereas no effects from the supply-side oil price shocks can be reported. These findings have important implications for international portfolio diversifications and risk management.

Oil price plays a significant role across economies around the globe. As such, there has been an increasing and continued interest for investigating associations between stock market and oil prices over the recent decades. This paper briefly reviews the well established association measures to date and evaluates their performance via an application to stock market and oil price data. 10 oil-importing and 9 oil-exporting countries are included with corresponding stock indices for comparisons. The results provide valuable information relating to the possible existence of linear or nonlinear associations and its corresponding effects on the estimates. Initially, an attempt is made to compare association measures based on the the specific subject of stock market and oil prices relationship. This provides a broad and comprehensive view of the potential associations among all groups of variables whilst taking into account the country and time range differences. The findings provide significant contributions towards helping with the selection of the most suitable model for relationship investigation, and data prediction on stock market and oil prices.

The extant literature suggests a significant association between oil prices and unemployment. However, the relevant literature is unclear on how oil prices impact unemployment in oil-importing economies. In this study, we empirically examine the impact of oil prices on unemployment in 29 African oil-importing economies employing the linear and nonlinear panel autoregressive distributed lag (ARDL) techniques. Our findings demonstrate a negative and significant relationship between oil prices and unemployment in the short run. In contrast, we observe a direct but weak association between unemployment and oil prices in the long run. Besides, unemployment reacts negatively and positively, respectively, to positive and negative changes in oil prices in the short run. In the long run, we detect that an increase in oil prices aggravates unemployment significantly, but a fall in oil prices improves unemployment significantly. All our findings prove robust to data of different frequencies (quarterly and yearly) and Brent and West Texas Intermediate (WTI) oil price indices.

This study explores the long- and short-run movement between oil prices and the real exchange rates of two large oil-exporting countries – Canada and Norway. Cointegration and serial correlation common features tests are jointly used to identify the long-term common trend and short-term common cycles. Our test results find that oil prices and the real exchange rates of the Canadian Dollar and the Norwegian Krone have two shared trends and one shared cycle. The trend–cycle decomposition shows a great deal of positive comovement among the trend and cyclical components. The two currencies show economic dynamics very similar to crude oil prices. They do not exhibit any qualitative differences in the trajectory of the trend and cycles when controlling for different crude oil prices. Our results indicate that oil price fluctuations play significant role in explaining the exchange rate movements of oil-exporting countries.

The first objective of this research is to explain and analyze the financial indicators of the Islamic banking sector in the Middle East and North Africa (MENA) countries before and over the COVID-19 pandemic period, and the second objective is to explore the key determinant that might affect Islamic banks performance before and during COVID-19 pandemic period. Orbis Bank Focus database and annual financial reports are used to collect financial information of Islamic banks in MENA countries over two years: 2019 and 2020. Descriptive statistics, t-test, and multiple regression are employed to analyze the financial structure and performance of Islamic banks before and during COVID-19 pandemic period. The results of this study reveal that there is a sharp drop in financial indicators in Islamic banks during the pandemic period, liquidity risk, bank size, managerial efficiency ratio, and oil price shocks are the determinants of Islamic banks profitability before the appearance of COVID-19. The credit risk, bank size, liquidity risk, managerial efficiency, inflation, and oil price shocks are the determinants of Islamic banks profitability during the pandemic period. Finally, there is no significant impact of GDP and capital structure on Islamic banks profitability before and during the COVID-19 pandemic period.

We examine the impact of COVID-19 pandemic on oil prices, CO₂ emissions, and stock market volatility. We demonstrate that although the increasing number of COVID-19 infections caused a decrease in the price of crude oil, the negative response of the oil market is short-lived. However, the response of economic activities as measured by CO₂ emissions persists. Also, we find a stronger impact on equity market volatility than on crude oil prices and CO₂ emissions. Lastly, the share of error variance in CO₂ emissions is stronger than that of the energy and stock markets. Taken together, our findings shed light on the depth of the impact of COVID-19 on the environment, and the energy and financial markets.