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We develop a five-region version (Canada, a group of oil exporting countries, the United States, emerging Asia and Japan plus the euro area) of the Global Economy Model (GEM) encompassing production and trade of crude oil, and use it to study the international transmission mechanism of shocks that drive oil prices. In the presence of real adjustmen...
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Context 1
... imported oil, there is less preference as to the region of origin ( O = 3.0). As in all previous work on the GEM, the calibration of markups follows Martins et al. (1996) for prices in the monopolistically competitive tradable and nontradable sectors , and Jean and Nicoletti (2002) for wages (see Table 8). However, we have assumed almost no markup in the oil sector for any region, with the exception of the group of exporting countries, which has a markup of 476 percent. ...
Citations
... However, because crude oil is a significant source of energy on a global scale, its demand and supply are vulnerable to a variety of national and international events, including pandemics, the global economy, and geopolitical unreliability [3,4]. Understanding crude oil pricing therefore becomes very crucial to the global economy. ...
Crude oil plays a pivotal role in global economics, serving as a crucial raw material for manufacturing and a primary ingredient in transportation gasoline. Accurate forecasting of crude oil prices is essential for various sectors. Conventional statistical and econometric models often struggle with the non-linear and inconsistent nature of crude oil price data, leading to poor prediction performance. In this study, we propose a novel hybrid approach combining Support Vector Regression (SVR), a nonlinear machine learning model, with Autoregressive Integrated Moving Average (ARIMA), a linear econometric model. Genetic Algorithms (GA) was employed to optimize the parameters of both models. Our hybrid models, namely SVRGA_ARIMA and SVRGA_ARIMAGA, outperform individual models such as ARIMA and SVR, as well as the hybrid model SVR_ARIMA, in terms of forecasting accuracy. The proposed hybrid models achieve a significantly lower root mean square error compared to other models. Overall, our findings suggest that the hybrid SVRGA_ARIMA and SVRGA_ARIMAGA models, optimized with GA, offer a reliable framework for forecasting weekly crude oil prices.
... d determines the degree of substitution between oil and the other factors of production. Following Elekdag et al. (2008), the function Γ O,t is introduced to reflect the costs of adjusting oil intensity in the domestic good output. It is assumed to take the following quadratic form: ...
... The steady state Canadian crude oil output in GDP is set equal to 3.6 percent, while the net exports of crude oil in GDP is set equal to 0.1 percent, corresponding to their sample mean. Following Elekdag et al. (2008), the capital and labor income shares in oil output have been calibrated equal to 30 percent and 11 percent respectively. These ratios are set in order to capture the relative capital-intensive technology used in the process of oil extraction in Canada, in particular, for Athabasca oil sands in Alberta or the offshore oil-rigs of Hibernia. ...
... Following Bouakez and Rebei (2008), I set the prior mean for the elasticity of substitution between domestic and imported goods, , equal to 1.5. Consistent with Elekdag et al. (2008), the prior mean for the elasticity of substitution among factors used in oil production, o , and the prior mean of the elasticity of substitution among factors used in domestic good production, d , are set equal to 0.6 and 0.7, respectively. I impose an inverse-gamma distribution with a prior mean of ⋆ o = 0.44 for the price-elasticity of oil demand in the foreign economy. ...
This paper investigates how oil supply shocks, aggregate demand shocks, and speculative oil demand shocks affect Canada’s economy, within an estimated Dynamic Stochastic General Equilibrium (DSGE) model. The estimation is conducted using Bayesian methods, with Canadian quarterly data from 1983Q1 to 2021Q4. The results suggest that the dynamic effects of oil price shocks on Canadian macroeconomic variables vary according to their sources. In particular, a 10 percent increase in the real price of oil driven by positive foreign aggregate demand shocks has a positive effect of about 1.2 percent on Canada’s real GDP upon impact and the effect remains positive over time. In contrast, an increase in the real price of oil driven by negative foreign oil supply shocks or by positive speculative oil demand shocks causes a small effect of about 0.15 percent on Canada’s real GDP upon impact but causes a slightly decline afterwards. At the same time, an oil price increase that originates from aggregate demand shock causes an increase in consumption and investment, while an oil price increase that originates from oil supply shocks or from speculative oil demand shocks cause a decline in consumption and investment. Furthermore, among the identified oil shocks, aggregate demand shocks have been by fare more important in explaining the variations of most of Canadian macroeconomic variables over the estimation period. In contrast, speculative oil demand shock appears to be the first source of variations in real oil price.
... Such an effect might create concerns regarding productivity in the future, lead to negative market sentiment, and decreases in companies' equity values (see, e.g., Cunado and de Gracia, 2014;Joo and Park, 2021; and references therein). Further impacts can be seen on the GDP since radical changes in oil prices can create a wealth transfer between oil-importing and oil-exporting countries through a shift in terms of trade (see, e.g., Elekdag et al., 2008). Therefore, the question about oil price driving factors has attracted considerable attention since a reliable forecast of oil prices is of great interest to energy companies, portfolio managers, and policymakers. ...
We develop two news-based investor attention indices from the news trends function of the Bloomberg terminal and investigate their predictive power for monthly returns on crude oil futures contracts with maturities up to 12 months over the period from January 2012 to December 2020. After controlling for relevant macroeconomic variables, our main results show that the Oil-based Institutional Attention Index is useful in predicting oil futures returns, especially during price downturn periods, while the forecasting accuracy is further improved when the Commodity Market Institutional Attention Index is used. However, this forecasting accuracy decreases with the maturity of oil futures contracts. Moreover, we find some evidence of Granger-causality and regime-dependent interactions between investor attention measures and oil futures returns. Finally, variable selection algorithms matter before making predictions since they create the best forecasting results in many cases considered. These findings are essential for informed traders and policymakers to better understand the price dynamics of the oil markets.
... information related to several markets and variables (Sadorsky, 1999). Consequently, these uncertain oil price movements affect different financial and economic assets (Elekdag et al., 2008). Like other markets, the energy market faces a considerable risk primarily derived from price fluctuations (Sadeghi & Shavvalpour, 2006). ...
In this preface, we investigate the past, study the present, and look for the future of financial modeling, risk management of energy and environmental instruments, and derivatives based on articles selected in this special issue (SI). We also summarize the significant findings of those articles and identify the research trends.
... Empirical studies have identified an important role of broad-based demanddriven oil price movements (Juvenal & Petrella, 2014;Kilian, 2009;Kilian & Murphy, 2014). In particular, the run-up of oil prices over the 2000-2007 period have been attributed to the successive upward revisions of real global output, particularly for China (Elekdag et al., 2008;Kilian & Hicks, 2013). Such foreign demand shocks increase the demand for all goods and have different consequences for the output-inflation trade-off of monetary policy (Bodenstein et al., 2012). ...
... Such foreign demand shocks increase the demand for all goods and have different consequences for the output-inflation trade-off of monetary policy (Bodenstein et al., 2012). A global model with endogenous commodity prices is necessary to distinguish foreign supply from demand driven commodity price shocks and capture their differential effects on exchange rates, capital accounts, and the non-oil trade balance (Bodenstein & Guerrieri, 2011;Elekdag et al., 2008). ...
... The elasticity of substitution between domestically produced tradable goods and imported tradable goods is consistent with the Erceg et al. (2005) and Murchison and Rennison (2006). Together, the real adjustment costs as well as the elasticities of substitution in both the production and the usage of energy and non-energy commodities govern the "steepness" of the demand and the supply curves and are calibrated to be consistent with Elekdag et al. (2008) and Lalonde and Muir (2007). ...
We examine the relative ability of simple inflation targeting (IT) and price level targeting (PLT) monetary policy rules to minimize both inflation variability and business cycle fluctuations in Canada for shocks that have important consequences for global commodity prices. We find that commodities can play a key role in affecting the relative merits of the alternative monetary policy frameworks. In particular, large real adjustment costs in energy supply and demand induce highly persistent cost-push pressures in the economy leading to a significant deterioration in the inflation – output gap trade-off available to central banks, particularly to those pursuing price level targeting.
... Therefore, changes in crude oil prices have aroused great attention around the world. Theoretically, the fluctuation of crude oil prices depends on the basic supply-demand relationship of the oil market, but with the continuous changes and development of the oil market and the economies of various countries, there are many potentially important forces in oil price fluctuations, such as the global economy [1], exchange rate changes [2,3], speculative behavior [4], and geopolitics [5,6]. Due to the interactive impact of these factors, oil price always shows the traits of non-linearity, non-stationarity, and high complexity [7], which also brings huge challenges to crude oil price forecasting. ...
In order to improve the prediction performance in oil price forecasting, a novel memory-trait-driven decomposition-reconstruction-ensemble learning paradigm is proposed for oil price forecasting. The proposed methodology consists of four steps, i.e., data decomposition for original complex time series, component reconstruction for decomposed components, individual prediction for the reconstructed components, and ensemble output based on the individual component prediction results, which are all driven by memory traits. For verification purpose, the West Texas Intermediate (WTI) crude oil spot prices are used as the sample data. The experimental results demonstrated that the proposed methodology can produce the better and more robust results relative to the benchmarking models listed in this study. This indicates that the proposed memory-trait-driven decomposition-reconstruction-ensemble methodology can be used as a promising solution to oil price prediction with the traits of memory.
... The choice of predicting the WTI Crude Oil Price is due to its chaotic behaviour, which follows a nonlinear dynamic deterministic process -as proved by Moshiri and Foroutan [4]. Thus, understanding the dynamics of Crude Oil Prices, either spot or futures, represents one of the most striking challenges to the forecasting abilities of private and public institutions worldwide [5]. ...
This study looks for signals of economic awareness on online social media and tests their significance in economic predictions. The study analyses, over a period of two years, the relationship between the West Texas Intermediate daily crude oil price and multiple predictors extracted from Twitter, Google Trends, Wikipedia, and the Global Data on Events, Language, and Tone database (GDELT). Semantic analysis is applied to study the sentiment, emotionality and complexity of the language used. Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) models are used to make predictions and to confirm the value of the study variables. Results show that the combined analysis of the four media platforms carries valuable information in making financial forecasting. Twitter language complexity, GDELT number of articles and Wikipedia page reads have the highest predictive power. This study also allows a comparison of the different fore-sighting abilities of each platform, in terms of how many days ahead a platform can predict a price movement before it happens. In comparison with previous work, more media sources and more dimensions of the interaction and of the language used are combined in a joint analysis.
... A limited number of studies have investigated whether economic growth has any effect on global commodity prices. For example, focussing on oil prices Elekdag, Lalonde, Laxton, Muir, and Pesenti (2007) examine the increase in demand for oil from emerging Asia using a Dynamic Stochastic General Equilibrium (DSGE) model and find that an exogenous shock to China's oil demand could lead to an increase in oil prices. Cheung and Morin (2007) use a regression approach with oil prices that are modelled to be dependent on lagged prices of oil, world output gap and the U.S. real exchange rate. ...
Since the turn of the century, China has experienced economic growth that has called for rapid industrialisation, infrastructure growth and urbanisation, making China highly dependent on primary commodities. The resource intensive growth path followed by a slowdown in recent years has raised the question to what extent her demand for commodities can affect international commodity prices. To this end, recent studies have analysed the link between economic growth or slowdown on commodity prices using linear multivariate models. However, a problem is that commodity prices are known to be highly variable and characterised by multiple structural changes. With such characteristics of the data, it is likely to be the case one may obtain mis‐specified inferences from a linear multivariate framework. Accordingly, we make use of novel Flexible Fourier Form (FFF) econometric procedures to account for multiple breaks in the economic variables, which have better power and size properties over the standard linear models. We find that the persistence of economic variables employed in this study and their causal link are better approximated by such nonlinear FFF procedures. Our results show that there is evidence of short run predictability between selected commodity prices and economic growth in China. Further, the responses of different commodity prices to shocks in economic growth are quite profound especially for those cases where we find causal links with economic growth in China. While we find a significant impact of economic growth of China on commodity prices, the response varies for different commodities.
... The choice of predicting the WTI crude oil price is due to its chaotic behaviour, which follows a non-linear dynamic deterministic process -as proved by Moshiri and Foroutan [4]. Thus, understanding the dynamics of crude oil prices, either spot or futures, represents one of the most striking challenges to the forecasting abilities of private and public institutions worldwide [5]. ...
This study looks for signals of economic awareness on online social media and tests their significance in economic predictions. The study analyses, over a period of 2 years, the relationship between the West Texas Intermediate daily crude oil price and multiple predictors extracted from Twitter; Google Trends; Wikipedia; and the Global Data on Events, Location and Tone (GDELT) database. Semantic analysis is applied to study the sentiment, emotionality and complexity of the language used. Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) models are used to make predictions and to confirm the value of the study variables. Results show that the combined analysis of the four media platforms carries valuable information in making financial forecasting. Twitter language complexity, GDELT number of articles and Wikipedia page reads have the highest predictive power. This study also allows a comparison of the different fore-sighting abilities of each platform, in terms of how many days ahead a platform can predict a price movement before it happens. In comparison with previous work, more media sources and more dimensions of the interaction and of the language used are combined in a joint analysis.
... By 2017, according to forecasts, the world's economies will demand about 33 million barrels per day [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Cost-effective oil production is becoming more important than ever, not only to the profit-making oil corporations and nations, but also to countries, communities and individuals around the world. ...
With the oil and gas industry growing rapidly, increasing the yield and profit require advances in technology for cost-effective production in key areas of reservoir exploration and in oil-well production-management. In this paper we review our group’s research into fiber Bragg gratings (FBGs) and their applications in the oil industry, especially in the well-logging field. FBG sensors used for seismic exploration in the oil and gas industry need to be capable of measuring multiple physical parameters such as temperature, pressure, and acoustic waves in a hostile environment. This application requires that the FBG sensors display high sensitivity over the broad vibration frequency range of 5 Hz to 2.5 kHz, which contains the important geological information. We report the incorporation of mechanical transducers in the FBG sensors to enable enhance the sensors’ amplitude and frequency response. Whenever the FBG sensors are working within a well, they must withstand high temperatures and high pressures, up to 175 °C and 40 Mpa or more. We use femtosecond laser side-illumination to ensure that the FBGs themselves have the high temperature resistance up to 1100 °C. Using FBG sensors combined with suitable metal transducers, we have experimentally realized high- temperature and pressure measurements up to 400 °C and 100 Mpa. We introduce a novel technology of ultrasonic imaging of seismic physical models using FBG sensors, which is superior to conventional seismic exploration methods. Compared with piezoelectric transducers, FBG ultrasonic sensors demonstrate superior sensitivity, more compact structure, improved spatial resolution, high stability and immunity to electromagnetic interference (EMI). In the last section, we present a case study of a well-logging field to demonstrate the utility of FBG sensors in the oil and gas industry.
