Real Price Of Crude Oil Research Articles

The Impact of Indonesian Crude Oil Demand Prices on the Indonesian Biodiesel Industry

Biodiesel is one of Indonesia's potential export commodities as there is a production surplus that occurs every year from 2006 to 2020. The study used the 2SLS method with a validation and simultaneous equation model. Data processing using SAS/ETS version 9.4. The purpose of this study is to analyze the impact of Indonesia's crude oil demand price on Indonesia's biodiesel industry. The findings indicate that the demand for Indonesian biodiesel is positively impacted by a rise in the global real price of crude oil by 0.11%. This increase is due to both the biodiesel industry's and the local market's need for Indonesian palm oil. The local price of Indonesian biodiesel would rise by 0.49% due to the increase in demand for the fuel. The amount of biodiesel produced in Indonesia will increase by 0.87%, this increase in production is due to increasing domestic demand for Indonesian palm oil, and the volume of biodiesel exported to destination countries will increase by 0.01%. Exports of palm oil may be more impacted by the policy of raising the actual world price of crude oil. This implies that the program may potentially result in a rise in raw material exports. On the other hand, the biodiesel industry and other industries also see a spike in palm oil as a result of the strategy of raising the real world price of crude oil.

Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

AbstractAs the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making.

Forecasts of the real price of oil revisited: Do they beat the random walk?

In macroeconomic forecasting, the real price of oil is traditionally computed as the monthly average price of oil deflated by the price index. Consequently, the no-change forecast used to benchmark forecasts of the real price of crude oil is a monthly average price. We demonstrate that an alternative no-change forecast which reflects the random walk forecast from daily oil prices – the end-of-month price – is significantly more accurate in predicting the real price of oil up to one year ahead. We find that at the one-step-ahead prediction, all existing forecasts that outperform the monthly average no-change forecast perform worse than the end-of-month no-change forecast. The results call into question the usefulness of existing forecasting approaches for the real price of crude oil relative to naive forecasts.

A weekly structural VAR model of the US crude oil market

We present a weekly structural Vector Autoregressive model of the US crude oil market. Exploiting weekly data we can explain short-run crude oil price dynamics, including variations related with the COVID-19 pandemic and with the Russia’s invasion of Ukraine. The model is set identified with a Bayesian approach that allows to impose restrictions directly on structural parameters of interest, such as supply and demand elasticizes. Our model incorporates both the futures-spot price spread to capture shocks to the real price of crude oil driven by changes in expectations and US inventories to describe price fluctuations due to unexpected variations of above-ground stocks. Including the futures-spot price spread is key for accounting for feedback effects from the financial to the physical market for crude oil and for identifying a new structural shock that we label expectational shock. This shock plays a crucial role when describing the series of events that have led to the spike in the price of crude oil recorded in the aftermath of Russia’s invasion of Ukraine.

Comparative analysis of two resources-based economies: A study of policy recommendation toward sustainable development

This is a comparative study between two resource endowed economies, Norway and Nigeria towards ascertaining the impact of the resources in the sustainable development (SD) of the countries. The findings are expected to be a highlight for the poor performing economy (Nigeria) and to replicate the tested and workable policies of Norway to Nigeria economic performance. Separate empirical estimates and analyses with quarterly data of (1992QI-2018QIV and 1992QI-2019QIV) for both countries (Nigeria and Norway) respectively are done for each in a time series manner. The results from the both autoregressive distributed lag (ARDL) and granger causality for both countries are as follows: Dutch disease is found via government spending effects and crude oil price on agriculture. Foreign direct investment (FDI), real gross domestic product (GDP) and real exchange rate are found positively impacting Nigerian agricultural sectors. The findings for the Norway's case are as follows: Dutch disease symptom is found via government spending effects, real exchange rate and crude oil price on manufacturing sector. From granger causality findings, there is a clear exposition of nexus among the government spending, oil price, FDI and real exchange rate which shows implication of government spending and oil price in both studies. This is a pointer towards existence of Dutch disease in both countries. This, notwithstanding, Norway as a country is among the best performing economies of the world due to efficient and effective policies. Nigeria having performed so poor is expected to consider Norway as a model in mitigating her Dutch diseases.

Forecasting the real prices of crude oil: A robust weighted least squares approach

The ordinary least squares (OLS) estimator inflates the estimation variance of parameters in the presence of outliers, thus providing poor out-of-sample forecasts. We forecast the real price of crude oil using a robust weighted least squares (RWLS) approach that has the potential to improve forecasting performance by dealing with outliers. This approach down-weights extreme observations using a class of kernel functions. Our results show that the RWLS improves forecasting accuracy relative to the standard OLS approach. Furthermore, the predictability of oil prices revealed by the RWLS is statistically significant for longer horizons. The predictive ability of the RWLS comes from more efficient estimates and the trade-off between forecast bias and variance. Sensitivity analyses indicate that the findings are robust to alternative validation samples, kernel functions, and combination strategies.

Equity premium prediction using the price of crude oil: Uncovering the nonlinear predictive impact

Highly cited studies, such as Mork (1989), Hamilton (1996), Hamilton (2003) and Kilian and Vigfusson (2013) establish a nonlinear connection between movements in the real (nominal) price of crude oil and the real gross domestic product (GDP) growth rate. Based on the observation that crude oil price increases have a different impact on the real GDP growth rate than crude oil price decreases, the authors introduce various nonlinear transformations of the price of crude oil to account for the nonlinear predictive impact. Using predictive regressions à la Goyal and Welch (2008), a growing number of studies in recent years have conditioned on these nonlinear crude oil price measures to predict equity premium out-of-sample. By applying the dynamic rotation rule suggested in Zhu and Timmermann (2021), we demonstrate that the form of nonlinearity that helps improve relative out-of-sample point prediction accuracy the most does not have to do with crude oil price increases (decreases) relative to recent prices as perceived by the current literature, but with connecting the relative out-of-sample point prediction performance between the benchmark and the crude oil price-based model with movements in economic variables measuring equity market volatility, economic activity, uncertainty and financial conditions. The statistical evidence of out-of-sample equity premium predictability also translates to economic gains.

The influence from a demand perspective with real economic activity: China versus the United States in world oil markets

AbstractIn this paper, we provide direct evidence for an increasingly critical role of China in the world oil markets. Specifically, our empirical results confirm that the influence of China's oil demand on the oil price of the world has increased over time and surpassed that of the United States. The contribution of demand perspective from China are formally quantified by three key variables of world oil market, which are world oil production, real economic activity and the real price of crude oil. The impact of oil demand from China is used to compared with those from other oil markets, including shocks from the United States. We also construct suitable proxies that reflect real economic activity from these economies, and identify them via a structural vector autoregressive model to disentangle the influence of oil demand from China, the United States and the rest of the world. Our results confirm the finding of recent research, which is the increasing demand for oil from developing countries has become a key driver of the price of oil.

Geopolitical risk trends and crude oil price predictability

Motivated by recent investigations on the connections between geopolitical risk and crude oil prices, we implement a moving average strategy using the geopolitical risk index to identify risk uptrends and thus forecast real crude oil prices. The empirical results show that geopolitical risk trends can significantly predict oil prices both in- and out-of-sample. From an economic perspective, a mean-variance investor can achieve considerable gains using such a simple conversion of geopolitical risk. Moreover, we find that the geopolitical risk trend contains additional information content beyond financial, commodity, and oil fundamentals. The uptrend of geopolitical risk, which disrupts both economic activity and oil production, imposes stronger shocks on future oil demand than on supply, and thus results in a dramatic decrease in oil prices.

Prime money market funds regulation, global liquidity, and the crude oil market

This paper explores how the 2016 US Prime Money Market Funds (PMMFs) regulation affected the crude oil market. This reform led to an increase in short-term dollar borrowing costs and the oil sector became particularly susceptible to disruptions in the global funding market due to a post-financial crisis debt expansion which far outpaced other commodity industries. Building on the global crude oil market SVAR model pioneered by Kilian and Murphy (2014), we find that tighter PMMFs funding conditions have a lagged negative effect on the real price of crude oil and a lagged positive effect on oil production. We show that these responses are driven primarily by a fall in certificates of deposits issued by global banks. Lastly, we evidence that the US nominal effective exchange rate acts as a transmission channel for the negative funding shock to the real price of oil.

Oil prices and the natural gas liquids markets

<abstract><p>This paper investigates the impact of oil market structural shocks on the prices of natural gas liquids (NGLs), including ethane, propane, normal butane, isobutane, and natural gasoline, over the period from January 1985 to April 2020. To identify the structural demand and supply shocks in the crude oil market, we use a vector autoregression model and assume that the innovations to the real price of crude oil are predetermined with respect to the local NGLs markets. Our results show that, in the long run, more than 55% of the variation in the real price of NGLs is explained by the structural shocks in the global crude oil market. We also find that, unlike oil supply shocks, demand-side shocks have permanent and persistent impacts on NGLs' real prices and should be of main concern to investors aiming to develop gas wells and NGLs producing technologies.</p></abstract>

Assessing the food vs. fuel issue: An agent-based simulation

Energy security and environmental concerns are critical issues for North America. The development of the bio-fuel industry has given grain farmers another crop option. Trends towards renewable fuels could also put fuel production at odds with traditional food agriculture, a situation referred to as the “food vs. fuel” debate. This research models profit maximizing farm crop choices as energy crops become a crop option. We develop a simulation to estimate the oil price level where farmers would switch from the production of grain for food over to bio-energy crops. We also investigate consequences associated with this process on the elevator industry.Using a major crop production region in Canada as a template, we estimate that real annual crude oil prices must remain consistently 30% above the (2008) peak oil price before most farmers in the region would switch over to the production of wheat for biofuel. If government biofuel support falls from current levels, a considerably higher oil price would be needed to entice farmers to switch. As the production of food grain decreases, we also observe that the grain handling industry will gradually “shake out” and likely be characterized by much higher elevator firm concentration than exists currently.

Oil for Pakistan: What are the main factors affecting the oil import?

Oil is the second largest primary source of energy supply in Pakistan, which is linked to numerous sectors. The existing study aims to calculate the crude oil import demand in Pakistan as a function of real income and the real price of crude oil from 1986 to 2018. We carried out the autoregressive distributive lag method to measure the robustness of price and income elasticities and forecasted crude oil import dependency analysis based on a fitting line from 1986 to 2035. The empirical outcomes of the study are first, the price and income elasticities are consistent with the theoretical prospects, which confirm that income elasticity is significant, while price elasticity is insignificant. Second, the positive growth of income elasticity is 0.21 proposes that imported crude oil in Pakistan is rising income level due to sectorial oil consumption. Third, the two-dimensional imported crude oil and forecasted oil dependency during 2019–2035 estimated that Pakistan's imported crude oil dependency would increase annually by 0.07 %, and 76 % dependency would reach until 2035. Finally, being a necessary product, the Government should support macroeconomic regulation and strengthen the mechanism of oil security and price regulations. Furthermore, the policy suggestions provided below will help Pakistan's policymakers respond appropriately.

Forecasting crude oil real prices with averaging time-varying VAR models

The aim of this research is to discuss the ability to forecast real crude oil price by the use of Time-Varying Vector Autoregression (TVP-VAR) models. In particular, model averaging and model selection schemes over several TVP-VAR models are performed. These methods address the problem of variable uncertainty. Indeed, several previous studies indicate that explanatory variables for crude oil prices can be different in different periods of time. Further, the strength of the relationship between crude oil price and its determinants can vary in time. The applied model combination schemes are an extension of Dynamic Model Averaging, which has already been found viable. Moreover, geopolitical risk is included in each model as an endogenous variable, and the model combination scheme is constructed in a way to tackle the joint forecasting ability (with respect to crude oil real price and geopolitical risk). It is found that, indeed, the Vector Autoregression approach results in more accurate forecasts than single equation a Time-Varying Regression or the standard Dynamic Model Averaging. Also, the model combination scheme of several Vector Autoregression models outperforms a single Vector Autoregression model approach. However, forecast accuracy is tested with some novel tools, such as Giacomini and Rossi fluctuation test and Murphy diagrams, which are able to capture time-varying predictive ability significances and several scoring functions.

On the China factor in the world oil market: A regime switching approach

Abstract We investigate the relationship between China's macroeconomic performance and the world oil market over the past two decades. Unlike existing studies, we allow for possible regime changes by utilizing a class of Markov-switching vector autoregression (MS-VAR) models. The model identifies key regime changes in the structural shocks when the oil market experiences low and high volatility. We find that demand shocks from China and the rest of the world have a larger impact on the real price of crude oil during periods of high volatility. Supply shocks, in contrast, have a large effect on the price in the low volatility regime. A similar state-dependent phenomenon is observed for the impact of oil price shocks on China economic activity, however the size of these responses is relatively small. Thus, despite China being a major player in international oil markets, we conclude that oil market shocks tend to have little impact on China's real GDP growth.

Internet Usage and Economic Growth: The Case of Mexico

This paper finds that more internet users as a percentage of total population promote economic growth in Mexico and that the impact exhibited a nonlinear relation and was greater in the initial stage of Internet adoption. In addition, less government budget deficit as a percent of GDP, a higher real stock price, real peso appreciation, a higher real crude oil price or a lower expected inflation rate would enhance economic growth.

Evidence of speculative bubbles and regime switch in real estate market and crude oil price: Insight from Saudi Arabia

AbstractThe sector performances of many economies especially the oil‐producing ones, are consistently linked with the volatility of the international crude oil prices. Considering the significance of the international crude oil price to the economy of the Saudi Arabia, the current study investigates the dynamics of the crude oil price and the country's real estate market. This is achieved by investigating the evidence of both speculative bubbles and regime switching in the crude oil and real estate market from October 3, 2005, to March 29, 2018. As such, the Supremum Augmented Dickey–Fuller (SADF) and Markov switching regression model were employed. Although there is no significant evidence of speculative bubbles in the Saudi Arabia's real estate market, the result reveals statistical significant evidence of price bubbles in the Brent crude oil price especially between the period of 2007 to 2008, which coincides with the Global Financial Crisis period. However, the Markov switching investigation reveal that there is evidence of significant and positive impact of crude oil price return on the real estate market in the regimes. Importantly, empirical evidence shows that the impact is higher in Regime 2 even as the global factor (proxy volatility index) is evidently significant. Although the regimes are persistent, the expected regime duration of the stable regime is of the higher quarter. This investigation is statistically significant and robust, especially when Organization of the Petroleum Exporting Countries (OPEC) price of crude oil is used in lieu of the European Brent price. The estimation result encourages more strict energy policies that tackle the Saudi Arabia's energy and the real estate challenges.

Effects of International Crude Oil Prices on Energy Consumption in China

This study aims to test the effects of changes in international crude oil prices on changes in crude oil and hydropower use from 1965 to 2016. We suggest a cointegration relationship between the consumption of coal, crude oil, and hydropower and the real crude oil price. The real price is weakly exogenous for the long-run relationship and has impacted energy consumption accordingly. The long-run crude oil price elasticity of oil use is 0.460. Our estimate suggests a positive oil price–oil use relationship in China, which is dramatically different from many previous studies but is consistent with a few past studies. The growth in external oil prices may lead to a long-run increase in hydropower use in China, with a long-run price elasticity of 0.242. The long-run crude oil price elasticity of coal use is −0.930. Hence, increased oil and hydropower use could make up the energy supply–demand gap left over by the decreased coal use. Strictly planned domestic fuel prices and rapidly growing family incomes should diminish the negative effect of external oil prices on domestic crude oil demand. In the long run, given a strictly managed energy price, the growth in external oil prices is not likely to noticeably restrain the domestic oil demand or lead to a dramatic increase in coal use. We suggest that the large-scale development and utilization of hydropower may be inappropriate. Coal utilization policies must be reviewed. The appropriate increase in clean coal consumption could reduce the consumption of crude oil and hydropower; meanwhile, carbon emissions will not increase.

Dynamic Nexuses between Macroeconomic Variables and Sectoral Stock Indices: Reflection from Indian Manufacturing Industry

Determination of significant sector specific macroeconomic factors under the board manufacturing industry is an important task. In Indian context, using the monthly data on five major manufacturing sector specific indices (such as BSE-Basic Materials, BSE-Consumer Discretionary Goods and Services, BSE-Fast Moving Consumer Goods, BSE-Health Care and BSE-Industrials) and the macroeconomic variables (gold price, index of industrial production, wholesale price index, money supply, foreign portfolio investment ratio (FPIR), rate of interest, real effective exchange rate and crude oil price and economic policy uncertainty) for the period September, 2005 to November, 2016, the present study attempted to explore the significant sector specific macroeconomic variables in long run as well as short run. The empirical results obtained by applying the ARDL-UECM model suggested that economic policy uncertainty, FPIR and price factor were observed to be the most important determinants of all the five sectoral stock indices for the study period.

Fundamentals versus speculation in oil market: The role of asymmetries in price adjustment?

The recent literature on financialisation of oil markets highlights the destabilizing impact of speculative activity on the oil price formation. Time-series studies using linear unit root tests might suggest absence of long-run mean reversion in oil prices, implying dominance of speculation over fundamental factors. However, the price adjustment could take a nonlinear form due to certain characteristics inherent to the oil markets. This study examines the presence of nonlinear adjustment behaviour in nominal and real crude oil prices towards a long-run equilibrium using two specific types of smooth transition models, capturing size and sign asymmetries. The results suggest asymmetric mean-reversion of oil prices towards the trend over the cycle and potential structural breaks leading to trend-shifts. In sum, taking cognizance of asymmetries suggests an adjustment behaviour largely based on fundamentals in long-run, pointing out relatively limited impact of speculation due to financialisation in oil market. Furthermore, the analysis suggest higher forecasting power of the proposed smooth transition models for nominal oil prices compared to alternative linear benchmarks.