Crude Oil Price Movements Research Articles

OPEC and demand response to crude oil prices

This paper investigates demand response to crude oil price movements before and after the recent global financial and economic crisis. It employs several market power indices to structurally estimate price elasticities. A newly developed market power index for crude oil markets is implemented. In this approach OPEC is the central player and acts as a dominant producer in the global oil market. We quantify how a change in market structure (such as changes in marginal cost of production) would contribute to market power exercise of OPEC and have an ultimate impact on price elasticity of demand for oil. Our price elasticity predictions fall in a range reported in the literature, however estimates for pre-crisis deviate from the post-crisis ones. In fact, demand response to crude oil prices has almost doubled during the crisis. This severe change in price response can be associated with record price levels caused by supply shortages and surge in alternative renewable energy resources. The key advantages of this methodology over the existing literature are that it is simple to use and estimates price elasticity using a competition framework without specifying demand/supply function(s), and utilizes commonly observable market variables that can be applied to any admissible data frequency.

Forecasting Crude Oil Prices: a Deep Learning based Model

With the popularity of the deep learning model in the engineering fields, it has attracted significant research interests in the economic and finance fields. In this paper, we use the deep learning model to capture the unknown complex nonlinear characteristics of the crude oil price movement. We further propose a new hybrid crude oil price forecasting model based on the deep learning model. Using the proposed model, major crude oil price movement is analyzed and modeled. The performance of the proposed model is evaluated using the price data in the WTI crude oil markets. The empirical results show that the proposed model achieves the improved forecasting accuracy.

Revisiting the Effect of Crude Oil Price Movements on US Stock Market Returns and Volatility

From mid-2014 to 2016, oil prices plunged rapidly causing significant volatility in the US and global equity markets. This change in crude oil prices occurred after a significant run up in oil prices three to four years earlier. Each change in the growth trajectory of oil prices affects stock market returns. How and why do oil price shocks affect the expected stock market returns among key sectors of the economy? This paper explores this issue by examining how the magnitude of crude oil price changes affects the stock market returns and variances of key producing, banking and consuming segments of the US economy. Our findings provide some explanations for the asymmetric responses to positive and negative oil shocks found in these key sectors of the economy.

Dynamic Relationship between Crude Oil Price, Exchange Rate and Stock Market Performance in Nigeria

This study employed a multivariate Vector Error Correction Model (VECM) that uses the Granger causality test and generalized variance decomposition analysis to study the relationship between crude oil prices, exchange rate and stock market performance in Nigeria from January 1995 to December 2014. As expected from an oil exporting country, a short-run positive relationship is observed between the Nigerian stock market and crude oil prices and the direction is from crude oil prices to the Nigerian stock market but not the other way round. The short run relationship between exchange rate and Nigerian stock market is observed to be positive and the direction is from the exchange rate to the Nigerian stock market. Exchange rate is also observed to be positively related to the movements in the crude oil prices in the short run with the direction of causality running from crude oil prices to exchange rate. However, the results of a multi-variate Johansen cointegration test suggest the existence negative relationship among the three variables in long run. The Variance Decomposition analysis shows that the Nigerian stock market performance and Exchange rate behaviour are strongly influenced by the movements in Crude Oil prices. Key Words : Crude Oil Price, Exchange Rate, Nigerian Stock Market, Vector Error Correction Model

Impact of oil price fluctuations on an energy-exporting economy: Evidence from Russia

The oil prices have been falling recently and reached the record low level. This paper assesses the impact of crude oil price movements on two macro variables, the Gross Domestic Product (GDP) growth rate, and consumer price index inflation rate, in Russia. Russia is selected for this analysis because it is one of the world’s largest oil exporters and oil revenue accounts for a significant portion of the country’s government budget. The main objective of this study is to see and compare the response of the two macro-variables of Russia during the high oil price era with the low oil price era. The results obtained suggest that the impact of crude oil price movements on Russia’s GDP growth rate in low oil prices era is much higher than in high oil price era. On the other hand, the impact of crude oil price movements on the consumer price index inflation was found to be milder in low oil price era. These results are obtained by examining the impact of oil price fluctuations on supply and demand side of the Russia`s economy.

Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics

For the modeling of complex and nonlinear crude oil price dynamics and movement, wavelet analysis can decompose the time series and produce multiple economically meaningful decomposition structures based on different assumptions of wavelet families and decomposition scale. However, the determination of the optimal model specification will critically affect the forecasting accuracy. In this paper, we propose a new wavelet entropy based approach to identify the optimal model specification and construct the effective wavelet entropy based forecasting models. The wavelet entropy algorithm is introduced to determine the optimal wavelet families and decomposition scale, that will produce the improved forecasting performance. Empirical studies conducted in the crude oil markets show that the proposed algorithm outperforms the benchmark model, in terms of conventional performance evaluation criteria for the model forecasting accuracy.

Macroeconomic effects of oil price fluctuations on emerging and developed economies in a model incorporating monetary variables

The goal of this paper is to examine the impact of crude oil price movements on two macro variables, the gross domestic product (GDP) growth rate and the consumer price index (CPI) inflation rate, in three countries, the People’s Republic of China (an emerging economy), Japan, and the United States (developed economies), in a model incorporating monetary variables (money supply and exchange rate). The main objective of this research is to investigate whether these economies are still reactive to oil price movements and compare their reactions. Monetary variables are included in this survey because our earlier research showed that they have a significant role in oil price determination. To assess the relationship between crude oil prices and macro variables we adopt an N-variable structural vector autoregression (SVAR) model. The results suggest that the impact of oil price fluctuations on developed oil importers’ GDP growth is much milder than on the GDP growth of an emerging economy. On the other hand, however, the impact of oil price fluctuations on the People’s Republic of China’s inflation rate was found to be milder than in the two developed countries that were examined.

Response of macro variables of emerging and developed oil importers to oil price movements

This paper assesses the impact of crude oil price movements on two macro variables, the gross domestic product (GDP) growth rate and consumer price index inflation rate, in the developed economies of the United States and Japan, and an emerging economy, the People's Republic of China (PRC). These countries were chosen for this research because they are the world's three largest oil consumers. The main objective of this study is to see whether these economies are still reactive to oil price movements. The results obtained suggest that the impact of oil price fluctuations on developed oil importers’ GDP growth is much lower than on the GDP growth of an emerging economy. The main reasons for this lie in fuel substitution (higher use of nuclear energy, gas, and renewables), a declining population (for Japan), the shale gas revolution (for the United States), and strategic oil stocks and government-mandated energy efficiency targets in developed economies. All of these factors make developed economies more resistant to oil shocks. On the other hand, the impact of oil price movements on the PRC's inflation rate was found to be milder than in the two developed countries that were examined. The main cause for this is that the PRC experiences a larger forward shift in its aggregate supply due to higher growth, which allows it to avoid a massive increase in price levels following oil price shocks.

Interpreting the crude oil price movements: Evidence from the Markov regime switching model

Since 2009, global financial crisis has eased gradually and world economy has begun to recover slowly. Meanwhile, both Brent and WTI (West Texas Intermediate) crude oil prices have entered into a new round of increase and volatility, and the abnormal price spreads between them have been identified. Under this circumstance, this paper employs the Markov regime switching model with dynamic autoregressive coefficients to explore the price regimes of Brent and WTI after the financial crisis. Then it analyzes the causes of the abnormal spreads between the two benchmark crude oil prices based on the statistical observations of their typical regime differences. The results show that there are three main regimes in both Brent and WTI crude oil price returns, i.e., sharply downward, slightly downward and sharply upward regimes for Brent whilst sharply downward, relatively stable and sharply upward regimes for WTI. Meanwhile, the typical price regimes of Brent and WTI are the “sharply upward” and “relatively stable” regimes after the financial crisis, respectively. Besides, their different movement regimes in recent years are mainly attributed to their different market fundamental situations and the dynamics in crude oil markets, which also lead to the occurrence of their abnormal price spreads.

Zimbabwe Stock Exchange (“ZSE”)’s Exposure to Global Crude Oil Price Volatility

The major aim of this paper is to investigate Zimbabwe’s Stock Exchange indices’ exposure to global oil price volatility for the period 2009-2012.To determine the relationship between volatility of crude oil returns and volatility of stock returns of the ZSE indices using econometric GARCH models. Also to investigate the correlation of the global oil price in the form of Brent Crude oil prices index and Western Texas Intermediate (WTI) oil prices index with ZSE Industrial Index and the ZSE Mining index between 2009 and 2012. A GARCH approach is employed to analyse data from ZSE and Chicago Mercantile Exchange, OPEC and Datastream® Data. Daily data for crude oil prices and Zimbabwe stock exchange indices were collected for the period 2009-2012 and analysed. The variables of the Zimbabwe stock exchange are ZSE Industrial Index; and ZSE Mining index. Variables on Crude oil prices comprised of Western Texas Intermediate (“WTI”) spot prices index; Original Research Article Chiwanza et al.; BJEMT, 6(1): 22-37, 2015; Article no.BJEMT.2015.040 23 and Brent Crude oil spot prices index. Returns of stock on all the four indices were calculated. It was assumed that returns on stocks would mirror stock price movements. Volatility of returns on ZSE industrial index was very low with standard deviations ranging between -.01 to +.01. Volatility of returns on the ZSE Mining index was significant relative to the industrial index with standard between-0.1 to -0.1. Volatility of stock returns on Brent Crude spot price index was very high with standard deviations ranging between -0.6 to +0.6, while stock returns on Western Texas Intermediate (“WTI”) spot prices index displayed high volatility, standard deviations ranged from 0.1 to +0.1. Standard deviation indicates the level of dispersion from the the mean. GARCH coefficients indicated that the mean of stock returns as represented by were generally negative for the two domestic stock indices while the means of global oil stock returns were positive. Parameters ∝ and of the four indices were statistically significant. The coefficients of all the indices were highly significant ranging between 0.6300 to 0.9300 indicating that volatility was persistent in the period under investigation and that volatility was to a large extend driven by the prices and values of the previous time period (past performance).There was a positive correlation between industrial index and Brent crude with a correlation coefficient of 0.505 as well as a positive correlation between the ZSE Industrial index and the WTI oil price index with a coefficient of 0.520. There was a negative correlation coefficient of -0.332 between the Mining index and the global Brent crude oil prices as well as a negative correlation coefficient of -0.201 between Mining index and WTI Crude. The results of the study confirmed the hypothesis that the ZSE stock markets are indeed exposed to significant exogenous risks emanating from rising global crude oil price movements. There are however, moderating factors as the standard deviations on ZSE stock returns are much lower compared to standard deviations of stock returns on the global oil indices. Also the correlation coefficients are on the low side. Increases in crude oil prices have the potential to subdue any favourable factors to share price increases.

The dynamics of crude oil price movements: from financialisation of crude oil prices and price expectation perspectives

This paper aims to examine the price pressure transmission effect from the crude oil futures market to the spot market through price expectation channel. It derives the relationship between the spot and the futures prices in the crude oil market and finds that the changes of the futures price and world output significantly predict the spot price movement. The impulse responses and the variance decomposition analyses demonstrate that the effect of the futures price on the spot price change is limited in a shorter period and diminishes as transmitting to the medium-run and the long-run while the effect of world output eventually increases and dominates in the long-run. The financialisation of crude oil prices indeed leads to a more drastic and frequent variation of price expectation among market participants that acts as a catalyst that destabilises the crude oil prices.

The Response of Macro Variables of Emerging and Developed Oil Importers to Oil Price Movements

This paper assesses the impact of crude oil price movements on two macro variables — the gross domestic product (GDP) growth rate and consumer price index inflation rate — in the developed economies of the United States and Japan, and an emerging economy, the People’s Republic of China (PRC). These countries were chosen for this research because they are the world’s three largest oil consumers. The main objective of this study is to see whether these economies are still reactive to oil price movements. The results obtained suggest that the impact of oil price fluctuations on the GDP growth of the developed oil importers is much lower than on the GDP growth of the emerging economy. The main reasons for this lie in fuel substitution (higher use of nuclear energy, gas, and renewables), a declining population (for Japan), the shale gas revolution (for the United States), and strategic oil stocks and government-mandated energy efficiency targets in developed economies. All of these factors make developed economies more resistant to oil shocks. On the other hand, the impact of oil price movements on the PRC’s inflation rate was found to be milder than in the two developed countries that were examined. The main cause for this is that the PRC experiences a larger forward shift in its aggregate supply due to higher growth, which allows it to avoid a massive increase in price levels following oil price shocks.

Effect of inventory announcements on crude oil price volatility

This paper examines the behavior of crude oil futures price volatility and investigates how the EIA weekly crude oil inventory reports announcements, especially information shocks, impact crude oil price movement and volatility. This study focuses on inventory information shocks using a new measure rather than on inventory changes themselves. The empirical results reveal that inventory information shocks rather than actual inventory changes negatively affect crude oil returns on the day the EIA releases the inventory information, although inventory shocks have no effect on daily conditional variance, which mainly follows a GARCH(1,1) process. To test the robustness of our model, we re-estimate the models for three subsamples. According to all results, we find that the effect of inventory shocks is weakened in rapid growth periods and disappears in steep fall markets.

Day-ahead crude oil price forecasting using a novel morphological component analysis based model.

As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.

Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks

This paper uses monthly data from 1984:M10 to 2012:M8 to show that oil-sensitive stock price indices, particularly those in the energy sector, have strong power in predicting nominal and real crude oil prices at short horizons (one-month-ahead predictions), using both in- and out-of-sample tests. In particular, the forecasts based on oil-sensitive stock price indices are able to outperform significantly the no-change forecasts. For example, using the NYSE Arca (AMEX) oil index as a predictor, the one-month-ahead forecasts for nominal crude oil prices reduce the mean squared prediction error by between 22% (for the West Texas Intermediate oil price) and 28% (for the Dubai oil price). Moreover, we find that the directional forecast based the AMEX oil index is significantly better than a 50:50 coin toss. The novelty of this analysis is that it proposes a new and valuable predictor that both reflects timely market information and is readily available for forecasting the spot oil price.

Speculative trading and WTI crude oil futures price movement: An empirical analysis

Based on the historical data of CFTC’s Commitments of Traders (COT) reports from 2007 to 2010, this paper empirically studies the influence of speculators’ positions on WTI crude oil futures returns. The results indicate that, first, the instantaneous feedback of speculators’ position change on crude oil price return proves statistically significant and dominates the linear feedback relationship between them during the sample period although speculation does not appear a significant driver of crude oil price movement in the lead-and-lag sense. Second, the contemporaneous influence of speculators’ positions on oil price takes evident linearity but weak nonlinearity. Third, when oil price has high (low) volatility, non-commercials’ position change may exert a significant (insignificant) linear shock on oil price returns. And whether crude oil price stays in high or low volatility, the nonlinear influence does not appear significant. Finally, the linear influence appears symmetric when crude oil price goes up and down, but the nonlinear influence takes asymmetric feature; and neither of linear and nonlinear influence is symmetric when crude oil price experiences high and low volatility.

Crude Oil Price Movements and Determinant Factors: A Historical Overview

Crude oil is one of the most strategic commodities and plays a critical role on the world’s economy and macroeconomics factors, such as inflation, recession, economic growth, interest rates, exchange rates, and etc. In 2010, the crude oil’s share from the world’s fossil fuel consumption was 38%, share of coal was 35% and share of natural gas was 27% from the total fossil fuel consumption. Therefore, crude oil is the largest demanded fossil fuel in the world, and its fluctuations and determinant factors are one of the most encouraging subjects for energy researchers and economists. In this study we perform a historical analysis on crude oil price movements and determinant factors behind of that during 1940-2011. This analysis can be used by oil price forecasters.

Small Trends and Big Cycles in Crude Oil Prices

In this paper, we employ an unobserved components model to disentangle the long-term trend from cyclical movements in international benchmark crude oil prices using data from 1861 to 2010. The in-sample estimation of the model identifies a deterministic quadratic trend and two types of cycles, with the short cycle having a period of 6 years and the long cycle of 29 years. Compared to the large amplitude of the cycles, the growth rate of the long-term trend is small. The out-of-sample forecasting performance of various competing models is compared to that of a “no change” random walk forecast. While the random walk forecast tends to be the most accurate in shorter horizons, it is outperformed by the trend-cycle models in longer horizons. The results provide evidence of predictability in the price of crude oil in longer horizons.

Asymmetric adjustment of diesel or petrol retail prices to crude oil price movements: New Zealand evidence

AbstractThis study has twofold objectives. Firstly, to examine the short‐ and long‐run relationships between diesel or petrol retail prices in New Zealand and crude oil prices and, secondly, to assess the response of retail sector price movements to changes in crude oil prices. The study uses both standard and hidden asymmetric cointegration methods. The major findings are as follows: the prices of diesel and petrol maintain both short‐ and long‐run relationships with crude oil prices although the pass‐through rates differ. Diesel prices are found to be symmetric in response to the changes in crude oil prices, while petrol prices respond asymmetrically to changes in crude oil prices. When crude oil prices increase, petrol prices in New Zealand increase immediately and not vice versa. The reasons for the asymmetric pricing behaviour might include higher demand for petrol than diesel, individual and tourist‐type customers rather than commercial and industrial customers, search costs, revenue‐maximising behaviour of retailers based on high demand, and government taxes and levies.

Multiscale entropy analysis of crude oil price dynamics

Price formation in crude oil markets is the result of the action of many participants ( e.g., producers, governments, speculators, etc.) whose effects are perceived at different time scales, from days to years. The diversity of participants as well as the occurrence of extreme socio-political events yields a market with complex price evolution. This paper uses entropy methods to monitor the evolution of crude oil price movements. As the complexity of the price can depend of the time horizon, entropy computations are performed for different time scales via low-pass filtering of the price difference dynamics. The results are interpreted in term of relative market efficiency concepts in the sense that high entropy values should be related to a more complex and, hence, less predictable market evolution. It is shown that the highest market efficiency is found for small time scales up to one or two weeks. The multiscale entropy pattern for high time scales, longer than one quarter, is interesting as it shows alternating periods of high and low entropy levels. Interestingly, this alternating pattern has a dominant spectral component of about 4.3 years, which could be related to macroeconomic (Kitchin) business cycles. It is shown that U.S. recessions in the recent 25 years are coincident with periods of reduced entropy levels, meaning that during economic downturn the long-run market complexity is drastically reduced. The possible effects of extreme events ( e.g., Iraq War) are analyzed in terms of the relative market efficiency, suggesting that some events have affected the short-term but not the long-term market complexity. Overall, these results show that methods based on entropy concepts can shed light on the structure of crude oil markets as well as on its link to macroeconomic conditions and socio-political extreme events.