scholarly journals Heterogeneous Causal Relationships between Spot and Futures Oil Prices: Evidence from Quantile Causality Analysis

2019 ◽  
Vol 11 (5) ◽  
pp. 1359
Author(s):  
Xianfang Su ◽  
Huiming Zhu ◽  
Xinxia Yang
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Causal Relationships ◽  
Causality Analysis ◽  
Futures Prices ◽  
Spot Prices ◽  
Oil Market ◽  
Crude Oil Prices ◽  
Heterogeneous Effects ◽  
Quantile Causality

The causal relationships between spot and futures crude oil prices have attracted the attention of many researchers in the past several decades. Most of the studies, however, do not distinguish among the various oil market situations in analyses of linear and nonlinear causalities. In light of the fact that a booming or depressing oil market produces heterogeneous investment behaviors, this study applied a quantile causality framework to capture different causalities across various quantile levels and found that the causal relationships between crude oil spot and futures prices significantly derive from tail quantile intervals and appear as heterogeneous effects. Before the Iraq War, crude oil spot and futures prices were mutually Granger-caused at lower quantile levels, and only futures prices led spot prices at upper quantile levels. Since the war, a clear bidirectional causality has existed at the upper quantile levels, but only in lower quantile levels have futures prices led spot prices. These results provide useful information to investors using crude spot or futures prices to hedge or manage downside or upside risks in their portfolios.

Russia's Oil Prices: Moving toward the Market

1995 ◽  
Vol 13 (6) ◽  
pp. 553-582
Author(s):  
Eugene M. Khartukov
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Oil Industry ◽  
World Market ◽  
Market Pricing ◽  
Oil Market ◽  
Crude Oil Prices ◽  
Domestic Prices ◽  
Feasible Alternative ◽  
Oil And Oil Products

The painful perestroika of the ex-Soviet oil industry has been accompanied by an accelerated transition from the previous all-embracing and inflexible price control to actually decontrolled market pricing for both crude oil and oil products. The freed and soaring oil prices quickly hit equilibrium levels, led to sizeable contraction of inland oil demand, and generated two interrelated crises of nonpayment and overproduction. Rising transportation costs resulted in spontaneous “regionalization” of the national oil market and made oil product imports a feasible alternative to long-haul domestic supplies. While retail product prices became comparable with those in some Western countries, the backwardness of the country's refining industry and the resultant low gross product worth still are keeping domestic prices of crude oil substantially below world market parities. Though the rapid “globalization” of internal crude oil prices is on the Russian government's agenda, an immediate rise to world price levels is neither desirable nor actually possible.

What are the Driving Factors behind the Fluctuation of Crude Oil Prices? : An Independent Component Analysis

2014 ◽  
Vol 1073-1076 ◽  
pp. 2508-2511
Author(s):  
Hui Ping Li ◽  
Li Wei Fan ◽  
Peng Zhou
Keyword(s):  
Independent Component Analysis ◽  
Crude Oil ◽  
Oil Prices ◽  
Component Analysis ◽  
Driving Factors ◽  
Independent Component ◽  
Spot Prices ◽  
Independent Components ◽  
Crude Oil Prices ◽  
Ica Algorithm

This study adopted independent component analysis (ICA) to explore the underlying driving factors affect the international crude oil prices. Three original benchmark crude oil spot prices were first preprocessed to become normalized form by centering and whitening. Three independent components were then estimated by Fast-ICA algorithm. We find that the three independent components vary differently in their fluctuation amplitude and indicate clearly different hidden factors consisting of dominant long-term trend, medium-term extreme events influence, as well as frequent short-term irregular events such as weather and speculation. It shows that ICA is a powerful tool in finding out common hidden driving factors of international parallel crude oil prices.

scholarly journals Measuring the impact of COVID-19 surveillance variables over the international oil market

2022 ◽  
Vol 9 (1) ◽  
pp. 27-33
Author(s):  
Alshdadi et al. ◽  
Keyword(s):  
Saudi Arabia ◽  
Crude Oil ◽  
Global Economy ◽  
Oil Prices ◽  
Oil Reserves ◽  
Oil Market ◽  
Crude Oil Prices ◽  
Oil Markets ◽  
Long Term Impact ◽  
The Impact

Coronavirus (COVID-19) has turned to be an alarm for the whole world both in terms of health and economics. It is striking the global economy and increasing the unpredictability of the financial market in several ways. Significantly, the pandemic spread stimulated the social distancing which led to the lockdown of the countries’ businesses, financial markets, and daily life events. International oil markets have accommodated the crude oil prices during the early COVID-19 period. However, after the first 50 days, Saudi Arabia has surged the market with oil, which caused a certain decrease in crude oil prices, internationally. Saudi Arabia is one of the biggest oil reserves in the world. International trade is based on oil reservoirs which in turn, have been significantly dislodged by the pandemic. Therefore, it is crucial to study the impact of COVID-19 on the international oil market. The purpose of this study is to investigate the short-term and long-term impact of COVID-19 on the international oil market. The daily crude oil price data is used to analyze the impact of daily price fluctuation over COVID-19 surveillance variables. The correlation between surveillance variables and international crude oil prices is calculated and analyzed. Consequently, the project will help in stabilizing the expected world economic crises and particularly will provide the implications for the policymakers in the oil market.

scholarly journals Modeling Nonlinear Granger Causality and Co-Integration Between Gold Price Returns and Crude Oil Price Returns

2018 ◽  
Vol 14 (2) ◽  
pp. 105-116
Author(s):  
Nawaz Ahmad ◽  
Keyword(s):  
Crude Oil ◽  
Granger Causality ◽  
Oil Prices ◽  
Oil Market ◽  
Crude Oil Prices ◽  
Long Run ◽  
Gold Market ◽  
Oil Markets ◽  
Causality Tests ◽  
Multiplicative Effects

To model the nonlinear analysis of commodities, Gold market and crude oil market have importance to test their lead and lag price mechanism between the two. For this purpose, the log transformation has been done to calculate easier multiplicative effects. However, to record the dynamic effects of long run cointegreation model applied and tested to find the significance of the problem statement issues. Furthermore, granger causality approach also uses to examine the fundamental linkages between Gold Prices and Crude Oil prices. Meanwhile, the study of Gold markets and oil markets gained popularity among development economists during in last some decades. And try to find out stochastic relationship between the two nonlinear markets. The academic practitioners paved their efforts to run casual time series models in order to find out the robust results which help the economists and financial experts to drive the industry indicator in positive way. This study confirmed that there is cointegration between the two important indicators of large market commodities i.e Gold and crude oil and also casual interactions. Pairwise Granger Causality Tests concluded that Gold Prices return has Granger Cause on Oil Prices return in the long run and if the βeta change in the prices of gold may affect on the prices of crude oil in the long run.

scholarly journals Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach

2019 ◽  
Vol 11 (14) ◽  
pp. 3892 ◽  
Author(s):  
Lu-Tao Zhao ◽  
Li-Na Liu ◽  
Zi-Jie Wang ◽  
Ling-Yun He
Keyword(s):  
Risk Factors ◽  
Big Data ◽  
Crude Oil ◽  
Oil Prices ◽  
Online News ◽  
Oil Price ◽  
Dynamic Risk Factors ◽  
Oil Market ◽  
Dynamic Risk ◽  
Crude Oil Prices

The rapid fluctuations in global crude oil prices are one of the important factors affecting both the sustainable development and the green transformation of the global economy. To accurately measure the risks of crude oil prices, in the context of big data, this study introduces the two-layer non-negative matrix factorization model, a kind of natural language processing, to extract the dynamic risk factors from online news and assign them as weighted factors to historical data. Finally, this study proposes a giant information history simulation (GIHS) method which is used to forecast the value-at-risk (VaR) of crude oil. In conclusion, this paper shows that considering the impact of dynamic risk factors from online news on the VaR can improve the accuracy of crude oil VaR measurement, providing an effective tool for analyzing crude oil price risks in oil market, providing risk management support for international oil market investors, and providing the country with a sense of risk analysis to achieve sustainable and green transformation.

scholarly journals Relationship between Chinese and International Crude Oil Prices: A VEC-TARCH Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Changming Song ◽  
Chongguang Li
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Oil Price ◽  
External Shocks ◽  
Crude Oil Price ◽  
Economic Variables ◽  
Oil Market ◽  
Crude Oil Prices ◽  
Oil Markets ◽  
The Impact

Many studies focus on the impact of international crude oil price volatility on various economic variables in China with a hypothesis that international crude oil price affected Chinese crude oil price first and then other economic variables. However, there has been little research to explore whether or not international and Chinese oil market are integrated. This study aims to investigate the relationship between Chinese and international crude oil prices by VAR and VEC-TARCH models. It was found that the two crude oil markets have been integrated gradually. But the impact of external shocks on the Chinese crude oil market was stronger and the Chinese crude oil price was sensitive to changes in international crude oil price, implying that the centrally controlled oil market in China is less capable of coping with external risk. In addition, the volatility of both Chinese and international crude oil prices was mainly transmitted by prior fluctuation forecast and the impact of external shocks was limited, demonstrating that in both cases volatility would disappear rather slowly. Furthermore, Chinese and international crude oil markets have established a stable relationship. When the direction of external shocks on the two variables’ respective stochastic term was consistent, the impact on the two variables’ joint volatility was aggravated and vice versa.

scholarly journals Coal Pricing in China: Is It a Bit Too Crude?

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3752
Author(s):  
Raymond Li ◽  
David C. Broadstock
Keyword(s):  
Crude Oil ◽  
Granger Causality ◽  
Oil Prices ◽  
Supporting Evidence ◽  
Production Volume ◽  
Impulse Response Functions ◽  
Causality Analysis ◽  
Coal Price ◽  
Crude Oil Prices ◽  
Cointegration Tests

China is a global leader in methanol production volume, while coal is a major feedstock. The country also has the world’s largest commercial coal-to-methanol operations. Coal-based methanol is used widely within China and is a competitive substitute for gasoline. Owing to this, it is plausible that the price of coal may be linked to international crude oil prices, with methanol prices serving as the connecting channel. We add supporting evidence to a recently emerging area of literature and observe statistically significant relationships among the three prices, and, therefore, the influence from international crude oil and methanol prices on the coal price determination in China. This paper investigates the relationships among these prices for the period from January 2010 to December 2019 through spectral Granger causality analysis, alongside more traditional cointegration tests to develop a comprehensive picture of causal association between the price series in both the frequency and time domain. Cointegration is found in our tri-variate system while the frequency domain Granger causality tests reveal the long-run causality in all directions except from crude oil to methanol, thus, emphasizing the structure of coal price dependence. According to the generalized impulse response functions, the coal price reacts positively to shocks in crude oil prices.

An Empirical Investigation of Factors Affecting Stock Prices in Vietnam

2014 ◽  
pp. 74-89 ◽  
Author(s):  
Vinh Vo Xuan
Keyword(s):  
Exchange Rates ◽  
Crude Oil ◽  
Foreign Exchange ◽  
Stock Prices ◽  
Global Financial Crisis ◽  
Oil Prices ◽  
Foreign Exchange Rates ◽  
Factors Affecting ◽  
Crude Oil Prices ◽  
Before And After

This paper investigates factors affecting Vietnam’s stock prices including US stock prices, foreign exchange rates, gold prices and crude oil prices. Using the daily data from 2005 to 2012, the results indicate that Vietnam’s stock prices are influenced by crude oil prices. In addition, Vietnam’s stock prices are also affected significantly by US stock prices, and foreign exchange rates over the period before the 2008 Global Financial Crisis. There is evidence that Vietnam’s stock prices are highly correlated with US stock prices, foreign exchange rates and gold prices for the same period. Furthermore, Vietnam’s stock prices were cointegrated with US stock prices both before and after the crisis, and with foreign exchange rates, gold prices and crude oil prices only during and after the crisis.

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