Crude Oil Price and Fuel Ethanol Firm Profit

2015 ◽  
Vol 737 ◽  
pp. 132-135
Author(s):  
Jian Hua Li
Keyword(s):  
Crude Oil ◽  
Environmental Protection ◽  
Fuel Ethanol ◽  
Oil Price ◽  
Raw Material ◽  
Crude Oil Price ◽  
Ethanol Fuel ◽  
Gasoline Price ◽  
The Relationship ◽  
Firm Profit

This paper analyzes the relationship between crude oil price and fuel ethanol firm profit. Corn is the main raw material for fuel ethanol in China. According to our survey of fuel ethanol firms, three tons of corn input can produce one ton of fuel ethanol output technically, so we can estimate cost of fuel ethanol firms. Government of some provinces mandated gasoline with 10% ethanol fuel for environmental protection. Sinopec and PetroChina paid to the fuel ethanol firms in accordance with 0.911 times of gasoline price, so we can estimate earnings. Government can determine the subsidies of fuel ethanol firms based on estimation of their profit.

What matters for consumer sentiment in the euro area? World crude oil price or retail gasoline price?

2021 ◽  
pp. 105743
Author(s):  
Sofronis Clerides ◽  
Styliani-Iris Krokida ◽  
Neophytos Lambertides ◽  
Dimitris Tsouknidis
Keyword(s):  
Crude Oil ◽  
Euro Area ◽  
Oil Price ◽  
Consumer Sentiment ◽  
Crude Oil Price ◽  
Gasoline Price ◽  
Retail Gasoline ◽  
What Matters

Protocol to Assess the Impact of Crude Oil Price Fluctuations on Future Asphalt Prices

2021 ◽  
pp. 036119812199207
Author(s):  
Kaylyn M. Cardinal ◽  
Mohamed Khalafalla ◽  
Jorge Rueda-Benavides
Keyword(s):  
Crude Oil ◽  
Price Index ◽  
Oil Price ◽  
Crude Oil Price ◽  
Statistical Process ◽  
Economic Implications ◽  
Price Fluctuations ◽  
Time Gap ◽  
The Impact ◽  
The Relationship

It is clear for the transportation industry that asphalt prices are heavily affected by changes in the crude oil market. This occurs because asphalt is a byproduct of the process of refining crude oil. However, there is still a lack of research on assessing the economic implications of this relationship. This paper assesses those implications through an innovative statistical process designed to quantify the economic correlation between asphalt and crude oil price fluctuations in Alabama. The proposed statistical process is used in this paper to model the relationship between the Alabama Department of Transportation’s (ALDOT’s) monthly asphalt price index and a national crude oil index published by the US Energy Information Administration. The process quantifies the relationship between these two commodities in relation to two metrics: (1) the time gap between an observed change in the crude oil index and its corresponding impact on the asphalt price index and (2) the magnitude of that impact. It was found that the most likely time gap between crude oil and asphalt price fluctuations in Alabama is 3 months, with a change ratio of 0.58. This means that a 1% increase in the price of crude oil would most likely affect the Alabama asphalt market 3 months later with a price increase of about 0.58%. Recognizing that these are just average values, the paper also presents a risk assessment tool that provides ALDOT with the probability of occurrence of different scenarios taking into consideration the observed variability in time gaps and change ratios.

scholarly journals Relationship between International Crude Oil Price and Indian Stock Market (NSE)

2011 ◽  
pp. 63-73
Author(s):  
Rajendra Mahunta
Keyword(s):  
Crude Oil ◽  
Global Economy ◽  
Stock Exchange ◽  
Oil Prices ◽  
Oil Price ◽  
Raw Material ◽  
Crude Oil Price ◽  
New Era ◽  
Crude Oil Prices

In this new era of economic growth, the exceptional increase in the crude oil prices is one of the significant developments that affect the global economy. Crude oil is an important raw material used for manufacturing sectors, so that increase in the price of oil is bound to warn the economy with inflationary inclination. The study examine the long-term relationships between CNX NIFTY FIFTY index of National Stock Exchange and crude price by using various econometric test. The surge in crude oil prices during recent years has generated a lot of interest in the relationship between oil price and equity markets. The study covers the period between 01.01.2010 and 31.12.2014 and was performed with data consisting of 1245 days. The empirical results show there was a cointegrated long-term relationship between CNX index and crude price. Granger causality results reveal that there is unidirectional causality exists and crude oil price causes NSE (CNX) but NSE (CNX) does not cause oil price.

scholarly journals Forecasting model for crude oil price with structural break

2017 ◽  
Vol 13 (4-1) ◽  
pp. 421-424
Author(s):  
Norshela Mohd Noh ◽  
Arifah Bahar ◽  
Zaitul Marlizawati Zainuddin
Keyword(s):  
Brownian Motion ◽  
Crude Oil ◽  
Structural Break ◽  
Geometric Brownian Motion ◽  
Oil Price ◽  
Raw Material ◽  
Crude Oil Price ◽  
Uhlenbeck Process ◽  
Ornstein Uhlenbeck Process ◽  
Material Procurement

Nowadays, in unstable economic environment, oil refining company is facing fluctuating crude oil price that causes unstable profit margin. Fluctuating crude oil price leads to difficulty in forecasting raw material procurement. Inaccurate forecast leads to inefficient decision making in optimizing refining company profit margin. In order to overcome an inaccurate in forecasting raw material procurement, an appropriate study of forecasting model is needed. Thus the objective of this study is to model fluctuating crude oil price based on geometric Brownian motion and mean reverting Ornstein-Uhlenbeck process and also to forecast fluctuating crude oil price with structural break. In modeling crude oil price, the information on whether the structural break exists is very crucial due to the long memory property might be camouflaged by the existence of the structural break. In this study, we employed long memory test to West Texas Intermediate (WTI) daily data from 2nd January 1986 to 31st August 2016 using log periodogram regression of Geweke and Porter-Hudak (1983). Bai and Perron test was applied to find break date. The result indicates that crude oil price is characterized by structural breaks. With the assumption that future price is affected by today’s price, we modeled and forecasted crude oil price using geometric Brownian motion and mean reverting Ornstein-Uhlenbeck process for 14 days, 30 days and 6 months. Results showed that forecasting crude oil price is highly accurate for short term with geometric Brownian motion compared to mean reverting Ornstein-Uhlenbeck process. 

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