scholarly journals Investigating the Behavior of Iran's Light Crude Oil Price in Short Term

2016 ◽  
Vol 10 (3) ◽  
pp. 45
Author(s):  
Seyed Abdollah Razavi ◽  
Mostafa Salimifar ◽  
Seyed Mahdi Mostafavi ◽  
Mortaza Baky Haskuee
Keyword(s):  
Crude Oil ◽  
Price Volatility ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Short Term ◽  
Light Crude Oil ◽  
Oil Market ◽  
Oil Price Volatility

<p class="zhengwen">Investigate the causes of changing the oil price and modeling for predicting its volatility has always been one of the most important fields of Iran's economic literature study due to its position in Iran's economy. On the other hand, oil price volatility lead to the difficulty in the development programs. Empirical studies show that oil prices volatility are caused the structural bottlenecks (trade balance bottleneck, budget bottlenecks, etc.) in Iran's economic.<strong></strong></p>Understanding the mechanism of oil prices formation can reduce the risk of oil price volatility and its negative impacts on Iran's economy. With the development of oil bourse and oil futures market, oil market changed the crude oil price formation so that the cash flow between financial markets and oil market will deviant the crude oil price from its long term direction by changing in interest rate in short-term. In this paper, it is investigated the crude oil price deviation from its long-term direction with regard to the relationship between mentioned markets in short-term. For this purpose, Fisher price jump model and Frankel theory will be used for test by using daily time series data of 2005-13 about Iran's light crude oil in different areas (different markets), as well as multivariate GARCH technique method. Also, the results show that the pricing strategy is false signal in the use of Urals crude oil in the determining of crude oil price in the Mediterranean and North West Europe markets.

scholarly journals Oil threshold value between oil price and production

2019 ◽  
Vol 66 (4) ◽  
pp. 487-505 ◽  
Author(s):  
Tzu-Yi Yang ◽  
Ha Thanh ◽  
Chia-Wei Yen
Keyword(s):  
Crude Oil ◽  
Price Volatility ◽  
Oil Prices ◽  
Oil Production ◽  
Threshold Value ◽  
Oil Price ◽  
Smooth Transition ◽  
Crude Oil Price ◽  
Oil Price Volatility ◽  
Crude Oil Production

This study proposes a panel smooth transition regression (PSTR) model to investigate the nonlinear relationship between crude oil prices and crude oil production in 122 countries, both OPEC and non-OPEC, from March 1994 to October 2015. The statistical test for the existence of a threshold effect indicates that the relationship between oil prices and oil production is nonlinear, with different changes over time among the oil price and transition variables. Additionally, a threshold value exists. Furthermore, crude oil price volatility exhibits asymmetric responses to production volatility by fluctuating above or below the threshold value. Finally, when crude oil price volatility with a lag of two periods exceeds the threshold value, crude oil production changes have a positive impact on crude oil price volatility. In contrast, when crude oil price volatility with a lag of two periods is less than the threshold value, crude oil production changes have a negative impact on price volatility.

Crude Oil Price and Food Security Related Variables in Malaysia

2016 ◽  
Vol 4 (2) ◽  
pp. 17-21
Author(s):  
Shri Dewi Applanaidu ◽  
Mukhriz Izraf Azman Aziz
Keyword(s):  
Food Security ◽  
Crude Oil ◽  
Price Index ◽  
Price Volatility ◽  
Food Price ◽  
Oil Price ◽  
Crude Oil Price ◽  
Price Changes ◽  
Oil Price Volatility ◽  
Crude Oil Prices

Objective - This study analyzes the dynamic relationship between crude oil price and food security related variables (crude palm oil price, exchange rate, food import, food price index, food production index, income per capita and government development expenditure) in Malaysia using a Vector Auto Regressive (VAR) model. Methodology/Technique - The data covered the period of 1980-2014. Impulse response functions (IRFs) was applied to examine what will be the results of crude oil price changes to the variables in the model. To explore the impact of variation in crude oil prices on the selected food security related variables forecast error variance decomposition (VDC) was employed. Findings - Findings from IRFs suggest there are positive effects of oil price changes on food import and food price index. The VDC analyses suggest that crude oil price changes have relatively largest impact on real crude palm oil price, food import and food price index. This study would suggest to revisiting the formulation of food price policy by including appropriate weight of crude oil price volatility. In terms of crude oil palm price determination, the volatility of crude oil prices should be taken into account. Overdependence on food imports also needs to be reduced. Novelty - As the largest response of crude oil price volatility on related food security variables food vouchers can be implemented. Food vouchers have advantages compared to direct cash transfers since it can be targeted and can be restricted to certain types of products and group of people. Hence, it can act as a better aid compared cash transfers. Type of Paper - Empirical Keywords: Crude oil price, Food security related variables, IRF, VAR, VDC

The Co-Movements Between Crude Oil Price and Internet Concerns: Causality Analysis in the Frequency Domain

2020 ◽  
Vol 8 (3) ◽  
pp. 224-239
Author(s):  
Jingjing Li ◽  
Ling Tang ◽  
Ling Li
Keyword(s):  
Crude Oil ◽  
Test Method ◽  
Oil Price ◽  
Spot Price ◽  
Causality Analysis ◽  
Crude Oil Price ◽  
Causality Test ◽  
Low Frequencies ◽  
Oil Market

AbstractWith the boom of web technology, Internet concerns (IC) have become emerging drivers of crude oil price. This paper makes the first attempt to measure the frequency-varying co-movements between crude oil price and IC in five domains (i.e., fundamentals, supply-demand, crisis, war and weather) by using the frequency causality test method. Based on the monthly Brent spot price and search volumes (SVs) captured by Google Trends from January 2004 to September 2019, new and complementary insights regarding the co-movements between crude oil price and IC are obtained. 1) The co-movements between crude oil price and the IC of supply-demand, war, and weather support a neutral hypothesis at all frequencies due to the characteristics (low value or volatility) of these IC data. 2) There is a unidirectional causal relationship between crude oil price and the IC of fundamentals, running from the latter to the former at low frequencies (long-term). 3) There is a feedback relationship between crude oil price and the IC of crisis, with the IC of crisis driving crude oil price at medium and low frequencies (mid- and long-term) and crude oil price causing the IC of crisis to change permanently. The conclusions of this paper provide important implications for both oil market economists and investors.

scholarly journals Oil Price Forecasting Using a Time-Varying Approach

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1403
Author(s):  
Lu-Tao Zhao ◽  
Shun-Gang Wang ◽  
Zhi-Gang Zhang
Keyword(s):  
Crude Oil ◽  
Time Window ◽  
Oil Prices ◽  
Oil Price ◽  
Price Forecasting ◽  
Time Varying ◽  
Crude Oil Price ◽  
Trend Prediction ◽  
Oil Market ◽  
Polynomial Decomposition

The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.

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