Crude oil price hikes and issues for energy security for Southern Africa

2010 ◽  
Vol 21 (2) ◽  
pp. 12-16 ◽  
Author(s):  
Jabavu Clifford Nkomo
Keyword(s):  
Crude Oil ◽  
Southern Africa ◽  
Energy Security ◽  
Oil Price ◽  
Time Lags ◽  
Oil Market ◽  
Price Increases ◽  
Supply Shocks ◽  
Crude Oil Prices ◽  
Potential Use

This paper addresses a number of issues related to crude oil prices, focusing on Southern Africa. It begins by analysing oil price movements from 1970 to 2008, and examines various factors that may have contributed to the sharp rise and fall in prices. A characteristic feature in the oil market is the time lags it takes to react to price changes. A high oil intensity of GDP makes the economy vulnerable to oil price increases, so that countries with a high oil/GDP ratio are harder hit than others. There are two main issues for energy security: first, on whether the potential use of the oil weapon can be taken seriously; and second, how to minimize vulnerability to oil supply shocks by reducing oil dependence and by a developing or enlarging a strategic stockpile of oil.

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.

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

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

scholarly journals A Research on Markov Model and Hidden Markov Model in Crude Oil Price Forecasting for PETRONAS Malaysia

2019 ◽  
Vol 8 (12S2) ◽  
pp. 121-125 ◽  
Keyword(s):  
Markov Model ◽  
Crude Oil ◽  
Hidden Markov Model ◽  
Structural Equation ◽  
Hidden Markov ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Accurate Forecast

Crude oil price forecasting is an essential component of sustainable development of many countries as crude oil is an unavoidable product that exists on earth. In this paper, a model based on a hidden Markov model and Markov model for crude oil price forecasting was developed, and their relative performance was compared. Path analysis of Structural Equation Modelling was employed to model the effects of forecasted prices and the actual crude oil price to get the most accurate forecast. The key variables used to develop the models were monthly crude oil prices s from PETRONAS Malaysia. It was found that the hidden Markov model was more accurate than the Markov model in forecasting the crude oil price. The findings of this study show that the hidden Markov model is a potentially promising method of crude oil price forecasting that merit further study.

scholarly journals Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

2019 ◽  
Vol 66 (3) ◽  
pp. 363-388
Author(s):  
Serkan Aras ◽  
Manel Hamdi
Keyword(s):  
Neural Networks ◽  
Crude Oil ◽  
Significant Interaction ◽  
Statistical Tests ◽  
Oil Price ◽  
Training Data ◽  
Forecasting Model ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Selection Of

When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test and compare its performance with two general methods, namely, using all available data and using just two years of data. Furthermore, the importance of the selection of the final neural network model is investigated in detail. The results obtained from daily crude oil prices indicate that the data from the last structural change lead to simpler architectures of neural networks and have an advantage in reaching more accurate forecasts in terms of MAE value. In addition, the statistical tests show that there is a statistically significant interaction between data size and stopping rule.

scholarly journals PENGARUH NILAI TUKAR DAN HARGA MINYAK MENTAH DUNIA TERHADAP RETURN SAHAM PT. INDOMOBIL SUKSES INTERNASIONAL TBK. DAN PT. ASTRA INTERNASIONAL TBK. TAHUN 2006-2016

2017 ◽  
Vol 1 (2) ◽  
pp. 61
Author(s):  
Arif Fadlilah ◽  
Sri Hermuningsih
Keyword(s):  
Exchange Rates ◽  
Crude Oil ◽  
Stock Prices ◽  
Stock Return ◽  
T Test ◽  
Oil Price ◽  
Crude Oils ◽  
Crude Oil Price ◽  
F Test ◽  
Crude Oil Prices

This research is meant to find out the influence of exchange rates and crude oil price either simultaneous or partial to the stock return at PT. Indomobil Sukses Internasional Tbk. and PT Astra Internasional Tbk. The data which is applied in this research is the automotive companies’ stock prices, Rupiah exchange rates, and crude oil price from 2006 to 2016. The multiple linear regressions are applied as the analysis technique by carrying out F test and t test. Based on the F test it is found that simultaneously the rupiah exchange rates and crude oil prices have influence to the stock return. Based on the t test it is found that partially the rupiah exchange rates have no influence to PT. Indomobil Sukses Internasional Tbk stock return but have influence to PT. Astra Internasional Tbk stock return and crude oils prices have influence to stock return. t test indicates the dominant influence to the stock return PT. Indomobil Sukses International Tbk is crude oils variable and stock return PT. Astra International Tbk is exchange rates variable

scholarly journals “Shale Revolution” in the United States as the Main Driver of the World Oil Market Transformation

2019 ◽  
Vol 12 (6) ◽  
pp. 71-93
Author(s):  
S. A. Zolina ◽  
I. A. Kopytin ◽  
O. B. Reznikova
Keyword(s):  
United States ◽  
Crude Oil ◽  
Oil Production ◽  
The United States ◽  
Oil Price ◽  
Oil Market ◽  
Oil Exports ◽  
The World ◽  
Oil Sector ◽  
The U.S

In 2018 the United States surpassed Saudi Arabia and Russia to become the largest world oil producer. The article focuses on the mechanisms through which the American shale revolution increasingly impacts functioning of the world oil market. The authors show that this impact is translated to the world oil market mainly through the trade and price channels. Lifting the ban on crude oil exports in December 2015 allowed the United States to increase rapidly supply of crude oil to the world oil market, the country’s share in the world crude oil exports reached 4,4% in 2018 and continues to rise. The U.S. share in the world petroleum products exports, on which the American oil sector places the main stake, reached 18%. In parallel with increasing oil production the U.S. considerably shrank crude oil import that forced many oil exporters to reorient to other markets. Due to high elasticity of tight oil production to the oil price increases oil from the U.S. has started to constrain the world oil price from above. According to the majority of authoritative forecasts, oil production in the U.S. will continue to increase at least until 2025. Since 2017 the tendency to the increasing expansion of supermajors into American unconventional oil sector has become noticeable, what will contribute to further strengthening of the U.S. position in the world oil market and accelerate its restructuring.  

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.

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