Oil and Economy: Factors Influencing Crude Oil Prices and the Significance of these on the Global Economy

2013 ◽  
Author(s):  
Ahmed Sohaib Zafar Khan
Keyword(s):  
Crude Oil ◽  
Global Economy ◽  
Oil Prices ◽  
Factors Influencing ◽  
Crude Oil Prices

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 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 Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3603 ◽  
Author(s):  
Taiyong Li ◽  
Yingrui Zhou ◽  
Xinsheng Li ◽  
Jiang Wu ◽  
Ting He
Keyword(s):  
Crude Oil ◽  
Ridge Regression ◽  
Global Economy ◽  
Evaluation Criteria ◽  
Oil Prices ◽  
Ensemble Empirical Mode Decomposition ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Crude Oil Prices

As one of the leading types of energy, crude oil plays a crucial role in the global economy. Understanding the movement of crude oil prices is very attractive for producers, consumers and even researchers. However, due to its complex features of nonlinearity and nonstationarity, it is a very challenging task to accurately forecasting crude oil prices. Inspired by the well-known framework “decomposition and ensemble” in signal processing and/or time series forecasting, we propose a new approach that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), differential evolution (DE) and several types of ridge regression (RR), namely, ICEEMDAN-DE-RR, for more accurate crude oil price forecasting in this paper. The proposed approach consists of three steps. First, we use the ICEEMDAN to decompose the complex daily crude oil price series into several relatively simple components. Second, ridge regression or kernel ridge regression is employed to forecast each decomposed component. To enhance the accuracy of ridge regression, DE is used to jointly optimize the regularization item, the weights and parameters of each single kernel for each component. Finally, the predicted results of all components are aggregated as the final predicted results. The publicly available West Texas Intermediate (WTI) daily crude oil spot prices are used to validate the performance of the proposed approach. The experimental results indicate that the proposed approach can achieve better performance than some state-of-the-art approaches in terms of several evaluation criteria, demonstrating that the proposed ICEEMDAN-DE-RR is very promising for daily crude oil price forecasting.

scholarly journals A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yingrui Zhou ◽  
Taiyong Li ◽  
Jiayi Shi ◽  
Zijie Qian
Keyword(s):  
Crude Oil ◽  
Global Economy ◽  
Oil Prices ◽  
Ensemble Empirical Mode Decomposition ◽  
Gradient Boosting ◽  
Intrinsic Mode Functions ◽  
Crude Oil Prices ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Extreme Gradient Boosting

Crude oil is one of the most important types of energy for the global economy, and hence it is very attractive to understand the movement of crude oil prices. However, the sequences of crude oil prices usually show some characteristics of nonstationarity and nonlinearity, making it very challenging for accurate forecasting crude oil prices. To cope with this issue, in this paper, we propose a novel approach that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and extreme gradient boosting (XGBOOST), so-called CEEMDAN-XGBOOST, for forecasting crude oil prices. Firstly, we use CEEMDAN to decompose the nonstationary and nonlinear sequences of crude oil prices into several intrinsic mode functions (IMFs) and one residue. Secondly, XGBOOST is used to predict each IMF and the residue individually. Finally, the corresponding prediction results of each IMF and the residue are aggregated as the final forecasting results. To demonstrate the performance of the proposed approach, we conduct extensive experiments on the West Texas Intermediate (WTI) crude oil prices. The experimental results show that the proposed CEEMDAN-XGBOOST outperforms some state-of-the-art models in terms of several evaluation metrics.

scholarly journals Crude oil price prediction using CEEMDAN and LSTM-attention with news sentiment index

2021 ◽  
Vol 76 ◽  
pp. 28
Author(s):  
Zhenda Hu
Keyword(s):  
Crude Oil ◽  
Global Economy ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Oil Prices ◽  
Ensemble Empirical Mode Decomposition ◽  
Attention Mechanism ◽  
Scientific Model ◽  
Crude Oil Prices ◽  
News Sentiment

Crude oil is one of the most powerful types of energy and the fluctuation of its price influences the global economy. Therefore, building a scientific model to accurately predict the price of crude oil is significant for investors, governments and researchers. However, the nonlinearity and nonstationarity of crude oil prices make it a challenging task for forecasting time series accurately. To handle the issue, this paper proposed a novel forecasting approach for crude oil prices that combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Long Short-Term Memory (LSTM) with attention mechanism and addition, following the well-known “decomposition and ensemble” framework. In addition, a news sentiment index based on Chinese crude oil news texts was constructed and added to the prediction of crude oil prices. And we made full use of attention mechanism to better integrate price series and sentiment series according to the characteristics of each component. To validate the performance of the proposed CEEMDAN-LSTM_att-ADD, we selected the Mean Absolute Percent Error (MAPE), the Root Mean Squared Error (RMSE) and the Diebold-Mariano (DM) statistic as evaluation criterias. Abundant experiments were conducted on West Texas Intermediate (WTI) spot crude oil prices. The proposed approach outperformed several state-of-the-art methods for forecasting crude oil prices, which proved the effectiveness of the CEEMDAN-LSTM_att-ADD with the news sentiment index.

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.

Determinants of Inflation in Vietnam: A VECM Approach

2015 ◽  
Vol 22 (04) ◽  
pp. 26-50
Author(s):  
Ngoc Tran Thi Bich ◽  
Huong Pham Hoang Cam
Keyword(s):  
Interest Rate ◽  
Crude Oil ◽  
Error Correction ◽  
Budget Deficit ◽  
Oil Prices ◽  
Crude Oil Prices ◽  
Domestic Credit ◽  
Short Run ◽  
The Impact

This paper aims to examine the main determinants of inflation in Vietnam during the period from 2002Q1 to 2013Q2. The cointegration theory and the Vector Error Correction Model (VECM) approach are used to examine the impact of domestic credit, interest rate, budget deficit, and crude oil prices on inflation in both long and short terms. The results show that while there are long-term relations among inflation and the others, such factors as oil prices, domestic credit, and interest rate, in the short run, have no impact on fluctuations of inflation. Particularly, the budget deficit itself actually has a short-run impact, but its level is fundamentally weak. The cause of the current inflation is mainly due to public's expectations of the inflation in the last period. Although the error correction, from the long-run relationship, has affected inflation in the short run, the coefficient is small and insignificant. In other words, it means that the speed of the adjustment is very low or near zero. This also implies that once the relationship among inflation, domestic credit, interest rate, budget deficit, and crude oil prices deviate from the long-term trend, it will take the economy a lot of time to return to the equilibrium state.

Dynamic Relationship between Crude Oil and Stock Prices in India: Before And After the Subprime Financial Crisis 2008

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 96-104
Author(s):  
P. Sakthivel ◽  
S. Rajaswaminathan ◽  
R. Renuka ◽  
N. R.Vembu
Keyword(s):  
Financial Crisis ◽  
Crude Oil ◽  
Stock Prices ◽  
Oil Prices ◽  
Energy Index ◽  
Data Set ◽  
Dynamic Relationship ◽  
Crude Oil Prices ◽  
Long Run ◽  
Before And After

This paper empirically discovered the inter-linkages between stock and crude oil prices before and after the subprime financial crisis 2008 by using Johansan co-integration and Granger causality techniques to explore both long and short- run relationships.  The whole data set of Nifty index, Nifty energy index, BSE Sensex, BSE energy index and oil prices are divided into two periods; before crisis (from February 15, 2005 to December31, 2007) and after crisis (from January 1, 2008 to December 31, 2018) are collected and analyzed. The results discovered that there is one-way causal relationship from crude oil prices to Nifty index, Nifty energy index, BSE Sensex and BSE energy index but not other way around in both periods. However, a bidirectional causality relationship between BSE Energy index and crude oil prices during post subprime financial crisis 2008. The co-integration results suggested that the absence of long run relationship between crude oil prices and market indices of BSE Sensex, BSE energy index, Nifty index and Nifty energy index before and after subprime financial crisis 2008.

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