scholarly journals The Effect of Crude Oil Price on Merchandise Trade: Evidence from East Asia and Pacific

2021 ◽  
Vol 2 (8) ◽  
pp. 558-568
Author(s):  
Bhenu Artha ◽  
Bahri ◽  
Cahya Purnama Asri ◽  
Ardhi Khairi ◽  
Fikri Alamsyah
Keyword(s):  
Crude Oil ◽  
East Asia ◽  
Durable Goods ◽  
Quantitative Methods ◽  
Linear Regression Analysis ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Economic Activities ◽  
Merchandise Trade

One of the most important production inputs is energy, particularly crude oil. The impacts of oil price fluctuations on global trade flows can be understood by the uncertainty channel, fluctuations in oil prices may create uncertainty about the future path of the oil price, causing consumers to postpone irreversible purchases of consumer durable goods, and also causing firms to postpone irreversible investments, and also recent hikes and fluctuations in oil prices since 1999 have attracted attention and invoked concerns about their devastating effects on a variety of economic activities. The objective of this research is to determine the influence of crude oil price to merchandise trade in East Asia and Pacific. This research uses quantitative methods and linear regression analysis. The results of the analysis show that there is negative and significance effect of crude oil price on merchandise trade in East Asia and Pacific for the period 1987 – 2019.

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 Daqing Crude Oil Price Forecast Based on the ARIMA Model

2015 ◽  
Vol 8 (1) ◽  
pp. 457-462
Author(s):  
Li Quan
Keyword(s):  
Crude Oil ◽  
Arima Model ◽  
Oil Prices ◽  
Industrial Country ◽  
Oil Price ◽  
Crude Oil Price ◽  
Major Industrial Country ◽  
Price Forecast ◽  
Industrial Economy ◽  
Monthly Average

Oil is the lifeblood of the industrial economy, oil prices are affected by many factors. China is a major industrial country, changes in the price of oil will affect many aspects of economic development, and therefore the price of crude oil research is extremely important. In this paper, monthly average prices of crude oil in Daqing from January 2000 to December 2010 are utilized to do the research. Based on ARIMA model by building software using EVIEWS, rule of oil price movements is found and a prediction of oil price is made using the data from the first 10 months of 2011.

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.

The optimal thermal causal path analysis on the relationship between international crude oil price and stock market

Kybernetes ◽  
2018 ◽  
Vol 47 (6) ◽  
pp. 1242-1261 ◽  
Author(s):  
Can Zhong Yao ◽  
Peng Cheng Kuang ◽  
Ji Nan Lin
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Stock Markets ◽  
Oil Prices ◽  
Oil Price ◽  
Chinese Stock Market ◽  
Crude Oil Price ◽  
Content Type ◽  
Crude Oil Prices ◽  
Causal Path

Purpose The purpose of this study is to reveal the lead–lag structure between international crude oil price and stock markets. Design/methodology/approach The methods used for this study are as follows: empirical mode decomposition; shift-window-based Pearson coefficient and thermal causal path method. Findings The fluctuation characteristic of Chinese stock market before 2010 is very similar to international crude oil prices. After 2010, their fluctuation patterns are significantly different from each other. The two stock markets significantly led international crude oil prices, revealing varying lead–lag orders among stock markets. During 2000 and 2004, the stock markets significantly led international crude oil prices but they are less distinct from the lead–lag orders. After 2004, the effects changed so that the leading effect of Shanghai composite index remains no longer significant, and after 2012, S&P index just significantly lagged behind the international crude oil prices. Originality/value China and the US stock markets develop different pattens to handle the crude oil prices fluctuation after finance crisis in 1998.

scholarly journals Impact of FDI inflow, crude oil prices, and economic growth on CO2 emission in Tunisia: Symmetric and asymmetric analysis through ARDL and NARDL approach

2021 ◽  
Vol 12 (1) ◽  
pp. 1-13
Author(s):  
Tarek Ghazouani
Keyword(s):  
Economic Growth ◽  
Crude Oil ◽  
Carbon Emissions ◽  
Co2 Emission ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Fdi Inflows ◽  
Crude Oil Prices ◽  
Long Run

This study explores the symmetric and asymmetric impact of real GDP per capita, FDI inflow, and crude oil price on CO2 emission in Tunisia for the 1972–2016 period. Using the cointegration tests, namely ARDL and NARDL bound test, the results show that the variables are associated in a long run relationship. Long run estimates from both approach confirms the validity of ECK hypothesis for Tunisia. Symmetric analysis reveals that economic growth and the price of crude oil adversely affect the environment, in contrast to FDI inflows that reduce CO2 emissions in the long run. Whereas the asymmetric analysis show that increase in crude oil price harm the environment and decrease in crude oil price have positive repercussions on the environment. The causality analysis suggests that a bilateral link exists between economic growth and carbon emissions and a one-way causality ranges from FDI inflows and crude oil prices to carbon emissions. Thus, some policy recommendations have been formulated to help Tunisia reduce carbon emissions and support economic development.

scholarly journals Industrial production index - crude oil price nexus: Russia, Kazakhstan and Azerbaijan

2020 ◽  
Vol 65 (227) ◽  
pp. 119-141
Author(s):  
Fatih Kaplan ◽  
Ayşe Ünal
Keyword(s):  
Crude Oil ◽  
Frequency Domain ◽  
Industrial Production ◽  
Oil Prices ◽  
Oil Price ◽  
Causality Analysis ◽  
Crude Oil Price ◽  
Causality Test ◽  
Production Index ◽  
Industrial Production Index

The study aims to examine the causality between industrial production index and crude oil price for Russia, Kazakhstan and Azerbaijan by using Frequency Domain Causality Analysis. For this purpose, the monthly data of the industrial production index and Brent oil price data over the period 1993-2019 are used. The Frequency Domain Causality Analysis suggests that the uni-directional causality relationship runs from oil prices to industrial production index is valid in the medium run for Russia and Azerbaijan and in the short run for Kazakhstan. However, there is no uni-directional causality linkage between oil prices and industrial production index in the long run for any of the countries. We hope to contribute to the literature by using frequency-domain causality test which examines the interrelation of crude oil prices on industrial production with the periodicity in these countries. The finding of this study is expected to serve as a tool for industrial production policy.

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 An ARDL Approach on Crude Oil Price and Macroeconomic Variables

2019 ◽  
Vol 4 (1) ◽  
pp. 68-73
Author(s):  
Seuk Yen Phoong ◽  
Seuk Wai Phoong
Keyword(s):  
Economic Growth ◽  
Crude Oil ◽  
Unemployment Rate ◽  
Positive Relationship ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Macroeconomic Variables ◽  
Crude Oil Prices ◽  
Ardl Approach

Objective - The removal of fuel subsidies by the Malaysian government in 2014 has been implement with the managed float system for fuel prices. Methodology/Technique - This study investigates the impact of the managed floating system of crude oil prices on the Malaysian economy using ARDL approach by looking at macroeconomic variables such as inflation, economic growth and unemployment rates. Findings - The results show that all of the variables have short lived relationship with oil prices whereby inflation and economic growth are positively related to oil prices. However, unemployment rate has a negative relationship with the changes of WTI crude oil prices. Novelty - The major input in the economy of Malaysia contributes to a positive relationship between inflation and oil prices, whilst the contribution of Malaysia being an oil-producing country results in the positive relationship of economic growth and oil price. Likewise, as oil prices are high, the increase in demand results in increase in job opportunities. Lastly, the correlation test shows that inflation and economic growth have a high positive correlation while unemployment rate has a low negative correlation with oil price. Type of Paper: Empirical. Keywords: ARDL; Crude Oil Price; GDP; Inflation; Unemployment. JEL Classification: E10, E30, E39. DOI: https://doi.org/10.35609/jber.2019.4.1(8)

scholarly journals MODEL KOMBINASI ARIMA DALAM PERAMALAN HARGA MINYAK MENTAH DUNIA

2018 ◽  
Vol 7 (1) ◽  
pp. 54-63
Author(s):  
Eka Setiyowati ◽  
Agus Rusgiyono ◽  
Tarno Tarno
Keyword(s):  
Crude Oil ◽  
Everyday Life ◽  
Oil Prices ◽  
Oil Price ◽  
Ensemble Method ◽  
Economic Conditions ◽  
Arima Models ◽  
Crude Oil Price ◽  
Crude Oil Prices

Oil is the most important commodity in everyday life, because oil is one of the main sources of energy that is needed for other people. Changes in crude oil prices greatly affect the economic conditions of a country.  Therefore, the aim of this study is develop an appropriate model for forecasting crude oil price based on the ARIMA and its ensembles. In this study, ensemble method uses some ARIMA models to create ensemble members which are then combined with averaging and stacking techniques. The data used are the price of world crude oil period 2003-2017. The results showed that ARIMA (1,1,0) model produces the smallest RMSE values for forecasting the next thirty six months. Keywords: Ensemble, ARIMA, Averaging, Stacking, Crude Oil Price

Sign in / Sign up

Export Citation Format

Share Document