Impacts of Changing Crude Oil Prices and Macroeconomic Conditions on Short-Term Output Fluctuations in Japan

2009 ◽  
Vol 36 (2) ◽  
pp. 103-114
Author(s):  
Yu Hsing
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Short Term ◽  
Macroeconomic Conditions ◽  
Output Fluctuations ◽  
Crude Oil Prices

scholarly journals Do Tense Geopolitical Factors Drive Crude Oil Prices?

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4277
Author(s):  
Fen Li ◽  
Zhehao Huang ◽  
Junhao Zhong ◽  
Khaldoon Albitar
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Causal Link ◽  
Short Term ◽  
Causality Test ◽  
Dynamic Correlation ◽  
Crude Oil Prices ◽  
Political Events ◽  
Real Factor ◽  
Copula Approach

Geopolitical factors are considered a crucial factor that makes a difference in crude oil prices. Over the last three decades, many political events occurred frequently, causing short-term fluctuations in crude oil prices. This paper aims to examine the dynamic correlation and causal link between geopolitical factors and crude oil prices based on data from June 1987 to February 2020. By using a time-varying copula approach, it is shown that the correlation between geopolitical factors and crude oil prices is strong during periods of political tensions. The GPA (geopolitical acts) index, as the real factor, drives the rise in prices of crude oil. Moreover, the dynamic correlation between geopolitical factors and crude oil prices shows strong volatility over time during periods of political tensions. We also found unidirectional causality running from geopolitical factors to crude oil prices by using the Granger causality test.

scholarly journals Smart System for Detecting Anomalies In Crude Oil Prices Using Long Short-Term Memory

2021 ◽  
Vol 10 (6) ◽  
pp. 3122-3127
Keyword(s):  
Crude Oil ◽  
Short Term Memory ◽  
Oil Prices ◽  
Experimental Result ◽  
Short Term ◽  
Smart System ◽  
Term Memory ◽  
Crude Oil Prices ◽  
Proposed Model ◽  
Long Short Term Memory

Crude oil is leading globally, as it represents roughly about 33% of the total energy consumed globally. It is one of the most significant exchanged resources in the world, oil in one way or the other affects our day to day routines, like transportation, cooking and power, and other numerous petrochemical items going from the things we use to the things we wear. The increment sought after for petroleum derivatives is on a persistent ascent, making it vital for the oil and gas industry to think of new methodologies for further developing activity. This paper presents a smart system for detecting anomalies in crude oil prices. The experimental process of the proposed system is of two phases. The first phase has to do with the pre-processing stage, and the training stage while the second phase of the experiment has to do with the building/training of the Long Short-Term Memory algorithm. The experimental result shows that LSTM model had an accuracy result of 98%. The result further shows that our proposed model is under fitting since the training loss is lesser than the validation loss. The proposed model was saved and was used in detecting anomalies of the crude oil prices ranging from 1990 to 2020.

scholarly journals Selection of Machine Learning Models for Oil Price Forecasting: Based on the Dual Attributes of Oil

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Lei Yan ◽  
Yuting Zhu ◽  
Haiyan Wang
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Short Term Memory ◽  
Oil Prices ◽  
Principal Component ◽  
Oil Price ◽  
Locally Linear Embedding ◽  
Short Term ◽  
Crude Oil Prices

Since the commodity and financial attributes of crude oil will have a long-term or short-term impact on crude oil prices, we propose a de-dimension machine learning model approach to forecast the international crude oil prices. First, we use principal component analysis (PCA), multidimensional scale (MDS), and locally linear embedding (LLE) methods to reduce the dimensions of the data. Then, based on the recurrent neural network (RNN) and long-term and short-term memory (LSTM) models, we build eight models for predicting the future and spot prices of international crude oil. From the analysis and comparison of the prediction results, we find that reducing the dimension of the data can improve the accuracy of the model and the applicability of RNN and LSTM models. In addition, the LLE-RNN/LSTM models can most successfully capture the nonlinear characteristics of crude oil prices. When the moving window size is twenty, that is, when crude oil price data are lagging by almost a month, each model can minimize its error, and the LLE-RNN /LSTM models have the best robustness.

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.

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