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.

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 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.

scholarly journals Heterogeneous Causal Relationships between Spot and Futures Oil Prices: Evidence from Quantile Causality Analysis

2019 ◽  
Vol 11 (5) ◽  
pp. 1359
Author(s):  
Xianfang Su ◽  
Huiming Zhu ◽  
Xinxia Yang
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Causal Relationships ◽  
Causality Analysis ◽  
Futures Prices ◽  
Spot Prices ◽  
Oil Market ◽  
Crude Oil Prices ◽  
Heterogeneous Effects ◽  
Quantile Causality

The causal relationships between spot and futures crude oil prices have attracted the attention of many researchers in the past several decades. Most of the studies, however, do not distinguish among the various oil market situations in analyses of linear and nonlinear causalities. In light of the fact that a booming or depressing oil market produces heterogeneous investment behaviors, this study applied a quantile causality framework to capture different causalities across various quantile levels and found that the causal relationships between crude oil spot and futures prices significantly derive from tail quantile intervals and appear as heterogeneous effects. Before the Iraq War, crude oil spot and futures prices were mutually Granger-caused at lower quantile levels, and only futures prices led spot prices at upper quantile levels. Since the war, a clear bidirectional causality has existed at the upper quantile levels, but only in lower quantile levels have futures prices led spot prices. These results provide useful information to investors using crude spot or futures prices to hedge or manage downside or upside risks in their portfolios.

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.

Common Trends and Common Cycles in Oil Price and Real Exchange Rate

2014 ◽  
Vol 14 (2) ◽  
pp. 249-263 ◽  
Author(s):  
Hem C. Basnet ◽  
Puneet Vatsa ◽  
Subhash Sharma
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Crude Oil ◽  
Real Exchange Rates ◽  
Oil Prices ◽  
Oil Price ◽  
Cycle Decomposition ◽  
The Real ◽  
Crude Oil Prices ◽  
Common Cycles

This study explores the long- and short-run movement between oil prices and the real exchange rates of two large oil-exporting countries – Canada and Norway. Cointegration and serial correlation common features tests are jointly used to identify the long-term common trend and short-term common cycles. Our test results find that oil prices and the real exchange rates of the Canadian Dollar and the Norwegian Krone have two shared trends and one shared cycle. The trend–cycle decomposition shows a great deal of positive comovement among the trend and cyclical components. The two currencies show economic dynamics very similar to crude oil prices. They do not exhibit any qualitative differences in the trajectory of the trend and cycles when controlling for different crude oil prices. Our results indicate that oil price fluctuations play significant role in explaining the exchange rate movements of oil-exporting countries.

Russia's Oil Prices: Moving toward the Market

1995 ◽  
Vol 13 (6) ◽  
pp. 553-582
Author(s):  
Eugene M. Khartukov
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Oil Industry ◽  
World Market ◽  
Market Pricing ◽  
Oil Market ◽  
Crude Oil Prices ◽  
Domestic Prices ◽  
Feasible Alternative ◽  
Oil And Oil Products

The painful perestroika of the ex-Soviet oil industry has been accompanied by an accelerated transition from the previous all-embracing and inflexible price control to actually decontrolled market pricing for both crude oil and oil products. The freed and soaring oil prices quickly hit equilibrium levels, led to sizeable contraction of inland oil demand, and generated two interrelated crises of nonpayment and overproduction. Rising transportation costs resulted in spontaneous “regionalization” of the national oil market and made oil product imports a feasible alternative to long-haul domestic supplies. While retail product prices became comparable with those in some Western countries, the backwardness of the country's refining industry and the resultant low gross product worth still are keeping domestic prices of crude oil substantially below world market parities. Though the rapid “globalization” of internal crude oil prices is on the Russian government's agenda, an immediate rise to world price levels is neither desirable nor actually possible.

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 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.

Oil prices complicate Latin America outlook

2015 ◽  
Keyword(s):  
Latin America ◽  
Crude Oil ◽  
Investment Decisions ◽  
Oil Prices ◽  
Price Impact ◽  
Oil Price ◽  
Content Type ◽  
Crude Oil Prices ◽  
The World ◽  
Alternative Energies

Subject 'Winners' and 'losers' from the recent collapse in oil prices. Significance The recent precipitate fall in crude oil prices, with the Brent crude price falling below 50 dollars/barrel in January (less than half its September 2014 level), is clearly having a major impact around the world. In Latin America, which includes both oil importing and exporting countries, there will be winners and losers from this development, although in some cases the oil price impact is likely to prove more nuanced. Impacts Plunging oil prices are compounding doubts surrounding the regional hydrocarbons sector. The effect on investment decisions will have a longer-term impact on the region. The development of alternative energies in Latin America will be hit by the lower prices.

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