scholarly journals A study of univariate forecasting methods for crude oil price

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mei-Ling Cheng ◽  
Ching-Wu Chu ◽  
Hsiu-Li Hsu
Keyword(s):  
Regression Model ◽  
Crude Oil ◽  
Mean Squared Error ◽  
Moving Average ◽  
Model Performance ◽  
Absolute Error ◽  
Oil Price ◽  
Crude Oil Price ◽  
Content Type ◽  
Forecasting Methods

PurposeThis paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.Design/methodology/approachSix different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.FindingsThe authors found that the grey forecast is a reliable forecasting method for crude oil prices.Originality/valueThe contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

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.

Time-varying dependence measures: a comparative analysis through wavelet approach

2017 ◽  
Vol 11 (2) ◽  
pp. 350-364 ◽  
Author(s):  
Anyssa Trimech
Keyword(s):  
Energy Consumption ◽  
Crude Oil ◽  
Oil Price ◽  
Consumption Pattern ◽  
Time Varying ◽  
Crude Oil Price ◽  
Data Set ◽  
Content Type ◽  
Single Scale ◽  
Measure Of Dependence

Purpose This paper aims to investigate the pattern of dependence between crude oil price and energy consumption of the most important economic sectors in the USA, over different time periods, using monthly data set from January 1986 to July 2014 and a comparative study between linear correlation versus copula correlation as a measure of dependence over the single scale and the multiscale analysis. Design/methodology/approach The proposed method is based on the multiresolution analysis which gives more extensive and detailed description of the dependence price-consumption pattern over different periods of time. Findings The empirical results show that the dependence between variables is strongly sensitive to the time varying and generally increasing with time scale. In particular, the Pearson coefficients are less than the dependence copula measures. The single-scale analysis covers many time-varying dependences which are made clear, flexible and comprehensive by the description given by the multiscale approach. It explains better the structure of relationships between variables and helps understand the variations and improve forecasts of the crude oil price and energy consumption over different time scales. Originality/value The proposed methodology offers the opportunity to construct dynamic management strategies by taking into account the multiscale nature of crude oil price and consumption relationship. Moreover, the paper uses wavelets as a relatively new and powerful tool for statistical analysis in addition to the copula technique that allows a new understanding of variable correlation. The paper will be of interest not only for academics in the field of data dependencies analysis but also for fund managers and market investors.

scholarly journals Exploring relationship between crude oil price volatility and stock indices movement using wavelet analysis: evidence from India and China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shekhar Mishra ◽  
Sathya Swaroop Debasish
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Crude Oil ◽  
Equity Market ◽  
Oil Price ◽  
Discrete Wavelet ◽  
Crude Oil Price ◽  
Content Type ◽  
India And China

Purpose This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China. Design/methodology/approach The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables. Findings The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market. Originality/value To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.

Economics of trickle-down corruption

2020 ◽  
Vol 19 (1) ◽  
pp. 93-102
Author(s):  
Guych Nuryyev ◽  
Charles Hickson
Keyword(s):  
Crude Oil ◽  
Oil And Gas ◽  
Political Elite ◽  
Oil Price ◽  
Crude Oil Price ◽  
Content Type ◽  
State Revenue ◽  
State Income ◽  
Rare Opportunity ◽  
Reduced State

Purpose This study aims to examine the effect of the crude oil price crash of 2014 on corruption decentralisation. In a corrupt state, a significant decrease in the state revenue might lead to concentration of power in the hands of the political elite who try to maintain their income, or to a weakening of the elite’s control as the bureaucrats compete for bribes. Design/methodology/approach Crude oil price crash provides a rare opportunity to test the effect of reduced state revenue on corruption decentralisation. This study constructs a measure for corruption decentralisation and analyses how it is affected by state income in 18 resource-rich and corrupt states. Findings The empirical model suggests that there is a positive relationship between corruption decentralisation and state oil and gas revenue, implying that as the revenue decreases, political elite in the exporting countries manage to maintain their control over the bureaucrats. Originality/value The results are important for academics as well as for policymakers, as they allow adjustment of anti-corruption efforts based on the level of corruption decentralisation.

Crude oil price–shale oil production nexus: a predictability analysis

2020 ◽  
Vol 14 (4) ◽  
pp. 729-744 ◽  
Author(s):  
Sam O. Olofin ◽  
Tirimisiyu Folorunsho Oloko ◽  
Kazeem O. Isah ◽  
Ahamuefula Ephraim Ogbonna
Keyword(s):  
Crude Oil ◽  
Predictive Model ◽  
Good Predictor ◽  
Forecast Accuracy ◽  
Oil Production ◽  
Oil Price ◽  
Shale Oil ◽  
Reverse Causality ◽  
Crude Oil Price ◽  
Content Type

Purpose The purpose of this study is to investigate the predictability of crude oil price and shale oil production, in a bid to examine the possibility of bi-directional causality. Design/methodology/approach The study adopts a recently developed predictability model by Westerlund and Narayan (2015), which accounts for persistence, endogeneity and heteroscedasticity. It also accounts for structural breaks in the predictive models. Findings The empirical results show that only a unidirectional causal relationship from crude oil price to shale oil production exists. This happens as crude oil price appears to be a good predictor of shale oil production; however, shale oil production does not serve as a good predictor for crude oil price. Accounting for structural break was found to improve the predictability and forecast accuracy of the predictive model. Our result is robust to choice of crude oil price benchmarks (West Texas Intermediate, Brent, Dubai Fateh and Refiners’ Acquisition Cost) and their denominations (real or nominal). Research limitations/implications The result implies that crude oil price must be considered when predicting shale oil production. Meanwhile, the non-significance of shale of production in crude oil price predictive model provides information to potential analyst, researchers and countries predicting crude oil price that failure to account for the effect of shale oil production would not have significant impact on the forecast accuracy of their models. Originality/value The study contributes originally to the literature on crude oil price–shale oil production in four major ways. First, it applies a recently developed predictability method by Westerlund and Narayan (2015), which is more suitable for dealing with persistence, conditional heteroscedasticity and endogeneity in the predictors. Second, it investigates existence of reverse causality between crude oil price and shale oil production. Third, it examines the variation in the response and effect of four major crude oil price benchmarks. Fourth, it considers crude oil price in both real and nominal terms.

Petrobras Brazil outlook improves but challenges loom

2018 ◽  
Keyword(s):  
Crude Oil ◽  
Government Policy ◽  
Oil Price ◽  
Crude Oil Price ◽  
Upward Trend ◽  
Short Term ◽  
Content Type ◽  
Oil Companies ◽  
Future Success

Subject The outlook for Petrobras. Significance The collapse of the crude oil price in 2014, a huge corruption scandal and government policy have combined to damage Petrobras. While most major international oil companies have turned a corner and their profitability is on an upward trend, uncertainties continue to plague the company. Impacts Future success will depend in part on Petrobras tackling its debt mountain effectively. Petrobras will face a struggle to regain credibility in the markets despite improving results. Plans to require congressional approval of privatisations, and diverging campaign promises on Petrobras, will increase short-term doubts.

Markets question degree of monetary divergence

2017 ◽  
Keyword(s):  
Monetary Policy ◽  
Crude Oil ◽  
Federal Reserve ◽  
Oil Price ◽  
Crude Oil Price ◽  
Content Type ◽  
First Time

Subject Monetary divergence. Significance Markets have been little affected by the plethora of monetary policy news: the sweeping re-election of Shinzo Abe in Japan and his re-commitment to ultra-loose policy, the ECB decision to extend its asset purchase programme, albeit at a reduced size, the first UK rate rise in ten years and the announcement that continuity candidate Jerome Powell will replace Janet Yellen as US Federal Reserve (Fed) Chair. However, Powell’s appointment reinforces market questioning of US tightening as inflation remains stubbornly low. Impacts The dollar index has risen 4% since early September on Fed hawkishness; the divergence with Europe and Japan will push it modestly higher. Despite a plethora of vulnerabilities in markets, the Vix Index, Wall Street’s so-called ‘fear gauge’, still stands at a 20-year low. The Brent crude oil price passed 60 dollars per barrel recently for the first time in over two years, but further upside is very limited.

Crude oil price prediction based on LSTM network and GM (1,1) model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tianxiang Yao ◽  
Zihan Wang
Keyword(s):  
Crude Oil ◽  
Oil Price ◽  
Crude Oil Price ◽  
Short Term ◽  
Content Type ◽  
Forecasting Models ◽  
Price Series ◽  
Mode Decomposition ◽  
Lstm Network

PurposeAccording to the problem of crude oil price forecasting, the purpose of this paper is to propose a multi-step prediction method based on the empirical mode decomposition, long short-term memory network and GM (1,1) model.Design/methodology/approachFirst, the empirical mode decomposition method is used to decompose the crude oil price series into several components with different frequencies. Then, each subsequence is classified and synthesized based on the specific periodicity and other properties to obtain several components with different significant characteristics. Finally, all components are substituted into a suitable prediction model for fitting. LSTM models with different parameters are constructed for predicting specific components, which approximately and respectively represent short-term market disturbance and long-term influences. Rolling GM (1,1) model is constructed to simulate a series representing the development trend of oil price. Eventually, all results obtained from forecasting models are summarized to evaluate the performance of the model.FindingsThe model is respectively applied to simulate daily, weekly and monthly WTI crude oil price sequences. The results show that the model has high accuracy on the prediction, especially in terms of series representing long-term influences with lower frequency. GM (1,1) model has excellent performance on fitting the trend of crude oil price.Originality/valueThis paper combines GM (1,1) model with LSTM network to forecast WTI crude oil price series. According to the different characteristics of different sequences, suitable forecasting models are constructed to simulate the components.

Sign in / Sign up

Export Citation Format

Share Document