scholarly journals Modeling the Nigerian Bonny Light Crude Oil Price: The Power of Fuzzy Time Series

2021 ◽  
Vol 09 (04) ◽  
pp. 370-3900
Author(s):  
Desmond Chekwube Bartholomew ◽  
Ukamaka Cynthia Orumie ◽  
Chukwudi Paul Obite ◽  
Blessing Iheoma Duru ◽  
Felix Chikereuba Akanno
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Oil Price ◽  
Fuzzy Time Series ◽  
Crude Oil Price ◽  
Light Crude Oil

scholarly journals Sugeno Intuitionistic Fuzzy Generator Based Computational Technique for Crude Oil Price Forecasting

2020 ◽  
Vol 5 (3) ◽  
pp. 488-496
Author(s):  
Gunjan Goyal ◽  
Dinesh C. S. Bisht
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Fuzzy Sets ◽  
Time Series Forecasting ◽  
Oil Price ◽  
Fuzzy Time Series ◽  
Spot Price ◽  
Computational Technique ◽  
Crude Oil Price ◽  
Intuitionistic Fuzzy

Crude oil being a significant source of energy, change of crude oil price can affect the global economy. In this paper, a new approach based on the intuitionistic fuzzy set theory has been implemented to predict the crude oil price. This paper presents the intuitionistic fuzzy time series forecasting algorithm to enhance the efficacy of time series forecasting which includes fuzzy c-means clustering to obtain the optimal cluster centers. Further, a computational technique is proposed for the construction of triangular fuzzy sets and these fuzzy sets are converted to intuitionistic fuzzy sets with the help of Sugeno type intuitionistic fuzzy generator. The popular benchmark dataset of West Texas Intermediate crude oil spot price is used for the validation process. The numerical results when compared with existing methods notify that the proposed method enhances the accuracy of the crude oil price forecasts.

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 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.

A novel multiscale forecasting model for crude oil price time series

2021 ◽  
Vol 173 ◽  
pp. 121181
Author(s):  
Ranran Li ◽  
Yucai Hu ◽  
Jiani Heng ◽  
Xueli Chen
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Oil Price ◽  
Forecasting Model ◽  
Crude Oil Price

scholarly journals Screening for light crude oil and market comovements

2020 ◽  
Vol 9 (7) ◽  
pp. 123-129
Author(s):  
Omid Faseli
Keyword(s):  
Crude Oil ◽  
Commodity Prices ◽  
Price Volatility ◽  
Oil Price ◽  
Mapping Technique ◽  
Crude Oil Price ◽  
Economic Sectors ◽  
Light Crude Oil ◽  
Global Indices ◽  
Oil Markets

This study aimed to perform a screening for economic interrelationships among market participants from the stock market, global stock indices, and commodities from fossil energy, agricultural, and the metals sector. Particular focus was put on the comovements of the light crude oil benchmarks West Texas Intermediate (WTI) and Brent crude oil. In finance research and the crude oil markets, identifying novel groupings and interactions is a fundamental requirement due to the extended impact of crude oil price fluctuations on economic growth and inflation. Thus, it is of high interest for investors to identify market players and interactions that appear sensitive to crude oil price volatility triggers. The price development of 14 stocks, 25 leading global indices, and 13 commodity prices, including WTI and Brent, were analyzed via data mining applying the hierarchical correlation cluster mapping technique. All price data comprised the period from January 2012 – December 2018 and were based on daily returns. The technique identifies and visualizes existing hierarchical clusters and correlation patterns emphasizing comovements that indicate positively correlated processes. The method successfully identified clustering patterns and a series of relevant and partly unexpected novel comovements in all investigated economic sectors. Although additional research is required to reveal the causative factors, the study offers an insight into in-depth market interrelationships.

scholarly journals Oil Prices, Emission Permits Trade of Carbon, and the Dependence Between Their Quantiles

2022 ◽  
Vol 16 ◽  
pp. 38-45
Author(s):  
Baoshuai Zhang ◽  
Yuqin Zhou
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Negative Impact ◽  
Positive Impact ◽  
Carbon Price ◽  
Oil Price ◽  
Series Data ◽  
Crude Oil Price ◽  
The Impact ◽  
Quantile Regression Method

The relations between carbon and oil market is concerned by many scholars but little research has focused on the dependence between their quantiles. We use Quantile on Quantile Regression method to study the impact of WTI crude oil price and Daqing crude oil price on carbon price and use wavelet analysis to clean and decompose the time series. Results show that the impact of crude oil on carbon is heterogeneous. Research based on the original sequence shows that crude oil price has a positive impact on carbon price at all quantile levels. Research based on decomposition sequence shows that the positive impact of crude oil on carbon begins to weaken, the zero effect begins to increase, and the negative impact also begins to appear. However, the negative impact on carbon price becomes stronger with the stability of the time series data obtained from the decomposition of crude oil price series gradually improving, while the positive impact gradually weakens.

scholarly journals Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Crude Oil ◽  
Oil Prices ◽  
Principal Component ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Swarm Optimization ◽  
Crude Oil Prices

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

Sign in / Sign up

Export Citation Format

Share Document