scholarly journals Fractal Scaling in Crude Oil Price Evolution via Time Series Analysis of Historical Data

2009 ◽  
Vol 4 (5) ◽  
Author(s):  
Dimitrios I Gerogiorgis
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Crude Oil ◽  
Scaling Law ◽  
Oil Price ◽  
Time Interval ◽  
Crude Oil Price ◽  
Price Variation ◽  
Fractal Scaling ◽  
Series Analysis

This paper presents historical price data for two different crude oil types and examines the stationarity and inherent structure in oil price variation, applying many degrees of time resolution. Time Series Analysis results are then used to identify patterns and analyze the variation timescales. A specific goal of this study is to investigate and demonstrate the presence of fractal scaling. In particular, we postulate and prove that the mean size of the absolute values of price changes obeys a fractal scaling law (a power law) and can be expressed as a function of the analysis time interval (here, the latter is an independently varying parameter, ranging from a day up to a calendar year). The fractal structure of crude oil price variation is confirmed, the drift exponent is computed and the power scaling window of validity is depicted for both types, illustrating the interplay of both short- and long-term effects on the intrinsic structure of crude oil prices before and after 2008.

scholarly journals Time series analysis of persistence in crude oil price volatility across bull and bear regimes

Energy ◽  
2016 ◽  
Vol 109 ◽  
pp. 29-37 ◽  
Author(s):  
Luis A. Gil-Alana ◽  
Rangan Gupta ◽  
Olusanya E. Olubusoye ◽  
OlaOluwa S. Yaya
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Crude Oil ◽  
Price Volatility ◽  
Oil Price ◽  
Crude Oil Price ◽  
Series Analysis ◽  
Oil Price Volatility

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 Impact of Crude Prices on Retail Prices – A Time Series Analysis

2019 ◽  
Vol 8 (2S7) ◽  
pp. 606-612
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Crude Oil ◽  
Energy Demand ◽  
Multivariate Time Series ◽  
Secondary Data ◽  
Series Analysis ◽  
Long Run ◽  
The Uk ◽  
The Impact

The UK has emerged as one of the largest producers of petroleum in the world. A significant amount of petroleum is used for fulfilling the energy demand within the country. However, the country witnessed a different trend from 2015. This is mainly due to the increase in imports of petroleum in order to meet domestic needs. To this, there is a need to identify the impact of changes exist in petrol and crude oil prices in the UK. In this context, the researcher has undertaken primary research to derive conclusions which are case specific and can comply with the research aim. The study used secondary data for the year 2015-2018 and conducted multivariate time series analysis. A series of tests including unit root, ARIMA, and co-integration tests were used to derive the results. The study found that there was an asymmetric relationship between the movements of prices of crude oil with respect to retail fuel prices in the long run. However, the study is not without limitations which are represented at the end of the study following with its future scope

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.

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