scholarly journals Multifractal Detrended Cross-correlation Analysis of Gold and WTI Crude Oil Price Time Series

2014 ◽  
Vol 03 (03) ◽  
Author(s):  
Burugupalli S Manimaran P
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Crude Oil ◽  
Cross Correlation ◽  
Oil Price ◽  
Crude Oil Price ◽  
Cross Correlation Analysis

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 Dollar’s Influence on Crude Oil and Gold Based on MF-DPCCA Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhonghui Ding ◽  
Kai Shi ◽  
Bin Wang
Keyword(s):  
Correlation Analysis ◽  
Crude Oil ◽  
Cross Correlation ◽  
Combined Effect ◽  
Analysis Method ◽  
Correlation Relationship ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method ◽  
Phase Randomization

This paper analyzed the influence of dollar on crude oil and gold based on the multifractal detrended partial cross-correlation analysis method. It showed that affected by the dollar, the crude oil and gold markets have a partial cross-correlation relationship which is stronger than their own cross-correlation. The partial cross-correlation is long-term and has multifractal characteristics. Through shuffled and Fourier-phase randomization, it is found that this multifractal feature is caused by the combined effect of the long-term cross-correlation between the returns and the fluctuation fat-tailed distribution, where the influence of the fat-tailed distribution is slightly greater than that of the long-term cross-correlation between the returns.

Multiscale Multifractal Detrended Cross-Correlation Analysis of High-Frequency Financial Time Series

2019 ◽  
Vol 18 (03) ◽  
pp. 1950014 ◽  
Author(s):  
Jingjing Huang ◽  
Danlei Gu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
High Frequency ◽  
Cross Correlation ◽  
Financial Time Series ◽  
Chinese Market ◽  
Change Of Scale ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

In order to obtain richer information on the cross-correlation properties between two time series, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). This method is based on the Hurst surface and can be used to study the non-linear relationship between two time series. By sweeping through all the scale ranges of the multifractal structure of the complex system, it can present more information than the multifractal detrended cross-correlation analysis (MF-DCCA). In this paper, we use the MM-DCCA method to study the cross-correlations between two sets of artificial data and two sets of 5[Formula: see text]min high-frequency stock data from home and abroad. They are SZSE and SSEC in the Chinese market, and DJI and NASDAQ in the US market. We use Hurst surface and Hurst exponential distribution histogram to analyze the research objects and find that SSEC, SZSE and DJI, NASDAQ all show multifractal properties and long-range cross-correlations. We find that the fluctuation of the Hurst surface is related to the positive and negative of [Formula: see text], the change of scale range, the difference of national system, and the length of time series. The results show that the MM-DCCA method can give more abundant information and more detailed dynamic processes.

Importance of Body Sway Velocity Information in Controlling Ankle Extensor Activities During Quiet Stance

2003 ◽  
Vol 90 (6) ◽  
pp. 3774-3782 ◽  
Author(s):  
Kei Masani ◽  
Milos R. Popovic ◽  
Kimitaka Nakazawa ◽  
Motoki Kouzaki ◽  
Daichi Nozaki
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Muscle Activity ◽  
Cross Correlation ◽  
Gastrocnemius Muscle ◽  
Quiet Stance ◽  
Lateral Gastrocnemius ◽  
Velocity Feedback ◽  
Cross Correlation Analysis ◽  
Velocity Information

In literature, it has been suggested that the CNS anticipates spontaneous change in body position during quiet stance and continuously modulates ankle extensor muscle activity to compensate for the change. The purpose of this study was to investigate whether velocity feedback contributes by modulating ankle extensor activities in an anticipatory fashion, facilitating effective control of quiet stance. Both theoretical analysis and experiments were carried out to investigate to what extent velocity feedback contributes to controlling quiet stance. The experiments were carried out with 16 healthy subjects who were asked to stand quietly with their eyes open or closed. During the experiments, the center of pressure (COP) displacement (COPdis), the center of mass (COM) displacement (COMdis), and COM velocity (COMvel) in the anteroposterior direction were measured. Rectified electromyograms (EMGs) were used to measure muscle activity in the right soleus muscle, the medial gastrocnemius muscle, and the lateral gastrocnemius muscle. The simulations were performed using an inverted pendulum model that described the anteroposterior kinematics and dynamics of quiet stance. In the simulations, an assumption was made that the COMdis of the body would be regulated using a proportional-derivative (PD) controller. Two different PD controllers were evaluated in these simulations: 1) a controller with the high-derivative/velocity gain (HDG) and 2) a controller with the low-derivative/velocity gain (LDG). Cross-correlation analysis was applied to investigate the relationships between time series obtained in experiments 1) COMdis and EMGs and 2) COMvel and EMGs. Identical cross-correlation analysis was applied to investigate the relationships between time series obtained in simulations 3) COMdis and ankle torque and 4) COMvel and ankle torque. The results of these analyses showed that the COMdis was positively correlated with all three EMGs and that the EMGs temporally preceded the COMdis. These findings agree with the previously published studies in which it was shown that the lateral gastrocnemius muscle is actively modulated in anticipation of the body's COM position change. The COMvel and all three EMGs were also correlated and the cross-correlation function (CCF) had two peaks: one that was positive and another that was negative. The positive peaks were statistically significant, unlike the negative ones; they were larger than the negative peaks; and their time shifts were much shorter compared with the time shifts of the negative peaks. When these results were compared with the CCF results obtained for simulated time series, it was discovered that the cross-correlation results for the HDG controller closely matched cross-correlation results for the experimental time series. On the other hand, the simulation result obtained for LDG controller did not match the experimental results. These findings suggest that the actual postural control system during quiet stance adopts a control strategy that relies notably on velocity information and that such a controller can modulate muscle activity in anticipatory manner without using a feed-forward mechanism.

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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