MULTIFRACTAL DETRENDED CROSS-CORRELATION ANALYSIS OF BVP MODEL TIME SERIES

We have also found that these cross-correlations are strongly multifractal in the short term and weakly multifractal in long the term. Two major sources of multifractality which can be found in various time series: Historically gold retains its value during times of crisis and is used as a hedge against inflation, deflation or currency devaluation. We found that there exists a power-law cross-correlation between the Gold and Crude Oil time series and the multifractal features are significant. Market agent perspective on fractal features in crude oil time series was studied [ 29 , 30 ]. Global crude oil pricing benchmarks Brent and WTI price hence its price volatility become a hot research topic recently. As a final result inflation hence gold price tend to appreciate with inflation rising. The analysis throws light on the structure of crude oil and Gold market as well as its link to macroeconomic conditions and socio-political extreme events.

Therefore, in recent years, many researchers attempted to quantify cross-correlations between non stationary data from complex systems. The analysis throws light on the structure of crude oil and Gold market as well as its link to macroeconomic conditions and socio-political extreme events. We get the crosscorrelation exponent 0. The Section 4 shares the result and its analysis. Our results have significant implications to market efficiency. Market agent perspective on fractal features in crude oil time series was studied [ 29 , 30 ]. Detrended Cross-Correlation Analysis DCCA was proposed [ 17 ] to investigate power-law cross-correlations between two simultaneously recorded time series in the presence of non-stationarity. The multifractality in real world non stationary time series data are described better from scaling exponents.

Section 5 gives our conclusions. The observed signals of the physical quantities characterizing any complex system like social, financial, ecological, biological or technological are composed of large number of interacting assorted parameters linked each other nonlinearly and they exhibit long-range correlations. Cross-correlations of very small fluctuations and large fluctuations are persistent but whereas for small fluctuations are anti-persistent in the long term.

For two markets, maybe Brent market is affected more by Gulf Hvp than WTI hence to avoid impact of gulf war onto Crude oil price we had chosen WTI Crude oil time series for cross correlation study with Gold time series. We found that the global Hurst coefficient varies with the q and there is multifractality evidenced through the multifractal spectrum also. Here the scaling exponent H xy q is known as the generalized crosscorrelation Hurst exponent, describing the power-low relationship.

Historically gold retains its value during times of crisis and is used as a hedge against inflation, deflation or currency devaluation. Select your language of interest to view the multivractal content in your interested language.

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While the cross-correlated scaling exponents H xy q decreases from 0. J Bus Fin Aff 3: Muptifractal, 2N s non-overlapping windows are obtained together. Two major sources of multifractality which can be found in various time series: While the cross-correlated scaling exponents H xy q are not varying considerably indicating crosscorrelated behavior is weakly multifractal in the long term.

To test the presence of cross-correlation quantitatively we need MF-X-FA method which can estimate the cross-correlation exponent. Moreover, their behavior for small fluctuations is persistent and those of large fluctuations are anti persistent in the short term.

The trends X v i and Y v i are the fitting polynomial with order m in each segment v.

This new method was applied to different time series for longrange power-law cross-correlations [ 18 ]. So it becomes evident for a cross correlation study and analysis on Gold and Crude Oil time series. Where and are the average of the two time series x i and y i. The Section 4 shares the result and its analysis. Therefore, understanding the dynamics of its price time series seems to be crucial, since it may allow one to assess the potential impacts of its shocks on several economies multifrractal on other financial assets.

This article is focused on that study to analyze the cross correlation between Gold and Crude Oil.

We have also found that these cross-correlations are strongly multifractal in the short term and weakly multifractal in long the term. We get the crosscorrelation exponent 0. The theory of fractals proposed by Mandelbrot [ 1 ] in contrast to the efficient market hypothesis leads to study of complex system behavior through different method development and approaches.

Increases in oil price increases prices of gasoline which is derived from oil which drives transport of goods costly hence the Good prices rises. Non-stationary time series; Fractals; Hurst exponent; Multifractal detrended Cross-correlation Analysis. Detrended Cross-Correlation Analysis DCCA was proposed [ 17 ] to investigate power-law cross-correlations between two simultaneously recorded time series in the presence of non-stationarity.

When the scaling exponent H xy q is bbp q, the cross-correlation between two series is monofractal while if the scaling mulhifractal Hxy q is dependent on q, then the cross-correlation between two series is multifractal.

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

Although crude oil prices possess long-range dependence, the degree of long-range has decreased in short time horizons, although the market is tending towards efficiency regime at long. Therefore, in recent years, many researchers attempted to quantify cross-correlations between non seriies data from complex systems.

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As crosss-correlation final result inflation hence gold price tend to appreciate with inflation rising. We find that the cross-correlations display the characteristic of multifractality in the short term. Diversity of participants like producers, government, extreme socio-political events and speculators drive crude oil market price.

We found that there exists a power-law cross-correlation between the Gold and Crude Oil time series and the multifractal features are significant. Moreover, the crosscorrelations of small fluctuations are persistent, and those of large fluctuations are anti-persistent in the short term, while the crosscorrelations of all kinds of fluctuations are persistent in the long term.

Analyze the scaling behavior of the fluctuations by observing logarithmic plots between F q s and s for each values of q. Further for positive q, the H xy q describes the scaling behavior of the segments with large fluctuations.

The return of daily price is calculated as, where Pt being the daily closing price index at time t Figure 1. The eetrended throws light on the structure of crude oil and Gold market as well as its link to macroeconomic conditions and socio-political extreme events.

So, an increase in the price of crude oil can eventually translate into higher gold price.

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Since the length N is not always a multiple of the considered time scale s hence in order not to discard the section of series, the same procedure is repeated starting from the reverse end of each profile. Market agent perspective on fractal features in crude oil time series was studied [ 2930 ]. Ddetrended also most popularly used as an investment. Furthermore, based on multifractal spectrum, we have produced substantial evidences to determine crosscorrelation behaviors exhibit multifractal features.

Global crude oil pricing benchmarks Brent and WTI price hence its price volatility become a hot research topic recently. The results demonstrate the overall significance fo the crosscorrelation based on the analysis of multifractality. It is of crucial importance and significance to quantify such long-range correlations to have a deep understanding of the dynamics of the underlying complex systems.

Dynamics of crude oil prices was studied thru stochastic multi-model approach [ 28 ].

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