Correlations and cross-correlations in the Brazilian agrarian commodities and stocks

Junior, Erinaldo Leite Siqueira; Stošić, Tatijana; Bejan, Lucian; Stošić, Borko

doi:10.1016/j.physa.2010.01.040

Cited by 87 publications

(21 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the literature, the estimated fractal cross-correlations are often related to the fractal properties of the individual signals themselves [36,50,51]. Therefore, in Fig.…”

Section: A Arfima Processesmentioning

confidence: 99%

Detrended cross-correlation analysis consistently extended to multifractality

et al. 2014

View full text Add to dashboard Cite

We propose an algorithm, multifractal cross-correlation analysis (MFCCA), which constitutes a consistent extension of the detrended cross-correlation analysis and is able to properly identify and quantify subtle characteristics of multifractal cross-correlations between two time series. Our motivation for introducing this algorithm is that the already existing methods, like multifractal extension, have at best serious limitations for most of the signals describing complex natural processes and often indicate multifractal cross-correlations when there are none. The principal component of the present extension is proper incorporation of the sign of fluctuations to their generalized moments. Furthermore, we present a broad analysis of the model fractal stochastic processes as well as of the real-world signals and show that MFCCA is a robust and selective tool at the same time and therefore allows for a reliable quantification of the cross-correlative structure of analyzed processes. In particular, it allows one to identify the boundaries of the multifractal scaling and to analyze a relation between the generalized Hurst exponent and the multifractal scaling parameter λ(q). This relation provides information about the character of potential multifractality in cross-correlations and thus enables a deeper insight into dynamics of the analyzed processes than allowed by any other related method available so far. By using examples of time series from the stock market, we show that financial fluctuations typically cross-correlate multifractally only for relatively large fluctuations, whereas small fluctuations remain mutually independent even at maximum of such cross-correlations. Finally, we indicate possible utility of MFCCA to study effects of the time-lagged cross-correlations.

show abstract

“…In the literature, the estimated fractal cross-correlations are often related to the fractal properties of the individual signals themselves [36,50,51]. Therefore, in Fig.…”

Section: A Arfima Processesmentioning

confidence: 99%

Detrended cross-correlation analysis consistently extended to multifractality

et al. 2014

View full text Add to dashboard Cite

show abstract

“…We adopt the Multifractal Detrended Fluctuation Analysis (MFDFA) method, a generalization of the Detrended Fluctuation Analysis (DFA) method, that has been proved to be a particularly flexible method, specially to deal with non-stationary series, (Kantelhardt et al 2002;Serrano and Figliola 2009;LeiteSiqueira et al 2010).…”

Section: The Multifractal Fluctuation Analysismentioning

confidence: 99%

Analysis of the nonlinear relationship between commodity prices in the last two decades

Catalano

Figliola

2014

Qual Quant

View full text Add to dashboard Cite

We propose the construction of a network to study the correlation among price indices of different commodities, by using the Multifractal Cross-Correlation method proposed by Podobnik and Stanley. This estimator, based on the method Multifractal Detrended Fluctuation Analysis, is effective for self-similar signals with characteristics such as those we here analyze. We construct different networks for time periods between 1991 and 2012. Each node represents a commodity group and the links are the cross-correlation between nodes. We study the evolution of these networks from January 1991 to April 2012. The results show that after 2007, high connectivity arises between the nodes of the matrix. We conjecture that this is a consequence of the cash flow from equities and real estate markets to the commodity market due to the subprime mortage crisis.

show abstract

“…Complexity, time irreversibility and fractal scaling characteristics reflect different aspects of nonlinear properties of living dynamical systems such as stock market fluctuations [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%