Does the monetary policy influenced cross-correlations on the main world stocks markets? Power Law Classification Scheme analysis

2019 ◽  
Vol 519 ◽  
pp. 72-81 ◽  
Author(s):  
Janusz Miśkiewicz ◽  
Adrian Tadla ◽  
Zenon Trela
Keyword(s):  
Monetary Policy ◽  
Power Law ◽  
Classification Scheme ◽  
Cross Correlations ◽  
Scheme Analysis

scholarly journals Network Analysis of Cross-Correlations on Forex Market during Crises. Globalisation on Forex Market

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 352
Author(s):  
Janusz Miśkiewicz
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Exchange Rates ◽  
Power Law ◽  
Financial Crises ◽  
Classification Scheme ◽  
Significant Growth ◽  
Cross Correlations

Within the paper, the problem of globalisation during financial crises is analysed. The research is based on the Forex exchange rates. In the analysis, the power law classification scheme (PLCS) is used. The study shows that during crises cross-correlations increase resulting in significant growth of cliques, and also the ranks of nodes on the converging time series network are growing. This suggests that the crises expose the globalisation processes, which can be verified by the proposed analysis.

Cross-Correlations in the Brownian Motion of Colloidal Nanoparticles

2020 ◽  
Vol 65 (1-2) ◽  
pp. 27-34
Author(s):  
Sz. Kelemen ◽  
◽  
L. Varga ◽  
Z. Néda ◽  
◽  
...  
Keyword(s):  
Brownian Motion ◽  
Power Law ◽  
Cross Correlation ◽  
Colloidal Particles ◽  
Pearson Correlation ◽  
Transverse Component ◽  
Collective Effects ◽  
Colloidal Nanoparticles ◽  
Diffusive Motion ◽  
Cross Correlations

"The two-body cross-correlation for the diffusive motion of colloidal nano-spheres is experimentally investigated. Polystyrene nano-spheres were used in a very low concentration suspension in order to minimize the three- or more body collective effects. Beside the generally used longitudinal and transverse component correlations we investigate also the Pearson correlation in the magnitude of the displacements. In agreement with previous studies we find that the longitudinal and transverse component correlations decay as a function of the inter-particle distance following a power-law trend with an exponent around -2. The Pearson correlation in the magnitude of the displacements decay also as a power-law with an exponent around -1. Keywords: colloidal particles, Brownian motion, cross-correlation. "

MODELING CROSS-CORRELATIONS OF TRAFFIC FLOW

2010 ◽  
Vol 20 (10) ◽  
pp. 3323-3328 ◽  
Author(s):  
PENGJIAN SHANG ◽  
KEQIANG DONG ◽  
SANTI KAMAE
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Power Law ◽  
Cross Correlation ◽  
Heavy Tails ◽  
Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
Large Increment ◽  
Active Research

The study of diverse natural and nonstationary signals has recently become an area of active research for physicists. This is because these signals exhibit interesting dynamical properties such as scale invariance, volatility correlation, heavy tails and fractality. The focus of the present paper is on the intriguing power-law autocorrelations and cross-correlations in traffic series. Detrended Cross-Correlation Analysis (DCCA) is used to study the traffic flow fluctuations. It is demonstrated that the time series, observed on the Anhua-Bridge highway in the Beijing Third Ring Road (BTRR), may exhibit power-law cross-correlations when they come from two adjacent sections or lanes. This indicates that a large increment in one traffic variable is more likely to be followed by large increment in the other traffic variable. However, for traffic time series derived from nonadjacent sections or lanes, we find that even though they are power-law autocorrelated, there is no cross-correlation between them with a unique exponent. Our results show that DCCA techniques based on Detrended Fluctuation Analysis (DFA) can be used to analyze and interpret the traffic flow.

Power-law autocorrelated stochastic processes with long-range cross-correlations

2007 ◽  
Vol 56 (1) ◽  
pp. 47-52 ◽  
Author(s):  
B. Podobnik ◽  
D. F. Fu ◽  
H. E. Stanley ◽  
P. Ch. Ivanov
Keyword(s):  
Stochastic Processes ◽  
Long Range ◽  
Power Law ◽  
Cross Correlations

Correlations versus causality in Stochastic Long-range Forecasting as a Past Value Problem

2021 ◽  
Author(s):  
Lenin Del Rio Amador ◽  
Shaun Lovejoy
Keyword(s):  
Granger Causality ◽  
Long Range ◽  
Power Law ◽  
Weather Prediction ◽  
Emergent Properties ◽  
Relaxation Processes ◽  
Initial Value ◽  
Relaxation Equation ◽  
Cross Correlations

<p>Over time scales between 10 days and 10-20 years – the macroweather regime – atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the (fractional) energy balance equation (fractional Gaussian noise) which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential. They are obtained when the order of the relaxation equation is fractional rather than integer and they are solved as past value problems rather than initial value problems.</p><p>Long-range weather prediction is conventionally an initial value problem that uses the current state of the atmosphere to produce ensemble forecasts. In contrast, StocSIPS predictions for long-memory processes are “past value” problems that use historical data to provide conditional forecasts. Cross-correlations can be used to define teleconnection patterns, and for identifying possible dynamical interactions, but they do not necessarily imply any causation. Using the precise notion of Granger causality, we show that for long-range stochastic temperature forecasts, the cross-correlations are only relevant at the level of the innovations – not temperatures. Extended here to the multivariate case, (m-StocSIPS) produces realistic space-time temperature simulations. Although it has no Granger causality, we are able to reproduce emergent properties including realistic teleconnection networks and El Niño events and indices.</p>

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