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Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series.
TLDR
A new method is proposed, detrended cross-correlation analysis, which is a generalization of detrende fluctuation analysis and is based on detrending covariance, designed to investigate power-law cross correlations between different simultaneously recorded time series in the presence of nonstationarity.
Cross-correlations between volume change and price change
TLDR
This work analyzes 14,981 daily recordings of the Standard and Poor's (S & P) 500 Index over the 59-year period 1950–2009, and finds power-law cross-correlations between |R| and |R̃| by using detrendedCross-correlation analysis (DCCA), and introduces a joint stochastic process that models these cross-Correlations.
Statistical tests for power-law cross-correlated processes.
TLDR
Using ρ(DCCA)(T,n), it is shown that the Chinese financial market's tendency to follow the U.S. market is extremely weak and an additional statistical test is proposed that can be used to quantify the existence of cross-correlations between two power-law correlated time series.
Detrended cross-correlation analysis for non-stationary time series with periodic trends
TLDR
It is demonstrated that one can accurately quantify power-law cross-correlations between different simultaneously recorded time series in the presence of highly non-stationary sinusoidal and polynomial overlying trends by using the new technique of detrendedCross-correlation analysis with varying order l of the polynometric.
Changes in Cross-Correlations as an Indicator for Systemic Risk
TLDR
This paper studies 10 different Dow Jones economic sector indexes, and applying principle component analysis (PCA) demonstrates that the rate of increase in principle components with short 12-month time windows can be effectively used as an indicator of systemic risk—the larger the change of PC1, the higher the increase of systemicrisk.
Spontaneous recovery in dynamical networks
Networks that fail can sometimes recover spontaneously—think of traffic jams suddenly easing or people waking from a coma. A model for such recoveries reveals spontaneous ‘phase flipping’ between
Systemic risk and spatiotemporal dynamics of the US housing market
TLDR
It is found that dramatic increases in the systemic risk are usually accompanied by regime shifts, which provide a means of early detection of housing bubbles, and richer economic information in the largest eigenvalues deviating from RMT predictions for the housing market than for stock markets.
Influence of corruption on economic growth rate and foreign investment
We analyze the dependence of the Gross Domestic Product (GDP) per capita growth rates on changes in the Corruption Perceptions Index (CPI). For the period 1999–2004 for all countries in the world, we
Risk-Adjusted Performance of Mutual Funds: Some Tests
The development of a stock market depends to a great extent on the development of institutional investors. The paper studies the mutual fund industry and applies various tests to evaluate the
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