DCCA cross-correlation coefficient with sliding windows approach

Guedes, Everaldo Freitas; Zebende, Gilney Figueira

doi:10.1016/j.physa.2019.121286

Cited by 31 publications

(13 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A value of ρ DCCA (n) = 0 means there is no crosscorrelation, while values −1 or 1 reveal a perfect negative or positive correlation. Such measure can be obtained through the following algorithm [22]:…”

Section: Detrended Cross-correlation Analysis (Dcca)mentioning

confidence: 99%

See 1 more Smart Citation

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach based on Network Community Detection

Ferretti

2022

Preprint

View full text Add to dashboard Cite

We present a study on portfolio investments in financial applications. We describe a general modeling and simulation framework and study the impact on the use of different metrics to measure the correlation among assets. In particular, besides the traditional Pearson's correlation, we employ the Detrended Cross-Correlation Analysis (DCCA) and Detrended Partial Cross-Correlation Analysis (DPCCA). Moreover, a novel portfolio allocation scheme is introduced that treats assets as a complex network and uses modularity to detect communities of correlated assets. Weights of the allocation are then distributed among different communities for the sake of diversification. Simulations compare this novel scheme against Critical Line Algorithm (CLA), Inverse Variance Portfolio (IVP), the Hierarchical Risk Parity (HRP). Synthetic times series are generated using the Gaussian model, Geometric Brownian motion, GARCH, ARFIMA and modified ARFIMA models. Results show that the proposed scheme outperforms state of the art approaches in many scenarios. We also validate simulation results via backtesting, whose results confirm the viability of the proposal.

show abstract

Section: Detrended Cross-correlation Analysis (Dcca)mentioning

confidence: 99%

“…As already mentioned, this metrics has been already studied in a wide range of different application scenarios where data series are used [48,42,23,53,27,52] and even in economic contexts [21,22,15]. Thus, it becomes interesting to understand if this is a viable metrics to use in portfolio asset allocation.…”

Section: Detrended Cross-correlation Analysis (Dcca)mentioning

confidence: 99%

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach based on Network Community Detection

Ferretti

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The ρDCCA method has been applied in different fields such as climatological data 76 , economics 77,78 , financial markets [79][80][81][82][83] , health 84 , and environment 76,85 .…”

Section: Datamentioning

confidence: 99%

Sustainable urban mobility analysis for elderly and disabled people in São Paulo

Azevedo

Sampaio

Filho

et al. 2021

Sci Rep

View full text Add to dashboard Cite

The Brazilian Urban Mobility Policy integrates urban transport with traffic planning, establishing appropriate public policies that indicate the need for a safe and accessible public transport system. The major challenge is the inclusion of the elderly and people with disabilities. In this paper, we quantify the impact of rainfall on the number of people with disabilities and elderly people who use the public bus transportation system for accessibility in the first and last miles in the city of São Paulo. The proposed methodology is used to evaluate the co-movements between the time series of free-fare users and rainfall in São Paulo. The findings confirm the hypothesis that significant rainfall causes a reduction in the number of daily free-fare passengers who use the public bus system in São Paulo.

show abstract

“…e DCCA cross-correlation coefficient is defined in terms of the DFA method and the DCCA method, which is the ratio of the detrended covariance's function F 2 DCCA to the product of two detrended variance function F DFA . is cross-correlation coefficient is widely used to measure the relations between two time series at different scales [48]. e function is defined as follows:…”

Section: Dcca Cross-correlation Coefficientmentioning

confidence: 99%

Quantifying the Cross-Correlations between Online Market Participation Willingness and Stock Market Dynamics

Chu

Zhang

2020

Complexity

View full text Add to dashboard Cite

The investors’ market participation willingness plays a vital role in the decision-making process of asset allocation. With the newly emerged dataset of investors’ market participation willingness, this paper provides the first evidence on the dynamic relationship between market participation willingness and the market dynamics in the Chinese stock market. We select four typical Chinese stock market indices, i.e., SSE50 Index, CSI300 Index, Small and Medium Enterprise Market Index, and Growth Enterprise Market Index, to represent different aspects of the Chinese stock market. Moreover, we use mutual information to measure the overall dependence between market participation willingness and stock market and employ the DCCA cross-correlation coefficient and MF-DCCA to investigate the cross-correlation between market participation willingness and market dynamics. We find that there exist overall dependence and power-law cross-correlation between market participation willingness and the Chinese stock market, and the cross-correlations are significantly multifractal.

show abstract

DCCA cross-correlation coefficient with sliding windows approach

Cited by 31 publications

References 33 publications

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach based on Network Community Detection

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach based on Network Community Detection

Sustainable urban mobility analysis for elderly and disabled people in São Paulo

Quantifying the Cross-Correlations between Online Market Participation Willingness and Stock Market Dynamics

Contact Info

Product

Resources

About