Optimal portfolio strategy with cross-correlation matrix composed by DCCA coefficients: Evidence from the Chinese stock market

2016 ◽  
Vol 444 ◽  
pp. 667-679 ◽  
Author(s):  
Xuelian Sun ◽  
Zixian Liu
Keyword(s):  
Stock Market ◽  
Correlation Matrix ◽  
Cross Correlation ◽  
Optimal Portfolio ◽  
Chinese Stock Market ◽  
Portfolio Strategy

scholarly journals Portfolio optimization under mean-CVaR simulation with copulas on the Vietnamese stock exchange

2021 ◽  
Vol 18 (2) ◽  
pp. 273-286
Author(s):  
Le Tuan Anh ◽  
Dao Thi Thanh Binh
Keyword(s):  
Stock Market ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Optimal Portfolio ◽  
Gaussian Copula ◽  
Optimal Portfolios ◽  
Empirical Results ◽  
Portfolio Strategy ◽  
T Copula ◽  
Mean Variance

This paper studies how to construct and compare various optimal portfolio frameworks for investors in the context of the Vietnamese stock market. The aim of the study is to help investors to find solutions for constructing an optimal portfolio strategy using modern investment frameworks in the Vietnamese stock market. The study contains a census of the top 43 companies listed on the Ho Chi Minh stock exchange (HOSE) over the ten-year period from July 2010 to January 2021. Optimal portfolios are constructed using Mean-Variance Framework, Mean-CVaR Framework under different copula simulations. Two-thirds of the data from 26/03/2014 to 27/1/2021 consists of the data of Vietnamese stocks during the COVID-19 recession, which caused depression globally; however, the results obtained during this period still provide a consistent outcome with the results for other periods. Furthermore, by randomly attempting different stocks in the research sample, the results also perform the same outcome as previous analyses. At about the same CvaR level of about 2.1%, for example, the Gaussian copula portfolio has daily Mean Return of 0.121%, the t copula portfolio has 0.12% Mean Return, while Mean-CvaR with the Raw Return portfolio has a lower Return at 0.103%, and the last portfolio of Mean-Variance with Raw Return has 0.102% Mean Return. Empirical results for all 10 portfolio levels showed that CVaR copula simulations significantly outperform the historical Mean-CVaR framework and Mean-Variance framework in the context of the Vietnamese stock exchange.

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

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Gang Chu ◽  
Xiao Li ◽  
Yongjie Zhang
Keyword(s):  
Stock Market ◽  
Asset Allocation ◽  
Cross Correlation ◽  
Vital Role ◽  
Market Dynamics ◽  
Chinese Stock Market ◽  
Market Participation ◽  
Market Index ◽  
Cross Correlations ◽  
The Cross

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.

scholarly journals Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

2017 ◽  
Vol 16 (02) ◽  
pp. 1750018 ◽  
Author(s):  
Rui-Qi Han ◽  
Wen-Jie Xie ◽  
Xiong Xiong ◽  
Wei Zhang ◽  
Wei-Xing Zhou
Keyword(s):  
Stock Market ◽  
Random Matrix ◽  
Correlation Matrix ◽  
Partial Correlation ◽  
Correlation Structure ◽  
Matrix Analysis ◽  
Chinese Stock Market ◽  
Average Correlation ◽  
Largest Eigenvalue ◽  
Largest Eigenvalues

The correlation structure of a stock market contains important financial contents, which may change remarkably due to the occurrence of financial crisis. We perform a comparative analysis of the Chinese stock market around the occurrence of the 2008 crisis based on the random matrix analysis of high-frequency stock returns of 1228 Chinese stocks. Both raw correlation matrix and partial correlation matrix with respect to the market index in two time periods of one year are investigated. We find that the Chinese stocks have stronger average correlation and partial correlation in 2008 than in 2007 and the average partial correlation is significantly weaker than the average correlation in each period. Accordingly, the largest eigenvalue of the correlation matrix is remarkably greater than that of the partial correlation matrix in each period. Moreover, each largest eigenvalue and its eigenvector reflect an evident market effect, while other deviating eigenvalues do not. We find no evidence that deviating eigenvalues contain industrial sectorial information. Surprisingly, the eigenvectors of the second largest eigenvalues in 2007 and of the third largest eigenvalues in 2008 are able to distinguish the stocks from the two exchanges. We also find that the component magnitudes of the some largest eigenvectors are proportional to the stocks’ capitalizations.

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