Dynamic cross-correlation and dynamic contagion of stock markets: a sliding windows approach with the DCCA correlation coefficient

Dynamic Connectivity in a Financial Network Using Time-Varying DCCA Correlation Coefficients

“…The results corroborate with those of Diebold and Yılmaz (2014), which relate the connectivity found in the network with the possibility of financial crises. Moreover, they are consistent with the results of Tilfani et al (2019), who found evidence in favour of the FMH. The results show that before the subprime crisis, markets became more connected.…”

“…As we aim to build a dynamic network, to measure the linkage between stock markets in a continuous way, we use sliding windows that calculate time varying DCCA correlation coefficients. This approach was presented, for example, in Tilfani et al (2019), who used it to analyse the continuous comovements between stock markets. We used windows of 1,000 observations to, not only guarantee the robustness of the estimated correlation coefficients, but also to ensure the trade-off between the long-term and local features.…”

“…With the DCCA, Ma et al (2013) analyse the relationship between some Asian stock markets, El Alaoui and Benbachir ( 2013) make an analysis of Middle East and North African indices, Shi et al (2014) assess the cross-correlations between U.S. and Chinese stock indices and Ferreira (2017) analyses the particular case of increased stock market integration between Portugal and Brazil. Aiming to study the evolution of comovements over time, Tilfani et al (2019) proposed for the application of sliding windows to the DCCA correlation coefficient, assessing the continuous evolution of stock markets' comovements, investigating the dynamic multiscale interdependence between major stock markets.…”

“…Additionally, with a multiscale network, the influence of time scales on network connectivity can be identified. For this reason, this article intends to create a network analysis method by joining the use of Detrended Cross-Correlation Analysis (DCCA) correlation coefficients that are based on the sliding windows approach (Tilfani et al, 2019) with the use of networks that are based on the global efficiency method (Latora and Marchiori, 2001) as a connectivity indicator. All in an effort to associate network connectivity with the occurrence of financial crises.…”

Crosscorrelation Analysis between P2P Lending Market and Stock Market in China

“…Fractal methods is widely applied in economics and finance, such as the detrending crosscorrelation analysis and the detrending moving averages, see [19][20][21][22][23][24][25][26][27][28][29] and the references therein. We will use the method of multifractal detrending crosscorrelation analysis (MF-DCCA), which is proposed by Podobnik and Stanley [27] and improved by Zhou [29], to calculate the crosscorrelation index to quantify the fractal features of the crosscorrelation between logarithmic returns of the P2P lending market and the stock market.…”