Assessment of 48 Stock markets using adaptive multifractal approach

TL;DR

This study analyzes 48 global stock markets using adaptive multifractal methods to understand their complex interrelations, non-stationarities, and scaling behaviors, revealing significant cross-correlations and multifractal characteristics.

Contribution

It applies adaptive multifractal approaches (AMF-DFA and AMF-DXA) to assess stock market comovements, providing new insights into their non-stationary and multifractal nature.

Findings

Only 170 pairs of markets are cointegrated.

Most markets exhibit non-stationary behavior with $h(q=2)>1$.

All pairs show cross-correlation and multifractality.

Abstract

Stock market comovements are examined using cointegration, Granger causality tests and nonlinear approaches in context of mutual information and correlations. Underlying data sets are affected by non-stationarities and trends, we also apply AMF-DFA and AMF-DXA. We find only 170 pair of Stock markets cointegrated, and according to the Granger causality and mutual information, we realize that the strongest relations lies between emerging markets, and between emerging and frontier markets. According to scaling exponent given by AMF-DFA, $h(q=2)>1$, we find that all underlying data sets belong to non-stationary process. According to EMH, only 8 markets are classified in uncorrelated processes at $2\sigma$ confidence interval. 6 Stock markets belong to anti-correlated class and dominant part of markets has memory in corresponding daily index prices during January 1995 to February 2014. New-Zealand with $H=0.457\pm0.004$ and Jordan with $H=0.602\pm 0.006$ are far from EMH. The nature of cross-correlation exponents based on AMF-DXA is almost multifractal for all pair of Stock markets. The empirical relation, $H_{xy}\le [H_{xx}+H_{yy}]/2$, is confirmed. Mentioned relation for $q>0$ is also satisfied while for $q<0$ there is a deviation from this relation confirming behavior of markets for small fluctuations is affected by contribution of major pair. For larger fluctuations, the cross-correlation contains information from both local and global conditions. Width of singularity spectrum for auto-correlation and cross-correlation are $\Delta \alpha_{xx}\in [0.304,0.905]$ and $\Delta \alpha_{xy}\in [0.246,1.178]$, respectively. The wide range of singularity spectrum for cross-correlation confirms that the bilateral relation between Stock markets is more complex. The value of $\sigma_{DCCA}$ indicates that all pairs of stock market studied in this time interval belong to cross-correlated processes.