Dynamic correlation of market connectivity, risk spillover and abnormal volatility in stock price
The connectivity of stock markets reflects the information efficiency of capital markets and contributes to interior risk contagion and spillover effects. We compare Shanghai Stock Exchange A-shares (SSE A-shares) during tranquil periods, with high leverage periods associated with the 2015 subprime...
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Veröffentlicht in: | Physica A 2022-02, Vol.587, p.126506, Article 126506 |
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Sprache: | eng |
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Zusammenfassung: | The connectivity of stock markets reflects the information efficiency of capital markets and contributes to interior risk contagion and spillover effects. We compare Shanghai Stock Exchange A-shares (SSE A-shares) during tranquil periods, with high leverage periods associated with the 2015 subprime mortgage crisis. We use Pearson correlations of returns, the maximum strongly connected subgraph, and 3σ principle to iteratively determine the threshold value for building a dynamic correlation network of SSE A-shares. Analyses are carried out based on the networking structure, intra-sector connectivity, and node status, identifying several contributions. First, compared with tranquil periods, the SSE A-shares network experiences a more significant small-world and connective effect during the subprime mortgage crisis and the high leverage period in 2015. Second, the finance, energy and utilities sectors have a stronger intra-industry connectivity than other sectors. Third, HUB nodes drive the growth of the SSE A-shares market during bull periods, while stocks have a think-tail degree distribution in bear periods and show distinct characteristics in terms of market value and finance. Granger linear and non-linear causality networks are also considered for the comparison purpose. Studies on the evolution of inter-cycle connectivity in the SSE A-share market may help investors improve portfolios and develop more robust risk management policies.
•A novel threshold determination method is proposed for constructing dynamic networks.•The dynamic evolution of the Shanghai Stock Exchange A-shares market is analyzed.•Factors driving the connectivity evolution of industries and stocks are investigated.•We develop the dynamic model of HUB nodes and show their impact on the stock market. |
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ISSN: | 0378-4371 1873-2119 |
DOI: | 10.1016/j.physa.2021.126506 |