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Evaluating the connectedness of commodity future markets via the cross-correlation network
Frontiers in Physics ; 10, 2022.
Article in English | Web of Science | ID: covidwho-2071115
ABSTRACT
Financial markets are widely believed to be complex systems where interdependencies exist among individual entities in the system enabling the risk spillover effect. The detrended cross-correlation analysis (DCCA) has found wide applications in examining the comovement of fluctuations among financial time series. However, to what extent can such cross-correlation represent the spillover effect is still unknown. This article constructs the DCCA network of commodity future markets and explores its proximity to the volatility spillover network. Results show a moderate agreement between the two networks. Centrality measures applied to the DCCA networks are able to identify key commodity futures that are transmitting or receiving risk spillovers. The evolution of the DCCA network reveals a significant change in the network structure during the COVID-19 pandemic in comparison to that of the pre- and post-pandemic periods. The pandemic made the commodity future markets more interconnected leading to a shorter diameter for the network. The intensified connections happen mostly between commodities from different categories. Accordingly, cross-category risk spillovers are more likely to happen during the pandemic. The analysis enriches the applications of the DCCA approach and provides useful insights into understanding the risk dynamics in commodity future markets.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: Frontiers in Physics Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: Frontiers in Physics Year: 2022 Document Type: Article