Extreme risk spillovers among traditional financial and FinTech institutions: A complex network perspective

Wen, Shigang; Li, Minglu; Huang, Chuangxia; Zhu, Xiaoqian

doi:10.1016/j.qref.2023.01.005

Cited by 19 publications

(2 citation statements)

References 46 publications

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“…It has been revealed that large diversified financial institutes were the influential senders of extreme risk spillovers. Also, banking institutions had a crucial role in transmitting extreme risks within a specific period mentioned therein 18 .…”

Section: Related Workmentioning

confidence: 99%

“…In current literature, using different kinds of centrality measures can reveal the kind of influences through the identification of influential nodes. Existing literature also highlights that most central nodes are highly influential in a network, and network behavior can be analyzed by identifying those nodes 6,18 . In the perspective of directional information flow, Katz's centrality can capture the both-way directional influence, but it is computationally complex .…”

Section: Introductionmentioning

confidence: 99%

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Centrality measures on segmented entropy networks to identify influencers and influencees for financial market scenario

Sengupta,

Middya,

Roy

2023

Preprint

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The rise and fall in stock prices are mutually dependent on different intra and inter-sectoral stocks. These mutual dependencies through the interactive market transactions are responsible for creating idiosyncratic shocks regarded as an influence. These necessitate the identification of influential stocks in terms of influencers and influencees in any financial network. These kinds of influential effects can be adequately captured by the flow of directional impact. As a consequence, the influencers in the financial market create an impact through the ups and downs in the economy on the influencees. In this context, attempts have been made to exhibit these kinds of directional influences through segmented entropy networks (SEN) using transfer entropy measures. In addition to the fragmentation of different levels of information flow, i.e., strong, moderate and weak, the network sub-categories are expected to reduce information loss that might occur for construction of network using threshold values. To identify the set of influential stocks in terms of influencees and influencers, the in-centrality and out-centrality measures have been proposed. Their performance is compared using the NSE (national stock exchange) dataset of India with other existing centralities namely, betweenness centrality, harmonic centrality, etc. It is noted that the financial and energy sectors drive the market as a whole during the period. On the other hand, the healthcare, basic material, and consumer defensive sectors create a moderate impact on the market. Additionally, the top influential stocks had high market capitalization and generated high market revenue at that time. The effectiveness of the proposed model has also been compared in terms of its spreading efficacy using a weighted independent cascade model for financial networks. Empirically, the seed stocks (i.e., top influential stocks) captured by the proposed model can spread the impact among other companies over the market by more than 50\% for strong influential network. However, for all other existing baseline centralities, the spreading performance is approximately 40\%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%