Network, market, and book-based systemic risk rankings

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe investigate the information content of stock correlation based network measures for systemic risk rankings, such as SIFIRank (based on Google's PageRank). Using European banking data, we first show that SIFIRank is empirically equivalent to a ranking based on average pairwise stock correlations. Next, we find that correlation based network measures still appear to complement currently available systemic risk ranking methods based on book or market values. A further analytical investigation, however, shows that the value-added appears to be mainly attributable to pairwise cross-sectional heterogeneity rather than to more subtle network relations and feedback loops.

Dynamic correlation of market connectivity, risk spillover and abnormal volatility in stock price

“…So for the latter two stages, the model of whether to introduce financing and financing quota is designed respectively. Financing and financing business is usually considered to enhance market liquidity; therefore, stocks with higher financing and financing quota may be more liquid and receive more investors' attention, calculated for the last two periods of our study using Equation (5).…”

“…Complex networks are widely applied to financial markets, such as stock connections, derivative price correlations, and investor sentiment [1,2,3,4,5,6]; increasingly, scholars focus are focusing on the abnormal networking structure during crisis periods [7,8,9,10,11,12], showing different national stock markets share similar patterns over several crises, while more heterogeneities exist between developed and developing countries. Onnela et al [13] employ the minimum spanning tree to construct the US equity market network from 1982 to 2000 and study the impact of Black Monday on the US market.…”

Risk Spillovers and Interconnectedness between Systemically Important Institutions

“…Quantifying the risk of distress is important from both financial stability and macroeconomic standpoints, as financial crises tend to have significant and persistent negative effects on the real economy (Acemoglu et al, 2012;Avdjiev et al, 2019). However, as systemic risk varies over time, institutions may prove not to be systemically important in some periods while remaining critical in others (Elliott and Litan, 2011) such that the systemic risk rankings may move in opposite directions (van de Leur et al, 2017). Moreover, most of the systemic risk measures proposed in the literature are market-and/or accounting-based.…”

Systemic Risk in the Chinese Stock Market Under Different Regimes: A Sector-Level Perspective

“…Following this intuition, DebtRank was introduced, which is relevant to the field of complex networks in general and can be applied to detect systemically important nodes in any directed and weighted network. Van De Leur et al (2017) propose a measure based on average pairwise correlations (CorrRank) that is much simpler than SIFI Rank (based on Google's PageRank), showing that direct (rather than indirect) connections in the network determine the systemic risk contributions. Silva et al (2018) simulate shocks to the real sector and evaluate how the financial system reacts and amplifies these events using unique Brazilian loan-level data between banks and firms.…”