Too Connected to Fail? Inferring Network Ties From Price Co-Movements

Abstract

We use extreme value theory methods to infer conventionally unobservable connections between financial institutions from joint extreme movements in credit default swap spreads and equity returns. Estimated pairwise co-crash probabilities identify significant connections among up to 186 financial institutions prior to the crisis of 2007/2008. Financial institutions that were very central prior to the crisis were more likely to be bailed out during the crisis or receive the status of systemically important institutions. This result remains intact also after controlling for indicators of too-big-to-fail concerns, systemic, systematic, and idiosyncratic risks. Both credit default swap (CDS)-based and equity-based connections are significant predictors of bailouts. Supplementary materials for this article are available online.

Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy

“…This is in line with the "two-eight divergence" that often occurs in the Chinese stock market, i.e., when the heavyweight stocks show significant gains, small-and mid-cap stocks tend to be less volatile or even decline. This phenomenon tends to be different from the performance of financial markets in developed countries, and Bosma et al [55] found through a correlation network among as many as 186 financial institutions in the 2007-2008 U.S. financial crisis that systemically important financial institutions with large sizes and market capitalisations are also at a more central position in terms of the correlation status of their stock returns. Overall, there tends to be a higher overlap between "too big to fail" and "too correlated to fail" institutions and stocks in developed country stock markets, i.e., stocks with larger market capitalisations tend to be at the centre of correlation and connectivity in stock markets.…”

Effectiveness and (In)Efficiencies of Compensation Regulation: Evidence from the EU Banker Bonus Cap

“…Yet, most scholars agree on a range of mutually nonexclusive drivers of systemic crises, which are common exposures of banks to overvalued assets that are subject to sudden corrections, subsequent liquidity freezes, and fire sales that cause financial market breakdowns (see, e.g., Acharya, 2009;Tirole, 2011;Wagner, 2011;Brunnermeier, Rother, and Schnabel, 2020). Gridlock in financial markets fuels the contagion of insolvency risk via observable and unobservable financial networks among banks (Glasserman and Young, 2016;Bosma, Koetter, and Wedow, 2019), of which some are considered too big, too connected, too many, or otherwise too important to fail, triggering government intervention (Acharya and Yorulmazer, 2007;Brown and Dinc, 2009;Farhi and Tirole, 2012;Freixas and Rochet, 2013). Given the ongoing debate about the sources of systemic financial crises, we remain agnostic as to the exact mechanisms explaining systemic risk.…”

“…The data are obtained from Table B.I of Carbó-Valverde, Cuadros-Solas, and Rodríguez-Fernández (2020), Table A.3 ofBosma et al (2019), and the state aid case-search engine of the European Commission (see http://ec.europa.eu/competition/elojade/isef/).…”

Identifying edges that facilitate the generation of extreme events in networked dynamical systems

“…For systems that can be described by a time-dependent (or evolving) interaction network, novel methods have been developed over the last years that allow one to identify precursors of extreme events 8 . This holds true particularly for climate extremes [9][10][11][12][13][14][15][16][17][18][19][20] , seismic extremes 21 , hydrological extremes 22 , economic extremes [23][24][25] , and epileptic seizures 26,27 . Methods employed so far either aim at assessing global networks properties (e.g., clustering-coefficient-related or path-related measures) or local network properties -mostly vertex centralities 28 .…”