Systemic Risk Contributions

By Xin Huang, Hao Zhou, Haibin Zhu. Journal of financial services research, 2011

392 citations recorded by the source corpus.

We adopt a systemic risk indicator measured by the price of insurance against systemic financial distress and assess individual banks’ marginal contributions to the systemic risk. The methodology is applied using publicly available data to the 19 bank holding companies covered by the U.S. Supervisory Capital Assessment Program (SCAP), with the systemic risk indicator peaking around $1.1 trillion in March 2009. Our systemic risk contribution measure shows interesting similarity to and divergence from the SCAP loss estimates under stress test scenarios. In general, we find that a bank’s contribution to the systemic risk is roughly linear in its default probability but highly nonlinear with respect to institution size and asset correlation.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ae5ad59e094dcfb9edab08d9622d6485cf502cea

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Citations in the corpus

Elimination of systemic risk in financial networks by means of a systemic risk transaction tax (2014, background): “[41] Xin Huang, Hao Zhou, and Haibin Zhu.”
Bank Risk During the Financial Crisis: Do Business Models Matter? (2011, background): “B Asset structure Size can be an important determinant of banks’ risk (Huang et al., 2011; Drehmann and Tarashev, 2011; Tarashev et al., 2009).”
Macroprudential policy and bank systemic risk (2020, neutral): “(2016) who document that bank CDS spreads and stock prices have reacted to bail-in events in Europe or Ignatowski et al. (2014) who report that banks most affected by changes in the US bank resolution regime significantly decrease their overall risk taking.”
Stress scenario selection by empirical likelihood (2015, methodology): “More recently, MES and MES-like measures have become part of the stress testing toolkit as measures of systemic risk; see, in particular, Acharya et al. (2010), Adrian and Brunnermeier (2009) and Huang et al. (2012).”
Systemic Risk and Bank Size (2018, positive): “…importance and firm size is typically an important driver of systemic risk measures (see, for instance, De Jonghe, 2010; Hovakimian et al., 2012; Huang et al., 2012; Vallascas and Keasey, 2012; Zhang et al., 2015; Laeven et al., 2016; Sedunov, 2016), there is growing evidence that size may not…”
Too-Systemic-To-Fail: What Option Markets Imply About Sector-Wide Government Guarantees (2016, positive): “(5)See Kareken and Wallace (1978) and Panageas (2010). (6)Our paper is also related to recent studies of the relative pricing of derivative securities. Coval, Jurek, and Stafford (2009) and Collin-Dufresne, Goldstein, and Yang (2010) compare the prices of CDX tranches to those of index options prior”
The multi-layer network nature of systemic risk and its implications for the costs of financial crises (2015, background): “DIP measures the price of insurance against systemic financial distress in the banking system and is closely related to the expected shortfall [6].”
On dependence consistency of CoVaR and some other systemic risk measures (2012, background): “In addition to systemic risk measures [cf. 2, 4, 14, 15, 1, 29, 16], related topics include the structure of interbank networks, e.g., [8, 11], models explaining how systemic risk is created, e.g., [10, 17], and attribution of systemic risk charges within a ﬁnancial system, as discussed in [26, 25].”
Testing Macroprudential Stress Tests: The Risk of Regulatory Risk Weights (2014, neutral): “6With the difference that Huang et al. (2012) use the marginal expected shortfall (MES) instead of the long-run counterpart LRMES, and the MES is multiplied by the Tier 1 capital instead of the market cap.”
Questioni di Economia e Finanza (2010, background): “Huang et al. (2012) suggest a framework for measuring and stress testing the systemic risk of a group of financial institutions as the price of insurance against financial distress, based on default probabilities of individual banks and forecasted asset return correlations.”