6. What We Know, Don’t Know, and Can’t Know about Bank Risk: A View from the Trenches

Kuritzkes, Andrew

doi:10.1515/9781400835287-007

Cited by 24 publications

(15 citation statements)

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“…commissions) owing to different underlying activities. In a survey of risk management practices, Kuritzkes and Schuermann (2010) report that further research is necessary regarding business risks and structural interest rate risks (nontrading i.e. mismatch of repricing between liabilities and assets) of banks.…”

Section: Related Literature and Research Focusmentioning

confidence: 99%

US bank holding companies: Structure of activities and performance through the cycles

Albert

2015

International Review of Financial Analysis

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Section: Related Literature and Research Focusmentioning

confidence: 99%

US bank holding companies: Structure of activities and performance through the cycles

Albert

2015

International Review of Financial Analysis

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“…trading) risk is by far the most precisely measured and modeled, and in that way the most tightly controlled. Kuritzkes and Schuermann (2010) place market risk at the most benign end of the spectrum of known, unknown and unknowable risks, typically making up only around 6% of total risk in a bank (though more in some, of course).…”

Section: Policy Implicationsmentioning

confidence: 99%

Bank Capital for Operational Risk: A Tale of Fragility and Instability

Ames¹,

Schuermann²,

Scott³

2014

SSRN Journal

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Operational risk is fundamentally different from all other risks taken on by a bank. It is embedded in every activity and product of an institution, and in contrast to the conventional financial risks (e.g. market, credit) is harder to measure and model, and not straight forwardly eliminated through simple adjustments like selling off a position. Operational risk tends to be about 9-13% of the total risk pie, though growing rapidly since the 2008-09 crisis. It tends to be more fat-tailed than other risks, and the data are poorer. As a result, models are fragile -small changes in the data have dramatic impacts on modeled output -and thus required operational risk capital is unstable. Yet the regulatory capital regime is, surprisingly, more rigidly model focused for this risk than for any other, at least in the U.S. We are especially concerned with the absence of incentives to invest in and improve business control processes through the granting of regulatory capital relief. We make four, not mutually exclusive policy suggestions. First, address model fragility through anchoring of key model parameters, yet allow each bank to scale capital to their data using robust methodologies. Second, relax the current tight linkage between statistical model output and required regulatory capital, incentivizing prudent risk management through joint use of scenarios and control factors in addition to data-based statistical models in setting regulatory capital. Third, provide allowance for real risk transfer through an insurance credit to capital, encouraging more effective risk sharing through future product innovation. Fourth, expand upon the standard taxonomy of event type and business line to include additional explanatory variables (such as product type, flags for litigated events, etc.) that would allow more effective interbank sharing and learning from experience. Until our understanding of operational risks increases, required regulatory capital should be based on methodologies that are simpler, more standardized, more stable and more robust. (Corresponding author: til.schuermann@oliverwyman.com). Professor Scott's participation in this project was supported under his general consulting relationship with State Street Corporation. We would like to thank Eduardo Canabarro, Misha Dobrolioubov, Ramy Farha, John Jordan, Andy Kuritzkes and Daniel Mikkelsen for helpful comments and discussion. All remaining errors are ours, of course.

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“…As discussed in the introduction, in line with current market and regulatory practices, we set the level of capital such that it equals the amount a bank needs to absorb unexpected losses over a certain time horizon at a given confidence level (Basel Committee, 2008 or Kuritzkes andSchuermann, 2007 NI provides positive contributions to net profits, so we are interested in the left tail of the distribution. Therefore z is in this case below or equal to 0.1.…”

Section: Economic Capitalmentioning

confidence: 99%

An Economic Capital Model Integrating Credit and Interest Rate Risk in the Banking Book

Alessandri

Drehmann

2010

SSRN Journal

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6. What We Know, Don’t Know, and Can’t Know about Bank Risk: A View from the Trenches

Cited by 24 publications

References 0 publications

US bank holding companies: Structure of activities and performance through the cycles

US bank holding companies: Structure of activities and performance through the cycles

Bank Capital for Operational Risk: A Tale of Fragility and Instability

An Economic Capital Model Integrating Credit and Interest Rate Risk in the Banking Book

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