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Record W263498020

Searching for New Paradigms at BIS: Market Turmoil Has Thrown VaR, and Basel II, a Curve

2008· article· en· W263498020 on OpenAlexaboutno aff
Ed Blount

Bibliographic record

VenueABA banking journal · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsBasel IBasel IIBasel IIIEconomicsFinancial systemCapital adequacy ratioCapital requirementLoanBusinessMonetary economicsFinanceMarket economy
DOInot available

Abstract

fetched live from OpenAlex

[ILLUSTRATION OMITTED] To central bankers, the implementation of the Basel II Capital Accord was planned as a stimulant to improve banks management practices, as well as to formulate appropriate, risk-sensitive capital levels for the global banking system. [ILLUSTRATION OMITTED] Now, after building up very high expectations and investing enormous intellectual capital in the reformulation of Basel II, many bankers and regulators, not to mention bank investors, have fallen disappointed at the unexpected deficiencies in banking capital, especially for complex global financial institutions, that have been exposed by the recent market turmoil. Even the improved management systems of these large banks--as stimulated by Basel II--have become orphaned by their erstwhile supporters. Consider that, for the past dozen years or so, the most popular paradigm has been VaR, or, value at Central bankers seem to have concluded that banks that relied on VaR tended to operate in ways which exaggerated the banking systems' natural procyclicality. That is, banks using VaR made too many loans into the credit boom economy, resulting in an overstimulation of the business cycle. VaR failed to prevent participants from building excess leverage, especially when market volatility was deceptively low. As a result, the VaR techniques be complemented by stress testing and by basic judgment and indicators, the deputy general manager of the Bank for International Settlements, Herve Hannoun, told a group of central bankers meeting in Ottawa on May 8. Among the simple indicators to be considered, Hannoun proposed maximum loan-to-value ratios for mortgage loans, capital charges on structured investment vehicles, leverage ratios, and dynamic provisioning. Each of these has a precedent in one or more national regulatory structures, he pointed out. Three weeks later, his boss at BIS, general manager Malcolm Knight, told a meeting of international securities regulators in Paris on May 29 that risk managers must rely on a wider range of tools to capture the multi-dimensionality of because tail exposures--including the of illiquidity--are not well measured by tools such as value at risk. Despite the relative rarity of losses out on the tails of a distribution, in some cases the bell-curve-shaped distribution is so flat that the actual value of losses can bankrupt banks and threaten financial markets. The bottom line on VaR is that it is so reliant on volatility as a measure of risk, that VaR adherents missed the entire accumulation of risky positions since there was very low volatility. Knight recommended several corrective actions in order to reduce the risks in today's market-dependent financial system. To begin, he emphasized the need for less complexity and more transparency in the securitization chain. He criticized mechanistic reliance on ratings agencies, explaining that their views should be supplemented with analyses of liquidity and of events that could trigger sudden ratings changes, especially for tranche-based instruments. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.013
Scholarly communication0.0130.024
Open science0.0010.003
Research integrity0.0100.019
Insufficient payload (model declined to judge)0.0160.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.244
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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