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Record W2106077283 · doi:10.15353/rea.v10i4.1475

Liquidity Provision, Ambiguous Asset Returns and the Financial Crisis

2018· article· en· W2106077283 on OpenAlexvenueno aff
Willem Spanjers

Bibliographic record

VenueReview of Economic Analysis · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsMarket liquidityMoral hazardFinancial crisisLiquidity crisisLiquidity riskEconomicsMonetary economicsContext (archaeology)Transparency (behavior)AmbiguityBusinessFinancial systemFinanceMicroeconomicsMacroeconomics

Abstract

fetched live from OpenAlex

For an economy with dysfunctional intertemporal financial markets the financial sector is modelled as a competitive banking sector offering deposit contracts. In a setting related to Allen and Gale (JoF, 1998) properties of the optimal liquidity provision are analyzed for illiquid assets with ambiguous returns.In the context of our model, ambiguity -- i.e. incalculable risk -- leads to dynamically inconsistent investor behaviour. If the financial sector fails to recognize the presence of ambiguity, unanticipated fundamental crises may occur, which are incorrectly blamed on investors 'loosing their nerves' and 'panicing'.The basic mechanism of the Financial Crisis resembles the liquidation of illiquid assets during a banking panic. The combination of providing additional liquidity and supporting distressed financial institutions implements the regulatory policy suggested by the model.A credible commitment to such 'bail-out policy' does not create a moral hazard problem. Rather, it implements the second best efficient outcome by discouraging excessive caution. Reducing ambiguity by increasing stability, transparency and predictability -- as suggested by ordo-liberalism and the 'Freiburger Schule' -- enhances ex-ante welfare.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.260
Teacher spread0.242 · 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 teacher head, not a consensus.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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