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Record W2608587812

Monetary easing and financial instability

2017· preprint· en· W2608587812 on OpenAlexfundno aff
Viral V. Acharya, Guillaume Plantin

Bibliographic record

VenueLondon School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2017
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsnot available
FundersEconomic and Social Research CouncilYork University
KeywordsCarry (investment)Monetary economicsMonetary policyEconomicsFinancial intermediaryRollover (web design)Quantitative easingInvestment (military)Interest rateFinancial marketMarket liquidityFinanceFinancial systemCentral bank
DOInot available

Abstract

fetched live from OpenAlex

We study optimal monetary policy in the presence of financial stability concerns. We build a model in which monetary easing can lower the cost of capital for firms and restore the natural level of investment, but does also subsidize inefficient maturity transformation by financial intermediaries in the form of “carry trades" that borrow cheap at the short-term against illiquid long-term assets. Carry trades not only lead to financial instability in the form of rollover risk, but also crowd out real investment since intermediaries equate the marginal return on lending to firms to that on carry trades. Optimal monetary policy trades off any stimulative gains against these costs of carry trades. The model provides a framework to understand the puzzling phenomenon that the unprecedented post-2008 monetary easing has been associated with below-trend real investment, even while returns to real and financial capital have been historically high.

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)0.014
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.000
Science and technology studies0.0010.018
Scholarly communication0.0020.002
Open science0.0030.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.087
GPT teacher head0.349
Teacher spread0.263 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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