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Record W3123759354

Endogenous Inflows of Speculative Capital and the Optimal Currency Appreciation Path

2009· preprint· en· W3123759354 on OpenAlexaff
Mei Li, Junfeng Qiu

Bibliographic record

VenueRePEc: Research Papers in Economics · 2009
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMonetary economicsMarket liquidityCommitEconomicsCurrencyCapital (architecture)Exchange ratePath (computing)
DOInot available

Abstract

fetched live from OpenAlex

This paper examines the optimal appreciation path of an under-valued currency in the presence of speculative capital inflows that are endogenously affected by the appreciation path. A central bank decides the optimal appreciation path based on three factors: (i) Misalignment costs associated with the gap between the actual exchange rate and the fundamental exchange rate, (ii) short-term adjustment costs due to fast appreciation, and (iii) capital losses due to speculative capital inflows. We examine two cases in which speculators do and do not face liquidity shocks. We show that, in the case without liquidity shocks, the central bank should appreciate quickly to discourage speculative capital, and should appreciate more quickly in initial periods than in later periods. In the case with liquidity shocks, the central bank should pre-commit to a slow appreciation path to discourage speculative capital. The central bank should appreciate slowest when the probability of liquidity shocks takes middle values. If the central bank cannot commit and can only take a discretionary policy, appreciation should be faster.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.267
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations1
Published2009
Admission routes1
Has abstractyes

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