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Record W4297576373 · doi:10.3386/w30418

Foreign Reserves Management and Original Sin

2022· report· en· W4297576373 on OpenAlexafffund
Michael Devereux, Steve Pak Yeung Wu

Bibliographic record

VenueNational Bureau of Economic Research · 2022
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsUniversity of British Columbia
FundersCanadian Intensive Care Foundation
KeywordsBusinessGeography

Abstract

fetched live from OpenAlex

This paper studies the interaction between foreign exchange reserves and the currency composition of sovereign debt in emerging countries.Focusing on inflation targeting countries, we find that holdings of foreign reserves are associated with higher local currency sovereign debt, an exchange rate which is less sensitive to global shocks, and a lower exchange rate risk premium in local currency sovereign spreads.We rationalize these findings within a financially constrained model of a small open economy.The Sovereign values local currency debt as a hedge against endowment risk, but since the exchange rate tends to depreciate in times of global downturns, risk averse international investors charge an additional currency risk premium on this debt.When a country optimally uses foreign reserves to lean against the wind in response to global shocks, this dampens the response of the exchange rate, providing insurance for the global investor.By reducing the risk premium on local currency debt, foreign exchange reserves therefore facilitate a higher share of local currency debt in the sovereign portfolio.Quantitatively, we find the welfare benefits for the sovereign from optimal foreign reserves management can be very large.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0210.003

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 head0.388
GPT teacher head0.474
Teacher spread0.086 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations11
Published2022
Admission routes2
Has abstractyes

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