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Record W2994315688 · doi:10.1111/irfi.12291

Currency hedging and quantitative easing: Evidence from global bond markets

2019· article· en· W2994315688 on OpenAlex
Lawrence Kryzanowski, Jie Zhang, Rui Zhong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Review of Finance · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsTrent UniversityConcordia University
Fundersnot available
KeywordsTreasuryQuantitative easingBondEconomicsMonetary economicsCurrencyFinancial economicsFinancial systemEconometricsCentral bankMonetary policyFinance

Abstract

fetched live from OpenAlex

Abstract We examine the influence of quantitative easing (QE) in the United States on hedging effectiveness and performance (E&P) of international bond portfolios. During the QE period, the bond portfolios have significantly lower excess returns and variances, and their excess returns (variances) are positive (negative) with the U.S. Federal Reserve's (Fed's) mortgage‐backed securities holdings and are less positive (less negative) with the Fed's Treasury holdings. E&P is higher for optimal versus full hedging during the QE versus pre‐QE period and differs for portfolios from developed and emerging countries. Results are robust using other hedging E&P measures and excluding countries with their own QEs implementations.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.300
Teacher spread0.252 · 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