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Record W4400412507 · doi:10.1016/j.irfa.2024.103429

Why does uncovered interest parity fail empirically?

2024· article· en· W4400412507 on OpenAlexaff
Nusrate Aziz

Bibliographic record

VenueInternational Review of Financial Analysis · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsAlgoma UniversityWilfrid Laurier UniversityThompson Rivers University
Fundersnot available
KeywordsInterest rate parityEconomicsExchange rateInterest rateEconometricsDe factoInternational Fisher effectEmpirical evidenceReal interest rateCapital marketFallacyPanel dataMonetary economicsFinancial economicsFisher hypothesisFinance

Abstract

fetched live from OpenAlex

The Uncovered Interest Parity theory, predicated on perfect capital mobility and a floating exchange rate regime, faces challenges in real-world contexts marked by capital restrictions and diverse exchange rate regimes. This research investigates the validity of the UIP hypothesis in selected OECD countries, considering the role of capital control and de facto exchange rate regimes. By analyzing annual and monthly data, including immediate rates, short-term borrowing rates, and 10-year government bond rate differentials, several panel estimation techniques and alternative empirical models are employed. The results are robust to sub-sample analyses, alternative specifications of the empirical model, and different estimation methods. The study provides evidence supporting the UIP, particularly for immediate and short-term interest rates. The empirical examination underscores the necessity of accounting for the de facto floating exchange rate regime and capital flow restrictions when testing the UIP model. Consequently, the UIP hypothesis moves beyond being a theoretical fallacy and emerges as empirically verifiable, holding significant implications for policymakers and advancing the understanding of international finance.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.065
GPT teacher head0.293
Teacher spread0.227 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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