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Record W3124754330 · doi:10.24149/gwp49

Asymmetries and State Dependence: The Impact of Macro Surprises on Intraday Exchange Rates

2010· preprint· en· W3124754330 on OpenAlexaff
Rasmus Fatum, Michael M. Hutchison, Thomas Wu

Bibliographic record

VenueFederal Reserve Bank of Dallas, Globalization and Monetary Policy Institute Working Papers · 2010
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMonetary economicsEconomicsExchange rateInterest rateEquity (law)Financial marketFinancial economicsFinance

Abstract

fetched live from OpenAlex

The impact of news surprises on exchange rates depends in principle upon a number of factors including the state of the economy, institutional setting and nature of the expected policy response. These characteristics may lead to state-contingent asymmetric responses to news. In this paper we investigate the possible asymmetric response of intraday exchange rates (5-minute intraday JPY/USD) to macroeconomic news announcements during a very unusual period--Japan during 1999-2006 when the money market interest rate was effectively zero. We may think of this period as a natural experiment consisting of an institutional setting when interest rates may rise but not decline, thereby constraining both endogenous policy reactions to news and private market expectations. Asymmetric responses to news, to the extent that they are important in exchange rate markets as they are in equity markets, would seem particularly likely to be evident during this period. We consider several ways asymmetric responses may be manifested and linked to macroeconomic news during the zero-interest rate period. We assess whether the intraday exchange rate responds differently depending on whether the news is emanating from Japan or the U.S.; we consider the state of the business cycle; and we distinguish between good and bad news.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.289
Teacher spread0.230 · 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 designObservational
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

Citations3
Published2010
Admission routes1
Has abstractyes

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