MétaCan
Menu
Back to cohort
Record W4406217179 · doi:10.3390/jrfm18010025

Oil Shocks, US Uncertainty, and Emerging Corporate Bond Markets

2025· article· en· W4406217179 on OpenAlexvenueno aff
Dohyoung Kwon

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
FundersGachon University
KeywordsBondCorporate bondEmerging marketsBusinessMonetary economicsFinancial economicsEconomicsFinancial systemFinance

Abstract

fetched live from OpenAlex

Using a structural VAR model, this paper investigates how oil price shocks and US uncertainty affect emerging market corporate bond returns. The key finding is that the response of emerging market corporate bond returns varies significantly depending on the underlying sources of oil price changes. Oil supply shocks generally have a negative impact on corporate bond returns, while aggregate demand and oil market-specific demand shocks lead to a temporary increase in returns, followed by a gradual fall. That is, when oil price increases are driven by stronger global economic activity or by speculative demand reflecting increased risk appetite, they can lead investors to search for higher yields in emerging markets, and thus raise corporate bond returns in the short term. Conversely, an unexpected rise in US uncertainty strengthens investors’ risk aversion and results in a substantial decline in emerging market corporate bond returns. These findings have crucial policy implications not only for portfolio strategies of global investors, but also for government authorities in emerging market economies.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.204
Teacher spread0.194 · 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 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

Citations9
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of risk and financial managementSame topicMarket Dynamics and VolatilityFrench-language works237,207