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Record W3176549043 · doi:10.15353/rea.v15i2.5213

The Effect of US Uncertainty Shock on International Equity Markets: The Role of the Global Financial Cycle

2023· article· en· W3176549043 on OpenAlexvenueno aff
Afees A. Salisu, Rangan Gupta, Idris A. Adediran

Bibliographic record

VenueReview of Economic Analysis · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsEquity (law)Shock (circulatory)Financial marketEconomicsFinancial systemFinancial economicsBusinessMonetary economicsFinancePolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

We contribute to the literature on the international propagation of uncertainty shocks with a Global Vector Autoregressive (GVAR) model that quantifies the spillover effects of uncertainty shocks in the US on to real equity prices of 32 advanced and emerging countries (besides the US). In this regard, we also account for the role of global financial market conditions in the propagation of these shocks, using high and low values of the Global Financial Cycle (GFCy) index. Using quarterly data over 1980:1 to 2019:2, our findings reveal greater response of advanced markets than emerging counterparts to an US uncertainty shock. Further, we show consistent higher negative responses during weak financial conditions than otherwise, confirming the intervening role of the GFCy index. Our results have important implications for investors and policymakers.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.268
Teacher spread0.259 · 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 designSimulation or modeling
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

Citations4
Published2023
Admission routes1
Has abstractyes

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