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Record W4410555711 · doi:10.1515/snde-2024-0012

Identifying Shock Propagation Mechanisms in Global Equity Markets

2025· article· en· W4410555711 on OpenAlexaff
Vance L. Martin, Saikat Sarkar

Bibliographic record

VenueStudies in Nonlinear Dynamics and Econometrics · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsYork University
Fundersnot available
KeywordsEconomicsEmerging marketsDiversification (marketing strategy)Risk premiumFinancial economicsCountry riskPortfolioVolatility (finance)Equity (law)Monetary economicsCapital asset pricing modelEconometricsBusinessFinance

Abstract

fetched live from OpenAlex

Abstract The intertemporal capital asset pricing model with time-varying price and quantity risk factors, is used to study the propagation mechanisms linking expected risk premia with shocks in global equity markets. The model allows for linear and nonlinear propagation channels with the relationship between expected risk premia and world and country shocks characterized by a bivariate cubic. Using daily data on developed and emerging country equity returns, the empirical results show that country risk prices are exposed to world risks, although the signs differ between developed and emerging countries. Country risk factors are especially important for risk prices in Asia-Pacific countries as well as selected emerging countries. Of the financial risk factors investigated, volatility risk is important for all developed countries, and currency basis risk is important for all emerging countries. The nonlinear propagation mechanisms linking shocks and expected risk premia are economically significant and show that the size and sign of shocks are important. Implications of the empirical results for international portfolio diversification are also investigated using a range of simulation experiments.

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)0.002
metaresearch head score (Gemma)0.007
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.063
GPT teacher head0.311
Teacher spread0.247 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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