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Record W2917411133 · doi:10.1111/irfi.12252

Market Volatility Risk and Stock Returns around the World: Implication for Multinational Corporations*

2019· article· en· W2917411133 on OpenAlex
Samuel Xin Liang, K.C. John Wei

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Review of Finance · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsTyndale University
Fundersnot available
KeywordsVolatility (finance)Volatility risk premiumMarket riskVolatility riskFinancial economicsVolatility swapFactor marketEconomicsMarket microstructureBusinessMarket depthStock marketSecurity market lineVolatility smileImplied volatilityMonetary economicsEconometricsFinanceOrder (exchange)Market economy

Abstract

fetched live from OpenAlex

Abstract We investigate the pricing of market volatility risk as a risk factor—the innovation risk and as a characteristic risk—the level risk. We find that the pricing of the country‐level (local) market volatility risk factor is not robust across 21 developed markets and that the global market volatility risk factor prices 21 developed market portfolios after controlling for global market, value, and size factors. Capturing various market information, idiosyncratic market volatility as a country‐specific characteristic risk dominates global market, value, size, and market volatility risk factors in predicting returns of market portfolios. Countries with higher investor protection and accounting standards have higher country‐specific market volatility. Market volatility is higher in these countries because corporate managers take higher risks on innovative projects that benefit economic growth.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.273
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