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Record W3125683144

International Asset Pricing Under Segmentation and PPP Deviations

2005· article· en· W3125683144 on OpenAlexaff
Ines Chaieb, Vihang R. Errunza

Bibliographic record

VenueArchive ouverte UNIGE (University of Geneva) · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsMcGill University
Fundersnot available
KeywordsCapital asset pricing modelRisk premiumFinancial economicsBusinessPortfolioConsumption-based capital asset pricing modelPurchasing power parityEconomicsSecurity market lineMonetary economicsFinanceStock marketExchange rate
DOInot available

Abstract

fetched live from OpenAlex

We analyze the impact of both Purchasing Power Parity (PPP) deviations and barriers to international investment on asset pricing and investor’s portfolio holdings. The freely traded securities are priced similarly to Adler and Dumas (1983) and command two premiums: a global market risk premium and an inflation risk premium. The securities that can be held by only a subset of the investors command two additional premiums; a conditional market risk premium in the vein of Errunza and Losq (1985) and a segflation risk premium from bearing inflation risk in the presence of barriers. Our model nests several existing international asset pricing models and thus provides a framework to distinguish empirically between competing models. We test the conditional version of our model for eight major emerging markets. We use global market and industry portfolios, US and UK traded closed-end country funds, American Depository Receipts and Global Depository Receipts to replicate the returns on unattainable securities. We find that the global market, the conditional market and the global exchange risks are significantly priced as in previous research. Our results also point to the importance of the segflation risk which is statistically and economically significant.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.890
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.020
GPT teacher head0.192
Teacher spread0.172 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2005
Admission routes1
Has abstractyes

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