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Record W2084353633 · doi:10.1108/10867371211203819

Consumption hedging and home‐country bias in a model of international capital market equilibrium

2012· article· en· W2084353633 on OpenAlexaff
Jacques A. Schnabel

Bibliographic record

VenueStudies in Economics and Finance · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsEconomicsPortfolioCapital market lineCapital asset pricing modelMarket portfolioHedgeSecurity market lineConsumption (sociology)EconometricsFinancial economicsMicroeconomicsStock marketContext (archaeology)Market depth

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to develop a model of international capital market equilibrium where investors exhibit home‐country bias due to their desire to hedge real consumption. Design/methodology/approach This paper posits a two‐stage process of portfolio choice for the representative investor of a country. In the first step, the investor's benchmark portfolio is determined, whereas in the second step, his optimal portfolio is chosen. The latter portfolio maximizes the expected portfolio rate of return minus the risk tolerance weighted variance of tracking error. The market equilibrium implications of the portfolio optimality conditions are determine via aggregation across all investors and countries. Findings A revised security market line is derived that differs from the traditional security market line in terms of vertical intercept, slope, and beta coefficient. It is demonstrated that the derived model may be interpreted as a multi‐country generalization of the Chen‐Boness extension of the capital asset pricing model under uncertain inflation. Originality/value This paper presents an innovative application of Roll's tracking portfolio paradigm. Another novel feature is the derivation of the international capital market equilibrium implications of such portfolio choice behaviour.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.074
GPT teacher head0.259
Teacher spread0.185 · 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 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

Citations3
Published2012
Admission routes1
Has abstractyes

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