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Record W3121524279 · doi:10.1111/jofi.12970

Model‐Free International Stochastic Discount Factors

2020· article· en· W3121524279 on OpenAlex

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

VenueThe Journal of Finance · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsTrojan Technologies (Canada)
Fundersnot available
KeywordsStochastic discount factorSalientFinancial marketWedge (geometry)Incomplete marketsEconometricsCapital asset pricing modelVolatility (finance)EconomicsMarket segmentationFinancial economicsMathematicsComputer scienceFinanceArtificial intelligenceMicroeconomics

Abstract

fetched live from OpenAlex

ABSTRACT We provide a theoretical framework to uncover in a model‐free way the relationships among international stochastic discount factors (SDFs), stochastic wedges, and financial market structures. Exchange rates are in general different from the ratio of international SDFs in incomplete markets, as captured by a stochastic wedge. We show theoretically that this wedge can be zero in incomplete and integrated markets. Market segmentation breaks the strong link between exchange rates and international SDFs, which helps address salient features of international asset returns while keeping the volatility and cross‐country correlation of SDFs at moderate levels.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.332

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 science0.0010.000
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 head0.081
GPT teacher head0.237
Teacher spread0.156 · 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