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Record W2111132118 · doi:10.1093/jjfinec/nbm010

Positivity Conditions for a Bivariate Autoregressive Volatility Specification

2007· article· en· W2111132118 on OpenAlexaff
Christian Gouriéroux

Bibliographic record

VenueJournal of Financial Econometrics · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBivariate analysisEconometricsAutoregressive modelVolatility (finance)Positive definitenessEconomicsMathematicsStochastic volatilityForward volatilityStatisticsPositive-definite matrix

Abstract

fetched live from OpenAlex

We derive necessary and sufficient conditions for the positive definiteness of the predicted volatility matrix in a bivariate autoregressive volatility specification. These nonlinear inequality restrictions have strong implications in terms of causality between volatilities and covolatilities.

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)0.006
metaresearch head score (Gemma)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0180.002

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 head0.069
GPT teacher head0.279
Teacher spread0.210 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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