Bibliographic record
Abstract
Multivariate models are of central interest in several fields of Financial Econometrics. First of all, international financial markets are dependent of each other and one must consider them jointly to understand the dynamic structure of international finance. For example, De Santis and Gerard (1997) test the CAPM for the world’s eight largest equity markets using a multivariate GARCH model. Their results indicate that, although severe market declines are contagious, the expected gains from international diversification for a US investor average 2.11 percent per year and have not significantly declined over the last two decades. Karolyi (1995) analyses the short-run dynamics of stocks and volatility for stocks traded on the New York and Toronto stock exchanges. He shows that inferences about the persistence of returns and the transmission of effect depend importantly on how the dynamics in volatility are modelled. He also discusses the implications for international asset pricing, hedging strategies and regulatory policy. The results of Kearney and Patton (2000) on exchange rate volatility transmission across the European Monetary System also indicate the importance of checking for specification on multivariate GARCH models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.014 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".