Model-free and model-based connectedness in highly, medium and lowly correlated financial returns: analyses of OECD inflations
Bibliographic record
Abstract
This paper deals with the analysis of inflation in financial returns by using model-free connectedness framework which includes investigating persistence in the series and data from 22 countries from April 1958 to November 2023 which are grouped into highly, medium and lowly correlated returns. The results indicate that 10 countries, among the members of G12 are listed among highly-medium correlated inflation returns. The G7 countries are listed with high-medium inflation returns, of which France, Germany, Italy, and the USA are net shock transmitters, while Canada, Japan and the UK are net shock receivers. Total connectedness indices are positively related to the correlations, and the connectedness is found to increase astronomically towards late 2020 due to economic and financial market integration. Global financial crisis such as that of 2007–2009 and the COVID-19 pandemic have reset the integration of economic variables again. A policy recommendation is therefore given at the end.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".