An Associative Analysis Method to Estimate Impact between Financial Market Risk and Macroeconomic Risk
Bibliographic record
Abstract
The intertwining and close correlation between financial market risk and macroeconomic risk have been a focal point of academic research. By constructing financial stress index and macroeconomic risk index, and employing the Time-Varying Parameter Vector Autoregression (TVP-VAR) model, this study analyzes the complex dynamic interactions between financial market risk and macroeconomic risk. The results indicate a bidirectional and intersecting relationship between financial market risk and macroeconomic risk. Financial market risk exerts a relatively significant impact on macroeconomic risk, and its accumulation exacerbates the downward pressure on the macroeconomy, while its alleviation does not promptly lead to economic prosperity. Moreover, there exists a significant time-varying correlation between financial market risk and macroeconomic risk, with macroeconomic risk continuously augmenting its promoting effect on financial market risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".