Economic policy uncertainty and return on financial assets: the G7 case
Bibliographic record
Abstract
This dissertation aims at understanding the impact of economic policy uncertainty on the stock and bond market returns of the group of seven. We test the hypothesis that higher uncertainty levels cause decreases on these financial assets returns. We analyse the impact that the economic policy uncertainty index and previous returns have on current returns and if an increase of economic policy uncertainty lead to an increase of conditional volatility. This research relies on the EPU Index, developed by Baker et al. (2016), and employs daily data averaged into monthly time series for G7 stock and bond markets, covering the period from January 2000 to December 2016. Concerning the stock market, we find evidence of a significant impact of EPU on current returns for most of the countries. Canada and Italy are the exceptions. Concerning conditional volatility, we report that the EPU has impact on United States (U.S.), United Kingdom (U.K.), Canada, Germany and Japan. EPU does not show significant results to France and Italy. In the bond market and in contrast with previous returns, EPU does not have a great impact on the current returns for most of the countries. In what concerns conditional volatility, the previous returns have no influence for the entire sample and EPU presents significant results for U.S., Canada, Germany, Italy and Japan.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".