Did equity returns and volatilities change after the 2016 Trump election victory?
Bibliographic record
Abstract
Abstract This paper investigates the reaction of equity markets to the 2016 US presidential elections in Canada, China, Mexico and Russia, and their interaction with the US market. This objective is carried out by studying the magnitude and direction of return and volatility transmission across the major stock indices of these countries. Daily data provided by Exchange Traded Funds, (ETFs) as well as country stock indices are utilized 2 years before and 2 years after the November 2016 elections. We use the VECH specification of the multivariate GARCH model. The results indicate the existence of significant co‐movement of returns although some important differences before and after the elections are noted. Lagged values of the US returns significantly affect all other market returns in the pre‐election period, but not in the post‐election period. There is also evidence of volatility spillovers. Implications such as diversification opportunities for investors are discussed and the critical importance of understanding the transmission process between markets for risk management and economic policy are indicated.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".