Geopolitical risks and commodity prices: The case of Canadian recourse companies
Bibliographic record
Abstract
This thesis examines the impact of geopolitical risks and commodity prices on the stock returns and volatility of Canadian resource companies from 2004 to 2023, with a focus on the oil and energy sector, gold sector, and other minerals mining sector. Employing a comprehensive analysis of non-segmented and segmented data which has three distinct periods-2004-2009, 2010-2018, and 2019-2023-this study addresses the nuanced effects of geopolitical risk (GPR) in times marked by both economic stability and major global disruptions. The findings reveal that GPR significantly influences stock return volatility and mean equation, and also has a sector specific difference with oil and energy sector being the most effected one. The local Canadian GPR index was found to exert a more substantial impact on both the mean and volatility of stock returns compared to the global GPR index, emphasizing the importance of local geopolitical events in the Canadian market context. This thesis contributes to the empirical literature by highlighting the sector-specific responses to GPR and commodity price fluctuations, offering valuable insights for policymakers, regulators, and investors. It underscores the critical need for incorporating commodities in investment portfolios and adapting investment strategies to account...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".