Linkages Between Oil Price Shocks and Stock Retums Revisited
Bibliographic record
Abstract
The main component of this thesis is a paper which examines the relationship between oil price shocks and stock market returns across 15 countries. Prior to this paper, I discuss the vast literature surrounding oil prices and their effect on the macroeconomy. The post-World War II period contains many examples of oil price shocks preceding US recessions causing many authors to postulate theories regarding the mechanisms which could explain this phenomena. As these theories garnered very little support from empirical studies, the unearthing of the true underlying mechanism driving oil price shocks became a major focus. This led Kilian (2009) to decompose oil prices into various components and show that, using a structural vector autoregression model, demand shocks are the main driver in explaining variations in the price of oil. Specifically focusing on the precautionary demand shocks identified by Kilian (2009), the paper presented in this thesis uses a similar quantile-on-quantile (QQ) regression model to the one introduced by Sim and Zhou (2015) in order to examine the behaviour between stock returns and oil price shocks. The study examines 15 countries whose classification as oil importers or oil exports depends on their net position in crude oil trade. The results indicate that the main finding by Sim and Zhou (2015) that large negative oil price shocks can bolster stock returns when markets are performing well is only partially supported by the three largest oil importers in the sample China, Japan and India during the period 1988:12007:12. When extending to more recent data (period 1988:1 2016:12) it is found that China and India experience higher returns when markets perform well and there is a large positive oil price shock. This effect is mirrored for oil exporting countries Canada, Russia, and Norway and moderately oil dependent countries such as Malaysia, Philippines, and Thailand, which see higher returns in the presence of large positive oil price shocks and well performing markets.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".