Geopolitical Risks and Yield Dynamics in the Australian Sovereign Bond Market
Bibliographic record
Abstract
Geopolitical risks and shocks such as military conflicts, terrorist attacks, and war tensions are known to cause significant economic downturns. The main purpose of this paper is to determine the dynamics between Australian sovereign bond yields and geopolitical risk. This is achieved by employing a quantile regression analysis. The findings of this study indicate that the impact of geopolitical risk on Australian sovereign yield dynamics is asymmetrical. Furthermore, an increase in geopolitical risk only impacts short-term yields at extreme regimes. However, the impact is, by and large, insignificant. On the other hand, an increase in geopolitical risk does have a statistically significant positive impact on medium- and long-term yields across most quantiles. Lastly, an increase in geopolitical risk tends to result in a steeper yield curve at the belly of the curve but causes the yield curve to flatten at the long end. This study is the first study that holistically examines the dynamics between geopolitical risk and Australian sovereign bond yields. The study thereby contributes to the body of knowledge on Australian bond yields, specifically, and adds to the sparse body of knowledge on the dynamics between geopolitical risk and sovereign bond yields. The findings of this study have implications for monetary policy makers, given that shifts in sovereign bond yields could impact all three core mandates of the Australian Reserve Bank. Furthermore, changes in the slope of the yieldcurve could be used by monetary policy makers to pre-empt changes in future economic growth. The results of this study also relate to fiscal policy formulation, given that yields directly impact the cost of government borrowing. Lastly, portfolio managers could benefit from the results of this study, as these results provide information on the ability of Australian sovereign bonds to hedge against geopolitical 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".