Economic Policy Uncertainty and Government Bond Prices
Bibliographic record
Abstract
Purpose : This paper investigated the impact of economic policy uncertainty (EPU) in the US, UK, Japan, Italy, India, Germany, France, China, Canada, and Brazil on Indian government bond prices using a new dataset of the Clearing Corporation of India Limited Broad Total Return Index (BTRI) and Liquid Total Return Index (LTRI).Methodology : We used the quantile regression approach and monthly dataset from January 2004 – December 2020 for the analysis.Findings : We found that the top 20 government bond prices decreased due to EPU in India, Japan, the US, and the UK. In contrast, the EPU of Canada, China, and the UK had a statistically significant positive impact on BTRI. Further, a negative relationship was found between the top five government bond prices and the EPU of three economies: India, Japan, and the US.Practical Implications : The analysis will help identify potential risks and vulnerabilities in government bonds. It assists regulators and policymakers in implementing effective risk management measures to safeguard financial stability. The findings will also be useful for investors and market participants to make informed investment decisions.Originality : From a data standpoint, this is the first study that used CCIL’s BTRI and LTRI data for the first time to canvass the impact of EPU on Indian government bonds as far as we know. Further, we took into account the unique characteristics of the EPU of the top 10 economies and directly compared the reaction of these economies’ EPU to the government bond price fluctuations.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".