Green Bond Yield Determinants in Indonesia: The Moderating Role of Bond Ratings
Bibliographic record
Abstract
This study investigates the relationship between bond-specific factors, macroeconomic variables, and green bond (GB) yields issued in the Indonesian bond market. The study sample includes 468 GBs issued by 30 issuers from both corporate and government entities from 2018 to 2023. This research method uses panel regression techniques with the random effects models to test hypotheses on two estimation model specifications. The study results reveal that interest, inflation, and exchange rates are significantly and positively related to GB yields. Bond-specific factors have different impacts, where coupons and maturity have a positive relationship with GB yields, while bond issuers have an adverse effect. Bond rating and issuance size as specific factors are shown to have no impact on GB yields. In the model with the moderating role of rating, the study’s results show that coupons still directly impact GB yields positively, while the influence of maturity is negative. The interaction of maturity and rating positively impacts GB yield. Different findings suggest that interactions with coupons weaken the impact of ratings on GB yields. The results of this study contribute to the financial literature on the determinants of the GB market and the role of bond ratings as a moderator. The study also provides new insights into Indonesia’s GB market, which includes developing countries. The findings can also help companies, investors, regulators, and researchers better understand the GB market.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".