Extreme Connectedness between Green Bonds, Government Bonds, Corporate Bonds and Other Asset Classes: Insights for Portfolio Investors
Bibliographic record
Abstract
This paper aims to examine the connectedness between green and conventional assets, particularly during the period of economic downturn. Specifically, we examine quantile-based time-varying connectedness between the green bond market and other financial assets using quantile vector autoregression (QVAR) from 9 March 2018 to 10 March 2021. We use daily prices of S&P U.S. Treasury Bond Index, S&P US Aggregate Bond Index, S&P US Treasury Bond Current 10Y Index, S&P 500 Bond Index, S&P 500 Financials index, S&P 500 Energy Bond Index and S&P 500, giving a total of 784 observations, and using Composite Index as a representative of conventional assets classes and S&P Green Bond Index to denote the green bond market. Results shows the connectedness between green bonds and the conventional asset classes intensified during the outbreak of the Coronavirus pandemic (COVID-19) as investors shifted their investment towards fixed income assets due to the plunge in the prices of stocks and commodities. The results also shows that green bonds are strongly connected with treasury bonds, aggregate bonds and bond index, as they share similarities with respect to issuance, risk and governance. Connectedness is weak in the case of composite index and energy bond index, as their prices do not have substantial influence on the green bond market. The study highlights the hedging and diversification benefits of green bonds. We have several implications for portfolio managers, policy makers and researchers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".