Navigating the Green Frontier: Dynamic Risk and Return Transmission Between Clean Energy ETFs and ESG Indexes in Emerging Markets
Bibliographic record
Abstract
This study is designed to investigate the dynamic risk transmission processes between clean energy ETFs and ESG indices in the BRICS countries—Brazil, India, China, and South Africa—while excluding Russia due to the lack of consistent data availability during the study period, which coincides with the Russia–Ukraine conflict. The analysis is conducted on daily data obtained from DataStream, spanning from 27 October 2021 to 5 January 2024. By applying a time-varying parameter vector autoregression (TVP-VAR) modeling framework, we considered examining the global market conditions and economic shocks’ effects on these indices’ interconnectedness, including COVID-19 and geopolitical tensions. In this context, clean energy ETFs turned out to stand as net shock transmitters throughout volatile market spans, while ESG indices proved to act as net receivers. Moreover, we undertook to estimate both of the minimum variance and minimum connectedness portfolios’ hedging efficiency and performance. The findings highlight that introducing clean energy indices into investment strategies helps boost financial outcomes while maintaining sustainability goals. Indeed, the minimum connectedness portfolio consistently delivers superior risk-adjusted returns across varying market circumstances. In this respect, the present study provides investors, regulators, and policymakers with practical insights. Investors may optimize their portfolios by integrating clean energy and ESG indexes, useful for achieving financial and sustainability aims. Similarly, regulators might apply the findings to establish reliable green investment norms and strategies. Thus, this work underscores the crucial role of dynamic portfolio management in optimizing risk and return in the globally evolving green economy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".