Interconnection between cryptocurrencies and energy markets: an analysis of volatility spillover
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The annual energy consumption of cryptocurrencies has been increasing in recent years. This paper studies the cryptocurrencies return volatility spillover and the underlying dynamics of five cryptocurrencies, namely Bitcoin, Bitcoin Cash, Ethereum, Ripple XRP and Litecoin's impact on four energy markets, namely Nifty Energy Index, S&P 500 Energy Index, S&P/TSX Canadian Energy Index and Shanghai Stock Exchange Energy Index for the period 2016–2021. We employed the Granger Causality Test and DCC MGARCH model to investigate the integration between cryptocurrencies and the energy markets. From the empirical analyses, we find that the overall time‐varying correlation between cryptocurrencies and the energy markets is low and weak. This study may be helpful for investors, academia and policymakers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it