Hedging Performance and Multiscale Relationships in the German Electricity Spot and Futures Markets
Bibliographic record
Abstract
We explore optimal hedge ratios and hedging effectiveness for the German electricity market. Given the increasing attention that wavelets received in the financial market, we concentrate on the investigation of the relationship, covariance/coherence evolution and hedge ratio analysis, on a time-frequency-scale approach (discrete and continuous), between electricity spot and futures. Simpler approaches are also used for comparison purposes like the naïve, OLS and the dynamic multivariate GARCH model in order to account for risk reduction through hedging. Results allow us to conclude that: dynamic hedging strategies provide higher variance reductions in terms of hedging effectiveness; there is poor correlation among spot and futures, not being homogeneous across scales, which condition the effectiveness of the hedging strategy; the long-horizon hedge ratio does not converge to its long run equilibrium of one. Wavelets poor fit in variance reduction is attributed to low coherence and to statistical relationships between spot and futures electricity series. The instability found in various aspects of market comovements may imply serious limitations to the investor’s ability to exploit potential benefits from hedging with futures contracts in electricity markets. Moreover, much variation in the contemporaneous relationship among spot and futures may highlight inadequacy in assuming (short-term) relationships in both markets, which might account for the difficulty in achieving profitable active trading.
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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 | 0.001 | 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 | 0.000 |
| 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".