Using Markov Chains and Entropy to Explain Value at Risk in European Electricity Markets
Bibliographic record
Abstract
The increasing complexity of energy systems amid the global push for decarbonization raises important questions about how transitions in the energy matrix affect financial risk in electricity markets. This study investigates the relationship between structural changes in national energy matrices and the systemic risk associated with electricity prices in Europe from 2015 to 2022. Using daily electricity price data, we calculate log returns and estimate the Value at Risk (VaR) at the 1% level as a measure of extreme financial loss. We incorporate energy market variables, including the volatility of Brent oil and coal prices, and an entropy-based indicator derived from the Shannon index, which captures the degree of technological dispersion in the energy mix over time. A fixed-effects panel regression model is applied across 21 European countries to identify the drivers of energy-related financial risk. Results show that higher volatility in Brent and coal prices significantly increases the VaR, and that greater entropy reflecting a more complex and dynamic energy transition also correlates with higher systemic risk. These findings suggest that while energy diversification is a goal of sustainability, it may entail short-term instability. The study contributes to the understanding of how structural transformations in energy systems interact with financial vulnerabilities in liberalized electricity markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".