Spillovers and Correlation Among Energy Futures Markets
Bibliographic record
Abstract
In recent years the economic risk has increased for all entities. One form of risk that has been consistently felt is exposure to fluctuations in raw material prices. The volatility of both financial and commodities markets is the subject of considerable attention. In particular, volatility in the energy commodities markets has become very important in recent years on tension on the commodity markets. In general, we refer to commodity price fluctuation recorded in 2009 and 2012-2013. Instead, analyzing energy commodities markets in more detail, we refer to price increases recorded in 2008, 2014 and 2022. For example, in 2014 and 2022 the Russia-Ukraine conflict had significantly consequences and repercussions on the price of natural gas and all commodity energy. Volatility is a factor of market instability as it measures risk. For this reason, attracts great attention for policy makers and financial market participants. From this premises we intend to estimate the appropriate model to analyze the volatility and correlation between the different energy commodities considered. The purpose of the paper is to analyze the presence and extent of volatility transmission in energy markets: Crude oil, Natural gas, Gasoline and Heating oil. Finally, we perform a rolling estimation and forecasting of a model. The results show a significantly volatility spillovers effects and large GARCH affects for all markets. The study provides evidence for a high level of integration in energy commodity derivatives markets.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".