A partial correlation-based connectedness approach: Extreme dependence among commodities and portfolio implications
Bibliographic record
Abstract
We propose a partial correlation-based connectedness approach to study the directional connectedness under normal and extreme market conditions among the returns of 22 commodities and compare it with the well-known Diebold and Yilmaz (i.e. generalized forecast error variance decomposition (GFEVD)) connectedness approach estimated at the mean and tails. Considering four groups of commodities, namely energy, agricultural, precious metals, and industrial metals, and daily data from September 1, 2005 to June 5, 2024, covering various crisis periods, we draw filtered networks and measures of directional connectedness. The main results are summarized as follows. Firstly, the total connectedness index captures the significant commodities related shocks, and intensifies during crises episodes, notably at the extreme lower quantile. Secondly, using partial correlations in the approach of connectedness leads to a surge of the total connectedness level at the extreme lower quantile and identifies the beginnings of major crises earlier than the GFEVD measure of connectedness. Thirdly, the connectedness structure of commodities based on partial correlation is unstable during turbulent market conditions, a feature that is ignored when the GFEVD approach of connectedness is used. Fourthly, in terms of practical implications, the partial correlation-based connectedness portfolio outperforms the GFEVD based minimum connectedness portfolio on a risk adjusted basis. • We proposed a PCBC approach to study connectedness in 22 commodity returns under normal & extreme market conditions. • PCBC TCI detects major commodity shocks & surges during crises, signaling heightened market stress effectively. • PCBC TCI identifies crisis onset earlier than GFEVD, offering a more responsive measure of market turmoil. • PCBC portfolio outperforms GFEVD-based minimum connectedness portfolio on risk-adjusted basis, improving efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".