Sectoral and Regional Volatility Connectedness: The Case of CDS Spreads and Equities
Bibliographic record
Abstract
This study analyses volatility connectedness at sectoral and regional level within and across the US, UK, EU and Japanese regions between the CDS and equity markets. Analysis is made on 32 sectors and 70 sub-sectors within the regions under study with each having 2,479 observations, covering the period between 2008 until June 2017. The sample is divided between crisis and after-crisis period and the novel connectedness index by Diebold-Yilmaz (2014) is proposed. The domestic and regional analysis show that connectedness between the two asset classes is in general higher during the crisis period. Although the static Gaussian results for the regional analysis show low levels of connectedness across the board, the dynamic analysis show significant connectedness levels, with levels being predominantly higher during the crisis period, signifying contagion effects also at regional level between the two asset classes. When considering the dynamic volatility connectedness between the two asset classes, equity is the asset class which transmits volatility the most. In the US and EU connectedness between the two asset classes in most sectors is predominantly large during disturbed periods, particularly the 2009 crisis and the EU sovereign crisis.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".