What Explains the Sovereign Credit Default Swap Spreads Changes in the GCC Region?
Bibliographic record
Abstract
This paper aimed to investigate the drivers of sovereign credit risk spreads changes in the case of four Gulf Cooperation Council (GCC) countries, namely Kingdom of Saudi Arabia (KSA), the United Arab Emirates (UAE), Qatar, and Bahrain. Specifically, we explained the changes in sovereign credit default swap (hereafter SCDS) spreads at different locations of the spread distributions by three categories of explanatory variables: global uncertainty factors, local financial variables, and global financial market variables. Using weekly data from 5 April 2013, to 17 January 2020, and the quantile regression model, empirical results indicate that the global factors outperform the local factors. The most significant variables for all SCDS spreads are the global financial uncertainty embedded in the Chicago Board Options Exchange (CBOE) volatility index (VIX) and the global conventional bond market uncertainty embedded in the Merrill Lynch Option Volatility Estimate (MOVE) index. Moreover, the MOVE index affects the various SCDS spreads only when the CDS markets are bullish. Interestingly, the SCDS spreads are not affected by the global economic policy and the gold market uncertainties. Additionally, a weak dependence is observed between oil prices and SCDS spreads. For the country-specific factors, stock market returns are the most significant variable and impact the SCDS spreads at different market circumstances.
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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 | 0.000 | 0.002 |
| 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".