Cross-Market Liquidity Shocks: Evidence from the CDS, Corporate Bond, and Equity Markets
Bibliographic record
Abstract
Using data from the credit default swap (CDS), corporate bond, and equity markets, we construct several measures of liquidity and examine the spill-over of liquidity shocks across these markets. Based on the principal component analysis of multiple liquidity measures, we show that there is a dominant first principal component in each of the markets. However, the linkage of liquidity shocks varies between different markets. In particular, there is a common component between the equity and both CDS and bond markets, but not between the CDS and bond market. Moreover, the vector autoregression results show that while there is spill-over of liquidity shocks between equity and CDS markets, surprisingly there is no clear spill-over of liquidity shocks between equity and bond markets. There appears to be a time lag of liquidity spill-over from the CDS to both bond and equity markets. Finally, we find no evidence of liquidity spill-over from bond to CDS market. Market liquidity has received great attention in recent finance literature and liquidity risk is generally viewed as an important factor of asset prices. Moreover, recent work in this area has
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".