Idiosyncratic contagion between ETFs and stocks: A high dimensional network perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines the return spillovers between Exchange-Traded Funds (ETFs) and stocks. While traditional approaches focus on proportional relationships between ETFs and their underlying assets, we develop a high-dimensional network framework that captures spillover effects between any ETF-stock pair, regardless of their compositional relationship. By separating idiosyncratic and systematic risks, we investigate potential drivers of contagion. We document substantial heterogeneity in spillover patterns across sectors, which is previously unaddressed in the literature. Sectors such as Utilities and Real Estate exhibit robust spillovers to both their component stocks and assets in other sectors. Conversely, in sectors such as Consumer Discretionary and Finance , cross-sector influences dominate intra-sector ETF-constituent linkages. Our results also highlight that during periods of high market volatility, sources of idiosyncratic contagion become more diverse, suggesting the need for broader market surveillance beyond the few most influential ETFs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it