“Slow-Burn” Spillover and “Fast and Furious” Contagion: A Study of International Stock Markets
Bibliographic record
Abstract
“Fast and furious” contagion across capital markets is an important phenomenon in an increasingly integrated financial world. Different from “slow-burn spillover” or inter- dependence among these markets, “fast and furious” contagion can occur instantly. To investigate this kind of contagion from the U.S., Japan, and Hong Kong to other Asian economies, we design a research strategy to capture fundamental interdependence, or “slow-burn spillover”, among these stock markets as well as short-term departures from this interdependence. Based on these departures, we propose a new contagion measure which reveals how one market responds over time to a shock in another market. We also propose international portfolio analysis for contagion via variance decomposition from the portfolio manager’s perspective. Using this research strategy, we find that the U.S. stock market was cointegrated with the Asian stock markets during the four specific periods from July 3, 1997 to April 30, 2014. Beyond this fundamental inter- dependence, the shocks from both Japan and Hong Kong have significant “fast and furious” contagion effects on other Asian stock markets during the U.S. subprime crisis, but the shocks from the U.S. have no such effects.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".