Contagion Effect of Natural Disaster and Financial Crisis Events on International Stock Markets
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
In the contemporary world bustling with global trade, a natural disaster or financial crisis in one country (or region) can cause substantial economic losses and turbulence in the local financial markets, which may then affect the economic activities and financial assets of other countries (or regions). This study focuses on the major natural disasters that occurred worldwide during the last decade, especially those in the Asia–Pacific region, and the economic effects of global financial crises. The heteroscedasticity bias correlation coefficient method and exponential general autoregressive conditional heteroscedasticity model are employed to compare the contagion effect in the stock markets of the initiating country on other countries, determining whether economically devastating factors have contagion or spillover effects on other countries. The empirical results indicate that among all the natural disasters considered, the 2008 Sichuan Earthquake in China caused the most substantial contagion effect in the stock markets of neighboring Asian countries. Regarding financial crises, the financial tsunami triggered by the secondary mortgage fallout in the United States generated the strongest contagion effect on the stock markets of developing and emerging economies. When building a diversified global investment portfolio, investors should be aware of the risks of major natural disasters and financial incidents.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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