No contagion, only volatility: U.S. equity correlations during COVID-19
Bibliographic record
Abstract
Purpose-During the COVID-19 crisis, correlations between U.S. equity returns and those of its three primary trading partners—Canada, China, and Mexico—rose sharply. In particular, the average correlation climbed from 0.56 in 2019 to 0.83 in 2020, the peak year. This study investigates whether this nearly 48% surge signals a contagion effect stemming from COVID-19. Methodology-Price data of ADRs for Canada, China, and Mexico, traded on the New York Stock Exchange were collected and returns on equally weighted portfolios for each country were computed. Using the returns on the country portfolios of ADRs and the US equity stock index S&P 500, cross-country correlations between the U.S. and each of its major trading partner countries were computed. These estimates were revised by applying the volatility adjustment procedure recommended by Forbes and Rigobon (2002). The revised estimates of correlations were tested whether they differed from the stable period values. Findings-During the pandemic, unadjusted Correlations between U.S. equities and each of its major trading partners increased. These estimates were then adjusted for the increased volatility. The revised correlations were not found to be significantly different from their pre-pandemic values. Conclusion-Estimates of correlations between U.S. equity and its major trading partner countries increased dramatically during the pandemic, implying possible contagion. This conclusion would be premature and incorrect as volatility changes are ignored in the estimation process. When corrected for it, the revised estimates of correlations do not support the presence of contagion effect. Keywords: COVID-19, pandemic, correlations, contagion, ADR
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".