The Effect of Global Crises on Stock Market Correlations: Evidence from Scalar Regressions via Functional Data Analysis
Bibliographic record
Abstract
This paper presents a novel, mixed-frequency based regression approach, derived from functional data analysis (FDA), to analyze the effect of global crises on stock market correlations, using a long span of data, dating as far back as early 1800s, thus covering a wide range of global crises that have not yet been examined in the literature in this context. Focusing on the advanced nations in the G7 group, we observe heterogeneous effects of global crises on the convergence patterns across developed stock markets. While the post World War II period experienced a general rise in the level of correlations among developed stock market returns, we find that global crises in general have led to a stronger association of stock market returns in the US, UK and Canada, whereas the opposite holds when it comes to how European and Japanese stock markets co-move with the US. Overall, our results suggest that crises that are global in nature generally contribute to the convergence of global stock markets, while the effect largely depends on the context and nature of the crises that possibly drive the perception of risk and/or contagion in financial markets. From an investment perspective, our findings suggest that, in the wake of global crises, diversification benefits will be limited by moving funds across the US and UK stock markets whereas possible diversification benefits would have been possible during the crises-ridden period of the early twentieth century by holding positions in equities in the remaining G7 nations to supplement positions in the US. However, these diversification benefits seem to have frittered away in the post World War II period, highlighting the role of emerging markets and alternative assets to improve diversification benefits in the modern era.
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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 | 0.016 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.002 | 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".