SME, Crisis and Geopolitical Risk: Lessons from COVID-19 and War
Bibliographic record
Abstract
This paper explores how geopolitical risk impacts small and medium-sized enterprises (SMEs), focusing on the COVID-19 pandemic and the Russia–Ukraine war. Using daily return data from the ECPI Italy SME Equity and Shenzhen SME Composite indexes, as well as the Global Geopolitical Risk Index, this study employs a Dynamic Conditional Correlation GARCH model to analyze how correlations change over time. The results show that Asian SMEs, represented by China, exhibit higher short-term volatility but stronger long-term resilience than their European counterparts. Notably, Asian markets react consistently across crises, while European markets distinguish between different events. These findings provide important insights for policymakers, suggesting the need for standardized crisis response frameworks and emphasizing short-term mitigation efforts. This study adds to SME theory by highlighting the complex relationship between geopolitical shocks and SME performance, with important implications for risk management and regulatory strategies in emerging economies.
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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 | 0.007 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".