THE IMPACT OF THE COVID-19 ON THE FINANCIAL MARKETS: EVIDENCE FROM G7
Bibliographic record
Abstract
This essay aims to analyse the impact of the 2020 global pandemic on the stock indexes of France (CAC 40), Germany (DAX 30), USA (DOW JONES), United Kingdom (FTSE 100), Italy (FTSE MID), Japan (Nikkei 225) and Canada (TSX 300), from January 2018 to June 2020, with the sample being divided into two sub periods: first sub period from January 2018 to August 2019 (Pre-Covid); second period from September 2019 to June 2020 (Covid-19). In order to carry out this analysis, different approaches were taken in order to analyse whether: (i) the global pandemic (Covid-19) increased the persistence of the G7 financial markets? In the Pre-Covid period, we can verify the presence of long memories in the Canadian market (TSX), while the markets in France (CAC 40) and Italy (FTSE MID) show signs of balance, since the random walk hypothesis was not rejected. The German (DAX 30), USA (DJI), United Kingdom (FTSE 100) and Japan (NIKKEI 225) markets have anti-persistence (0 <α <0.5). In period II, the Covid-19-time scale is contained, and we verified the presence of significant long memories, except for the US stock index (0.49). These findings make it possible to show that the assumption of the market efficiency hypothesis may be called into question, because these markets are predictable, which validate the research question. The results of the pDCCA correlation coefficients, in the Pre-Covid period, show 14 pairs of median markets (0.333 → ≌ 0.666). We can also see 7 pairs of markets with strong correlation coefficients (0.666 → ≌ 1,000), showing that these markets have a tendency towards integration, this evidence may call into question the hypothesis of portfolio diversification. In period II (Covid-19) the λ_DCCA correlation coefficients have 7 strong market pairs (0.666 → ≌ 1,000), 5 pairs have weak pDCCA coefficient (0.000 → ≌ 0.333), 5 market pairs show anti-correlation (-1.000 → ≌ 0.000), and 4 market pairs show median coefficients (pDCCA) (0.333 → ≌ 0.666) (out of 21 possible). When compared to the previous subperiod, we found that the majority of the pDCCAs decreased, which shows that the markets have decreased their integration, making it possible to diversify portfolios in certain markets, especially in the Japanese market (NIKKEI 225). These conclusions open space for market regulators to take measures to ensure better informational information, in the stock markets, in the 7 most advanced economies in the world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".