Bibliographic record
Abstract
According to the World Health Organization, tens of millions of confirmed cases and hundreds of thousands of confirmed deaths have been registered worldwide. COVID-19, a kind of coronavirus, has emerged as one of the most serious dangers to the global economy and financial markets in human history. The Covid-19 virus's introduction has caused a global reduction in economic activity, perhaps posing new dangers to financial stability. This study aims to look into and reveal the effect of coronavirus on two financial markets. Ten advanced countries' capital market and money market data with the time interval from March 2020 to November 2020 has been used in this study. Six indices of these financial market Shares, Mutual Funds, Treasury Bills, Certificates of Deposits, Bonds, and Mortgages worked as samples. The research has been conducted on advanced nations USA, Norway, Canada, Germany, Ireland, Sweden, Singapore, Netherlands, Australia, and Switzerland. Panel Regression Analysis, Spearman's rank correlation, and ANOVA are used to estimate the study results. The scholar constructs a weekly panel data of COVID- 19 confirmed cases and financial market indices. The second purpose is to calculate the Risk on the six chosen indices of these markets. COVAR methodology is used to measure the risks among capital market and money markets indices. Interestingly, this research noticed that all financial markets impacted by the coronavirus while the capital market has recorded maximum fluctuations and the stock market show minimum volatility. The final results give a detailed understanding of financial market indices. It will support future research on other money and Capital markets indices and investors after the Coronavirus period. KEYWORDS: Coronavirus, Financial Markets, COVAR, COVID-19 Confirmed Cases. Capital Market. Money Market, Developed economies,
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".