COVID-19 pandemic and risk dynamics of financial markets in G7 countries
Bibliographic record
Abstract
Purpose The purpose of this study is twofold: to examine the effects of the COVID-19 pandemic on the risk dynamics of stock and bond markets in G7 countries; and to examine if the stock-bond risk dynamics can be linked to government measures to contain the pandemic. Design/methodology/approach To examine the pandemic impact on the risk dynamics of the bond and stock markets, this study chooses G7 countries for their efficient financial market properties. This study uses standard generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) and exponential GARCH (1,1) models to determine the most volatile and sensitive market, most persistent market during the crisis and the leverage effect between stock and bond markets. This study then uses a panel study to investigate whether this volatility in stock and bond markets is affected by the COVID-19 cases and various government responses (fiscal stimulus packages, monetary policy, emergency investment in health care and vaccine investment). Findings The findings of the study confirm that the bad news of the pandemic is causing higher volatility than good news for all seven stock markets. Canadian stock and bond markets are the most volatile, and Italian bond and stock markets are the most sensitive G7 countries. Japan has shown the highest persistence, and the stock market exhibits higher leverage than the bond market. Fiscal stimulus packages are helping to reduce bond market volatility, but none of these measures are effective in the stock market. Research limitations/implications The pandemic is still spreading, and the rate at which it spreads wildly will always pose a limitation to any attempt to examine its full effect. Practical implications Investigation of market volatility will help policymakers and market players formulate the best strategies to overcome and exit the crisis and plan post-pandemic solutions. It provides valuable insights for investors to rebalance their portfolios during highly volatile markets while preserving their risk appetite and investment objectives. Originality/value The paper provides evidence on the impact of the pandemic-induced crisis and the respective government responses on the volatility of competing capital markets (stock and bond) in countries that are considered most efficient in reflecting news.
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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 | 0.000 |
| 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".