COVID-19 pandemic and stock market volatility spillovers
Bibliographic record
Abstract
Purpose This paper aims to explore the impact of the coronavirus on the volatility spillovers of 10 selected developed markets hit by this pandemic (e.g. the USA, Canada, Korea, Japan, the UK, Germany, Italy, Spain, France and China). Design/methodology/approach The database consists of daily data from January 1, 2020, to December 31, 2022. The data used are the precise daily closing prices of various indices of selected markets gathered from the DataStream and Investing.com databases. The authors use the VAR model to study the transmission of volatility between stock markets and analyze the dynamic links between them. Then, the Granger causality test is used to study the volatility movements and determine which of these markets is likely to influence the others. Then, impulse response functions are used to understand the reactions of the studied markets following shocks in the two most important markets, namely, the American and Chinese markets. Finally, forecast errors variance decomposition is used to measure the dynamic interactions that characterize the relationships between the studied markets. Findings Empirical results reveal instability in the returns of various indexes and the existence of causal relationships between standardized volatility of markets. The reactions of some markets following a shock in American and Chinese markets differ among markets. The empirical results also show that forecast errors variance of some markets begin coming from their own innovations during first periods. These shares decrease then in favor of other markets interventions. Practical implications The findings have significant practical implications for governments around the world as well as for financial investors. The successful practice of China’s pandemic prevention and control efforts may inspire governments to determine how to overcome panic and strengthen confidence in victory. Policymakers can use the insights from our study to design more effective economic policies and regulations to mitigate the negative impact of future pandemics on the financial system. Regulators can use these results to identify areas of weakness in the financial system and take proactive measures to address them. Financial investors may use the outcomes of our result to better understand the impact of global pandemics on financial markets. They may know which markets are the most active, which ones are causing considerable effects on the others and which ones show resilience and an anti-risk capacity. This may help them to make appropriate decisions about their investments. Originality/value It has become imperative to estimate the impact of this pandemic on the behavior of financial markets to prevent the deterioration and dysfunction of the global financial system. The findings have important implications for financial investors and governments who should know which markets are the most shaken, which cause remarkable effects on others and which show resilience and anti-risk capacity. Countries could follow China in some measures taken to moderate the negative effects of this epidemic on national economies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".