Covid-19 Pandemic and the Swedish Stock Market Response : Case Study using a VAR and Bayesian TVP-VAR Model
Bibliographic record
Abstract
This study examines the impact of the Covid-19 pandemic on the stock market performance of firms of different sizes in the Swedish economy.The analysis focuses on the small cap, mid cap, and large cap indices of the OMXSPI, considering Covid-19 infections at the regional and global levels.The study employs a bivariate vector autoregressive (VAR) and a time-varying parameter vector autoregressive (TVP) model to establish the relationship between the number of infections and stock market returns.The findings reveal that the pandemic had a significant effect on the stock market, with varying impacts on different market segments based on their market capitalization.Smaller capitalized companies experienced higher volatility and greater market returns but were also more vulnerable to market declines.The study highlights the importance of considering firm size in analyzing the effects of the pandemic on the stock market.The results contribute to our understanding of the relationship between Covid-19 infections and stock market returns, providing valuable insights for investors, policymakers, and market participants.Further research is suggested to explore additional factors and potential policy implications.1 Introduction Late December 2019 was the start of the most severe economic crisis since the Great Depression (Gopinath, 2020), causing widespread concern worldwide.Alongside the rapid spread of the virus and the absence of a viable treatment in sight (WHO 2020), the highly volatile financial market exacerbated the turmoil, leading to chaotic trading and substantial declines (Dang, M et al., 2021).Consequently, the stock market experienced rapid decline in March 2020, characterized as one of the most rapid declines in history.The Covid-19 pandemic caused significant disruptions to the global economy, resulting in a sharp contraction in economic activity across many countries.With the forced closures of businesses, widespread job losses, and reduced consumer spending, governments implemented various measures, such as fiscal stimulus packages, loan programs, and unemployment benefits, to support their economies.The subsequent recovery from the economic downturn has been uneven, with certain sectors and regions rebounding more rapidly than others.However, it is argued that the effects of the pandemic can vary depending on the size of the firm, which brings us to the study's focus.This study examines the performance of OMXSPI's small cap, mid cap, and large cap indices in relation to Covid-19 infections across different regions, including Sweden, Europe, and globally.Building upon Banz's size effect theory (1981), which suggests that smaller capitalized companies exhibit higher volatility, leading to greater market returns, we aim to explore the implications of this volatility for investors.Moreover, Pendse and Slen's (2016) research emphasizes that higher volatility and market returns also imply greater risk which in this case are believed to be very problematic for the investors.Younger enterprises, lacking market power and financial buffers, may be more vulnerable to market volatility and economic downturns.To deepen our understanding, we draw insights from Switzer's (2010) study, which investigates the behavior of small cap and large cap stocks during economic downturns and recovery periods in the United States and Canada.The performance differences between these stock categories during such periods may be attributed to factors like market capitalization, liquidity, growth potential, risk aversion, and investor sentiment, among others.We employ a bivariate vector autoregressive (VAR) and time-varying parameter vector autoregressive (TVP-VAR) model to establish a relationship between the number of Covid-19 infections and stock market returns for firms of different sizes.To accomplish this goal, we use data provided by the World Health Organization, focusing on the Swedish, European, and global regions.By applying this model to our dataset, our goal is to identify whether there is a
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".