Covid-19 Pandemic and the Swedish Stock Market Response : Case Study using a VAR and Bayesian TVP-VAR Model
Notice bibliographique
Résumé
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. IntroductionLate 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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».