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Impact of oil prices on stock market performance

2023· dissertation· en· W7010371620 sur OpenAlex

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Notice bibliographique

RevueLondon Met Repository (London Metropolitan University) · 2023
Typedissertation
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueMarket Dynamics and Volatility
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésStock marketStock market bubbleVolatility (finance)Stock (firearms)Oil priceMarket makerStock exchangeVector autoregression
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The study investigates the impact of oil prices on stock market performance in ten countries, including Canada. The volatility in oil prices and the accompanying swings in stock market performance raised the question of what, if any, is the relationship between these variables. The research seeks to address six strands of the phenomenon. The study evaluates the impact of oil prices on stock performance at the stock market’s aggregate and sector market levels. It establishes the effects of macroeconomic variables on stock market performance. Furthermore, it evaluates the role of the business cycle in the oil price shocks and stock market interface. Lastly, it examines the influence of oil prices on stock market performance in net oil-importing and oil-exporting countries. The empirical investigation uses monthly data from January 2003 to December 2020 and quarterly data from 1990Q1 to 2020Q4. Primary and secondary data were analysed using statistical tools and econometric modelling. The investigation employs the impulse response function, EGARCH and Markov switching models. The thesis concludes that the relationship between oil prices and stock market performance is time-varying, asymmetrical, heterogeneous and complex as several sector or country-specific factors drive the relationship. Specifically, the findings suggest that the response of the stock market sectors to oil price shocks differs substantially, depending on their degree of oil dependence and multiple transmission mechanisms. The findings further indicate that stock returns-generating processes in a net oil-exporting country like Canada exhibited a high degree of persistence in conditional variance, and the modelling of asymmetry was positive. Positive shocks from macroeconomic variables impact the country’s stock market more than negative shocks of the same magnitude. Two structural breaks are identified in the Canadian economy between 1990 and 2020. The data was further divided into two subsamples to reflect the two possible states for an economy, the bear and bull periods. Empirical analysis revealed that GDP, exchange rate, inflation rate, interest rate, and oil prices are significant drivers of the country’s stock market performance in economic contraction. During the expansion era, all the variables considered in the study, excluding GDP, significantly drive stock market performance. Hence, oil prices and stock market relationships tend to improve more during the economic expansion period than during the contraction era. Further analysis affirmed that the impact of oil price shocks is only significant in the top two net oil-importing countries. These findings convey information that guides policymakers in formulating macroeconomic policies, investors and portfolio managers in risk diversification relating to decision-making and investment strategies. The study investigates the impact of oil prices on stock market performance in ten countries, including Canada. The volatility in oil prices and the accompanying swings in stock market performance raised the question of what, if any, is the relationship between these variables. The research seeks to address six strands of the phenomenon. The study evaluates the impact of oil prices on stock performance at the stock market’s aggregate and sector market levels. It establishes the effects of macroeconomic variables on stock market performance. Furthermore, it evaluates the role of the business cycle in the oil price shocks and stock market interface. Lastly, it examines the influence of oil prices on stock market performance in net oil-importing and oil-exporting countries. The empirical investigation uses monthly data from January 2003 to December 2020 and quarterly data from 1990Q1 to 2020Q4. Primary and secondary data were analysed using statistical tools and econometric modelling. The investigation employs the impulse response function, EGARCH and Markov switching models. The thesis concludes that the relationship between oil prices and stock market performance is time-varying, asymmetrical, heterogeneous and complex as several sector or country-specific factors drive the relationship. Specifically, the findings suggest that the response of the stock market sectors to oil price shocks differs substantially, depending on their degree of oil dependence and multiple transmission mechanisms. The findings further indicate that stock returns-generating processes in a net oil-exporting country like Canada exhibited a high degree of persistence in conditional variance, and the modelling of asymmetry was positive. Positive shocks from macroeconomic variables impact the country’s stock market more than negative shocks of the same magnitude. Two structural breaks are identified in the Canadian economy between 1990 and 2020. The data was further divided into two subsamples to reflect the two possible states for an economy, the bear and bull periods. Empirical analysis revealed that GDP, exchange rate, inflation rate, interest rate, and oil prices are significant drivers of the country’s stock market performance in economic contraction. During the expansion era, all the variables considered in the study, excluding GDP, significantly drive stock market performance. Hence, oil prices and stock market relationships tend to improve more during the economic expansion period than during the contraction era. Further analysis affirmed that the impact of oil price shocks is only significant in the top two net oil-importing countries. These findings convey information that guides policymakers in formulating macroeconomic policies, investors and portfolio managers in risk diversification relating to decision-making and investment strategies.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,058
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,015
Tête enseignante GPT0,228
Écart entre enseignants0,213 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle