Notice bibliographique
Résumé
This thesis, entitled Essays on Financial Economics and Macroeconomics, studies the interactions between real macroeconomics and financial variables. There is an emerging literature aims to investigate how can we reduce the impacts from the financial crisis by considering both macroeconomics and finance conditions together. For example, decision-makers should consider the financial market conditions first before policies are made. Meanwhile, the forecasting of short term financial variables' returns should take long term macroeconomic conditions into consideration. This has motivated us to explore further in the relationship between the macroeconomic factors and financial market conditions. In the first chapter, we examine the short-run and long-run dynamics of the correlation between exchange rate and commodity returns, and assess the extent to which the long-run correlation is determined by economic fundamentals. Our empirical analysis is based on the dynamic conditional correlation model with mixed data sampling (DCC-MIDAS) of Colacito, Engle and Ghysels (2011). This model provides a framework that captures the high-frequency relation between exchange rate and commodity returns as well as the low-frequency relation of volatility and correlation to economic fundamentals. Using both economic and statistical criteria, we find that the DCC-MIDAS\\ model augmented with economic fundamentals performs better than competing models in sample and out of sample. In the second chapter, we investigate the direction of Granger causality between business and financial cycles. Our analysis is based on a vector autoregression model applied on mixed frequency data. This allows us to condition on data from higher frequency variables (such as monthly industrial production) and lower frequency variables (such as quarterly aggregate credit) in a way that avoids the effects on data aggregation. Our empirical investigation focuses on five industrialized countries: USA, Canada, UK, Germany and Japan. Firstly, we examine whether the monthly industrial production index causes quarterly aggregate credit or vice versa. Then, we determine the timing of when causality is statistically significant. We find that there is strong bidirectional causality between business and financial cycles. The timing of causality varies across countries, but for all countries, bidirectional causality is significant during the financial crisis. The third and final chapter, which is an extension of the second chapter, investigates the role of the US as a global leader. Specifically, by paring US with other country (i.e, Canada, UK, Germany and Japan), we examine whether the US industrial production or credit causes the industrial production or credit of the other countries. In addition, we investigate whether causality is affected by the nominal interest rate. Our main finding is that the US business cycle strongly causes the business cycles of Canada, the UK and Germany. Finally, there is strong evidence that causality tends to be significant when the US interest rate is higher.
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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».