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Enregistrement W7019925471

The Interaction between financial markets and monetary policy

2020· dissertation· en· W7019925471 sur OpenAlexaboutno aff

Notice bibliographique

RevueUEA Digital Repository (University of East Anglia) · 2020
Typedissertation
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueMonetary Policy and Economic Impact
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMonetary policyDebtGovernment (linguistics)Order (exchange)Context (archaeology)Payment
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This thesis deals with the interaction between financial markets and monetary policy from three different perspectives. First, I study the perspective of equity investors and their reaction to the Federal Open Market Committee (FOMC) announcements, when they disagree on Nominal Interest Rate level decisions. My evidence shows that investor expectations formulated prior to FOMC announcements have a significant impact on equity prices, particularly when these expectations are not aligned with the FOMC committee decisions. My results reconcile past findings on the monetary policy surprise literature and more recent empirical findings on the effect of FOMC announcements on equity markets. Moreover, as I find no effect on equity returns when the FOMC committee decision is anticipated by the market, a practical implication of my study is that monetary policy authorities should take into account market expectations when formulating disclosure policy in order to improve alignment with financial market expectations and smooth out their economic consequences.
\nSecond, I provide evidence of the effects of the European Central Bank (ECB) monetary policy shocks on the real economy, specifically on industrial production and inflation. This analysis investigates how the ECB monetary policy shocks impact industrial production (output) and inflation (prices) following the established narrative methodology of Romer & Romer (2004). Past standard statistical approaches have yielded very limited results in terms of magnitude. The narrative methodology, conversely, has yielded significant effects of monetary shocks on prices and output. Most of these studies analysed the effect of monetary policy in the United States and only a recent portion of the literature has extended the analysis to other countries (United Kingdom and Canada). This chapter contributes to the extant literature in extending the narrative methodology to the Eurozone and adapting it to include the unconventional monetary policies put in place by the Governing Council of the ECB in the past decade. To do so, I gather a novel dataset of macroeconomic forecasts and construct a new measure of monetary policy shocks. Industrial production responds to unpredictable monetary policy shocks with a decline of over 0.5%. On the contrary, inflation responds weakly to monetary shocks, with a very modest and unstable decrease of 0.05%. Furthermore, I provide empirical evidence of the heterogeneous responses of inflation and output among Eurozone countries. These last results are particularly relevant to policy makers of the ECB Governing Council, given that their policy decisions should have a homogenous effect on the Eurozone economy.
\nThird, I investigate whether financial market stability is a concern for monetary policy makers in the case of the European Central Bank (ECB) and Bank of England (BOE). Whether financial market stability should be a concern of monetary policy makers is an unresolved and long debated question, which has resurfaced after the 2008 financial crisis. In this chapter, I propose a forward-looking Augmented Taylor (1993) Rule to investigate the conduct of monetary policy and apply this idea to the 2003–2018 time period for both the ECB and the BOE. I show that a forward-looking Augmented Taylor Rule explains the deviation of observed rates consistent with its implied rates. By including a measure of Financial Market Stability Slack, I also show that the evolving preferences of monetary policy makers have taken into account the financial markets turmoil, particularly in the aftermath of the 2008 financial crisis.

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 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)0,000
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 enseignants0,257
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,025
Tête enseignante GPT0,190
Écart entre enseignants0,166 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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