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Record W7019925471

The Interaction between financial markets and monetary policy

2020· dissertation· en· W7019925471 on OpenAlexaboutno aff

Bibliographic record

VenueUEA Digital Repository (University of East Anglia) · 2020
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsMonetary policyDebtGovernment (linguistics)Order (exchange)Context (archaeology)Payment
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0050.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.025
GPT teacher head0.190
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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