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Enregistrement W7047826203

The impact of economic uncertainty on the financial markets: evidence from interest rates, exchange rates and cryptocurrency

2023· dissertation· en· W7047826203 sur OpenAlexaboutno aff

Notice bibliographique

RevueePrints Soton (University of Southampton) · 2023
Typedissertation
Langueen
DomaineEngineering
ThématiquePhotocathodes and Microchannel Plates
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFinancial marketInvestment (military)Interest rateEconomic riskRisk aversion (psychology)Financial riskEconomic modelEconomic impact analysis
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

There has been a growing interest in studying economic uncertainty and its propagation on the economy and financial markets since the last global financial crisis. Literature provides ample evidence of the interconnectedness between major economic, financial, political shocks, economic uncertainty, and economic stagnation. This thesis consists of three essays that extend the literature with a focus on economic uncertainty from various sources and its impact on the real economy alongside with the financial markets.<br/>In chapter 2, we theoretically investigate different measurement methods of constructing economic uncertainty and three major transmission channels of uncertainty shocks to the economy. We perform a structured examination of three major proxies for uncertainty in the literature, including the financial uncertainty, the survey-based uncertainty, and the newspaper-based uncertainty. Considering the pros and cons of each uncertainty measurement's approach, we use the newspaper-based uncertainty as our uncertainty estimator to implement empirical analysis of its impact on economic activities and financial markets. Also in this chapter, we also document three major transmission channels of uncertainty shocks to the economy, including real option, risk aversion, and growth options effects. Uncertainty, under the real option and risk aversion channels exerts a negative influence on the economic activities by diminishing financial wealth, curbing investment and consumption, and increasing perceived risks of market participants. While uncertainty under the growth options channel, on the contrary, promotes riskier investments and economic growth's outlook.<br/>In chapter 3, we empirically study the impact of economic uncertainty shocks in the bond markets on the dynamics of the entire term structure of interest rate. Conducting on the bond yields, volatility and holding excess returns for the US, UK and Japan, we find that the responses of the yield and volatility factors to uncertainty shocks are more pronounced for US and UK markets. Besides, the impact of uncertainty on bonds' yields is shown to be larger for shorter-term bonds in shorter investment horizons, while the impact of uncertainty on bond's volatility exhibits a hump-shape pattern. Moreover, the inclusion of uncertainty factor in the term structure model helps explain the term premia and improve the prediction power of the model without being spanned by the three main components of the yield curve (level, slope, and curvature).<br/>In chapter 4, we investigate the propagation of monetary policy uncertainty to the determinations of exchange rate's behaviors and the role of uncertainty in explaining the forward premia puzzle. Our empirical results using quantile threshold regression method show that the impact of monetary policy uncertainty on forward exchange rate premia are significantly different in the two uncertainty regimes and heterogeneous across exchange rate quantiles. We then employ a quantile-based approach to obtain the time-varying conditional distribution as well as the risk measures of future evolution of the exchange rate. Our findings indicate tight connectedness between risk measurements (represented by appreciation and depreciation risks) and important economic events associated with high monetary policy uncertainty. Moreover, the risk measure diagrams for different pairs of currencies point out that the US dollar, Japanese yen and Canadian dollar are qualified as safe-haven currencies due to their low volatility in depreciation risks and abnormal large upside movement during high uncertainty periods.<br/>Finally, in the last chapter of this thesis, we explore the dynamic of economic policy uncertainty on Bitcoin returns and volatility. Using the Quantile-on-Quantile regression model and the Quantile-Granger causality approach, we detect the heterogeneous impacts of economic uncertainty on Bitcoin returns and volatilities across distributions of all considered variables for all markets. The relations between Bitcoin returns and uncertainty are shown to be notably strong during high uncertainty periods, implying the hedging ability of Bitcoin against uncertainty in some markets. The effects of uncertainty on Bitcoin volatility are found significant at extreme quantiles of both variables, implying the speculative characteristics of Bitcoin reflected by the high volatility and sensitivity of Bitcoin’s price fluctuations to investor sentiment<br/>

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,707
Score d'incertitude au seuil0,919

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 savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
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 Opus0,030
Tête enseignante GPT0,258
Écart entre enseignants0,227 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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
Publié2023
Routes d'admission1
Résumé présentoui

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