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Essays in Risk Management

2020· article· en· W7037932636 sur OpenAlexaboutno aff

Notice bibliographique

RevueResearchArchive–Te Puna Rangahau (Victoria University of Wellington) · 2020
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueEcology and Vegetation Dynamics Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSurpriseVolatility (finance)Market liquidityProxy (statistics)Private information retrievalLiberian dollarRealized varianceVariance (accounting)Public information
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This thesis consists of five chapters that examines risk and uncertainty within two frameworks: foreign exchange market and real options. The first chapter is a preliminary part that overviews the structure of thesis. In the second chapter, I examine the impact of scheduled macroeconomic announcements on realised variance in the Canadian dollar/US dollar foreign exchange market. Information shocks as a whole are made up of public information shocks and private information shocks. I measure the public information shocks from the analyst forecast surprise and the private information shocks from volatility sensitivity to liquidity variables. I find that the realized variance is driven mainly by the latter rather than the former. However, my results for the most important announcements are not significant, which might be due to these being well-analysed publicly. Spread, as a proxy of private information shocks, is the most important liquidity measure, showing a significant increase around the arrival of announcements. My results are robust to joint effects of liquidity variables, considering announcements throughout the day (times other than 8:30 announcement), alternative measures of volatility (absolute return and modified absolute return), evaluation of announcements for US and Canada separately, examine the impact of surprise in model, and the economic classification of announcements. In the third chapter, I aim to evaluate risk and uncertainty using real options technique. I develop a framework to evaluate representative agents’ behaviour in a real options switching framework. I set up three models with revertible switching process under uncertainty and solve these using the alternating direction implicit algorithm. The models break down into: cash-cost model, cash-time model, and projection model. The cash-cost model captures the cash expenses of switching whereas the cash-time model not only captures the cash cost but also the exact time cost, which is critical in horticulture. The projection model presents an approximation of cash-time model that has less computational complexity. The results of my sensitivity analyses indicate that increases in cost, time, volatility, drift, and discount rate have negative impacts on the switch frequency. If the correlation between two crops is positive, it has negative impacts on switch frequency, otherwise it has positive impacts. Differences between the models are more pronounced over longer periods. In the fourth and fifth chapters, I extend the cash-time model from chapter three to evaluate orchardists’ behaviour in the Hawke’s Bay region. Chapter four examines the dataset thoroughly and provide a statistical review of orchards that will be modeled in chapter five. Orchardists have the incentive to switch from one type of apple to another as the apple profits change. In my model, orchardists have the option to carry on with the existing apple trees or to switch to competing apple types by uprooting the existing apple trees and planting new ones or grafting on the existing rootstock. The uprooting strategy is relatively expensive but is instantaneous, and results in young (unproductive) apple trees with a long life ahead of them. In contrast, the grafting strategy is less expensive and faster but continues with old trees. I compute the optimal land value at each age of apple trees from one-year to 33-years old. My results show that grafting is the optimal strategy when trees are young, whereas planting becomes optimal when they are old. Examining the apple dataset, I find that orchardists are biased against uprooting and grafting relative to my predictions. The deviation from what my model proposes and what orchardists follow in reality might be due to the assumption of my model and possible factors in the orchards that my model does not capture. My results show that the deviation from optimal policy for small orchardists is not significantly different from large orchardists.

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 candidatesCharge utile insuffisante (le modèle a refusé de juger)
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 enseignants0,209
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 technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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 Opus0,011
Tête enseignante GPT0,214
Écart entre enseignants0,203 · 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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