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Enregistrement W2542344939 · doi:10.25439/rmt.27581103

The causes and consequences of operational risk: some empirical tests

2016· dissertation· en· W2542344939 sur OpenAlexaboutno aff

Notice bibliographique

RevueRMIT Research Repository (RMIT University Library) · 2016
Typedissertation
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueBanking stability, regulation, efficiency
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOperational riskEmpirical researchEmpirical evidenceBusinessRisk analysis (engineering)EconomicsActuarial scienceRisk managementFinanceStatistics

Résumé

récupéré en direct d'OpenAlex

The thesis provides empirical evidence on the causes and consequences of operational risk. First, by including operational losses endured by firms across all sectors worldwide, we investigate the determinants that potentially explain cross-country differences in operational risk. These determinants are based on country-level information. They can be broadly classified into three categories measuring three unique dimensions of a country: macroeconomic, regulatory, and social. To circumvent model-specification issues and variable-selection bias, we carry out the empirical work according to extreme bounds analysis (EBA), which is an econometric modelling approach suggested by Leamer (1983, 1985) and further extended by Granger and Uhlig (1990), as well as Sala-i-Martin (1997).<br><br>The empirical results show that operational-loss severity, on average, rises as a country’s GDP level and the cost of living increase. In addition, a country as a whole is more likely to experience catastrophic losses with a poorer regulatory and governance standard, particularly against the background of the rigorous process by which a country’s government is selected, monitored, and replaced; and also on the capacity of that government to formulate and implement sound policies effectively. Furthermore, the overall development of a country’s citizens—including their life expectancy, education, and income levels—also plays a role when comparing operational-loss severity from one country to another.<br><br>Second, to address the consequences of operational risk, we use an event-study approach to examine the economic impact of operational-loss announcements on firms’ stock market value and the potential reputational damage that follows. We distinguish operational-loss settlement news from its initial press release to detect potential discrepancies in market reactions to the two announcement types, and we examine the effect of gradual information release. We account for the nominal amount of operational losses to separate the reputational effect of the loss announcement from its direct monetary impact, hence refining the measures of reputational risk. We scope the empirical estimation at a firm-level for 331 operational-loss events settled by commercial banks headquartered in the United States, the United Kingdom, and Canada during the period 1995 to 2008.<br><br>The findings reveal that the stock market reacts negatively to the initial press release of operational-loss events, as well as to its settlement news across all three countries analysed. This negative reaction is more abrupt surrounding the event dates, highlighting the strong initial reaction to loss news, although it fades quickly after the announcements are made to the public. This suggests that the market selloff may be short-lived. Reputational risk is consistently evident in the global and in all of the sub-regional samples, indicating that the market tends to overreact to operational-loss announcement. In addition, the market appears to be more sensitive to announcements of (i) losses resulting from internal fraud; (ii) losses of a bigger magnitude with an undisclosed loss figure; (iii) losses that result in restitutions, and (iv) losses that are consequences of regulators’ investigation.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
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 enseignants0,270
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
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 Opus0,039
Tête enseignante GPT0,279
Écart entre enseignants0,240 · 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
Publié2016
Routes d'admission1
Résumé présentoui

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