ESSAYS ON RISK MANAGEMENT OF INSURANCE COMPANIES
Notice bibliographique
Résumé
This dissertation examines the risk management of insurance companies. It consists of three essays, which study the risk management of property and casualty (P/C) insurance companies. The first essay examines the impact of board diversity on firms’ risk-taking strategies using Canadian P/C insurance companies. The findings show that board ethnic diversity significantly decreases company risk as measured by reinsurance, asset risk, and leverage risk. Ethnic background values of the board members could be the reason behind this effect, board members with ethnic backgrounds from countries with high (low) Uncertainty Avoidance Index (UAI) decrease (increase) the risk. Results also show that in a diversified business environment, the ethnic diversity of directors has a less critical role in implementing risk-reducing strategies. Also, we show that board ethnic diversity improves company performance. In the second essay, we examine whether the personal background of decision-makers affects accounting estimates. We use cultural origin and gender of actuaries and CEOs as a proxy of personal background and test their effect on the accuracy of loss reserves. Our results show evidence that the cultural origin of actuaries, but not CEOs, are significantly associated with the accuracy of loss reserves. We show that cultural values of Masculinity, Uncertainty Avoidance, Power Distance and Individualism could explain the effect of cultural origin on the accuracy of loss reserves. We find evidence that cultural values that promote greater (lower) uncertainty, greater (lower) overconfidence, and more (less) risky attitudes are associated with lower (greater) accuracy of loss reserves. In addition, we show that actuary gender is significantly associated with the accuracy of loss reserves upon under-reserving only.\nThe third essay studies the time variation of the market price of Catastrophe bonds for the period 1999-2016. While we find an overall decreasing trend in the price of expected loss risk, large catastrophes increase this price by an order of 34% on average. Our empirical tests show that the latter effect is temporary and unlikely to be the byproduct of behavioral changes in investors’ perceptions about catastrophic risk as previously argued. Instead, we find evidence that the changes in the price of expected loss risk may be explained by changes in investor effective risk aversion, initiated by catastrophic events triggering Cat bond losses that could bring investors closer to their habit consumption levels and lead to a hard reinsurance market environment. Contagion effects from the reinsurance markets are more relevant after main catastrophes given the levels of liquidity in the markets. Furthermore, contagion effects from financial markets are minor and only relevant during the subprime financial crisis as documented in previous studies.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».