ESSAYS ON RISK MANAGEMENT OF INSURANCE COMPANIES
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".