Litigation Risk in Directors’ and Officers’ Liability Insurance : The Impact of Company-Related Risk Factors on the Insurance Premium
Bibliographic record
Abstract
A directors’ and officers’ (D&O) liability insurance policy prices the risk of litigation. The objective of this thesis is to investigate whether company-specific risk factors are incorporated in the pricing, and in such case what effect these risk factors have on the premium. \nFor a sample of Canadian companies listed on the S&P/TSX Composite Index, we collect information on insurance coverage and premiums for a time period ranging from 2010 to 2020. Insurance data are matched with measures of risk estimated from both market and book values, along with information regarding the governance structure of companies. Before assessing how risk factors affect the premium, this study examines how the choice of coverage limit is affected. The empirical analysis further show that several of our hypothesized risk factors do affect the D&O premium, either directly or through the choice of coverage limit. As such, we find that coverage will be more costly for companies with higher litigation risk, proxied by company size, profitability, leverage, volatility, and others. \nBy implementing the Merton Model, we estimate the value and volatility of company assets. Analysing the premium based on these variables yield similar results to the estimation based on the directly observable variables of stock volatility and market capitalization. A common feature for both approaches is that the volatility measures are found to be important determinants of the premium. Additionally, the majority of the other risk measures are found to display similar impacts on the premium no matter the measures utilized for size and volatility. \nAltogether, the results obtained in this thesis indicate that insurers address and incorporate various company-related risk factors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".