Diversification benefits of cat bonds: An in‐depth examination
Bibliographic record
Abstract
Abstract We investigate whether the inclusion of Cat Bonds in portfolios composed of traditional assets and common factors is beneficial to investors. Various mean‐variance spanning tests performed for the period of 2002 to 2017 show that under different market conditions, the addition of Cat Bonds gives rise to previously unattainable portfolios. Using the Engle (2002) Dynamic Conditional Correlation (DCC) model, we find that including Cat bonds increases significantly the time‐varying Sharpe ratio and the Choueifaty and Coignard (2008) maximum diversification ratio. Cat Bonds provide needed diversification during critical times particularly during episodes of crisis and of high volatility. Under the second‐order stochastic dominance efficiency (SDE) tests, the null hypothesis that portfolios without Cat Bonds are efficient cannot be rejected. Out‐of‐sample analyses indicate that the performance of portfolios with Cat Bonds included varies depending on the performance measures employed, the portfolio construction techniques used and the assets or factors considered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".