Factors Affecting Derivatives Use for Life Insurance Companies
Bibliographic record
Abstract
The aim of this article is to investigate what factors affect derivatives use for life insurance companies in Korea. For life insurance companies in Korea, there are some problems to solve. First one is to meet IFRS standard which emphasizes solvency. Second one is to overcome problems from macroeconomic including low economic growth and low interest rate, fluctuating foreign currency exchange rate, and problems from population composition change and longer longevity. One of the possible ways to control the risks that life insurance companies face is using derivatives. Traditionally life insurance companies use reinsurance to hedge their inherent risks. However, hedging by using derivatives provides some different merits from those by reinsurance, such as, effects of controlling risks from macroeconomic change, in some cases less costs to control risks, etc. So using derivatives to control risks for life insurance companies is not only for sustainable management but for growth and becoming more competitive. The study results show that asset size, foreign assets and liabilities, proportion of deposit insurance, liquidity, RBC are significant factors affecting derivatives use.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".