A Risk Management Tool for Long Liabilities: The Static Control Model
Bibliographic record
Abstract
This paper looks at the problem of valuing and managing the A/L M risks associated with insurance liabilities that are too long to be matched by available investments. Two very different approaches to the problem are explored. The first approach called Yield Curve Extension starts with a number of simple ideas for extrapolating a yield curve and analyzes them from a risk management perspective. The paper concludes that these methods lead to unnecessarily extreme A/L M strategies. The paper then describes a second approach called the Static Control Model which allows one to use a total return vehicle as part of the A/L M strategy. The model decomposes a long liability into fixed income and total return components in a market consistent way. The fixed income component is a static hedge for the liability in the sense that it matches the first order sensitivities of the model liability as observable market information changes. The paper concludes by arguing that the Static Control Model leads to more useful A/L M strategies for long liabilities. Introduction and Summary of Results The issue of managing long liabilities has been a practical problem for life insurers and pension plan sponsors for many years. In developed economies products such as Long Term Care insurance in the United States, or Term to 100 life insurance in Canada, have very low lapse rates and can create liabilities with very long durations. In less developed economies even traditional life insurance products can be difficult to match simply because the local debt markets are not well developed. While the problem is far from new the advent of market consistent financial reporting requirements and the demands of evolving Enterprise Risk Management standards mean that the inherent difficulties should be addressed more comprehensively than they have in the past. So what should a comprehensive approach to managing long liabilities look like? In this author’s opinion a comprehensive approach should be able to do the following 1. Put a value on the liability that can be used in a market consistent balance sheet. 2. The value must roll forward in time in such a way that the resulting market consistent income statement makes sense. 1 The author is Vice President for Risk Research in the Group Risk Dept. of AEGON NV. 1111 North Charles St. Baltimore, Md. USA. 2 The views and opinions expressed in this paper are those of the author and not AEGON NV.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".