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Record W193249844

A Risk Management Tool for Long Liabilities: The Static Control Model

2009· article· en· W193249844 on OpenAlexaboutno aff
B. John Manistre

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsActuarial scienceHedgeLiabilityEconomicsLife insuranceRisk managementLiability insuranceCurrent liabilityControl (management)Yield (engineering)PensionBusinessInsurance policyFinanceWorking capital
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.209
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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