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Record W7043740432

A tale of two pension plans: Measuring pension plan risk from an economic capital perspective

2019· other· en· W7043740432 on OpenAlexfundno aff

Bibliographic record

VenueKent Academic Repository (University of Kent) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaThe Institute and Faculty of ActuariesUniversity of Waterloo
KeywordsPensionValuation (finance)Pension planLife expectancyContext (archaeology)Economic capitalLife insuranceLongevity riskRisk management
DOInot available

Abstract

fetched live from OpenAlex

Years of high inflation, good investment returns and profits during the 1970s and 1980s created the illusion that defined benefit (DB) pension plans are easily affordable. Due to the creation of large surpluses during those years, pension risks have generally been excluded from an organisation's general risk management processes. Over the past decade or more however, increasing life expectancy and a steady fall in interest rates have meant that pension costs have increased. Consequently, many DB pension plans now have insufficient assets to cover all of their promised benefits. As a result, security of members' benefits may be compromised. This research, funded by the Society of Actuaries, builds on the works of Porteous et al. (2012), who performed a risk assessment of UK's Universities Superannuation Scheme (USS) based on the 2008 USS valuation report. In this research project, we update the analysis based on the most recently available valuation report. We then extend our analysis to carry out risk assessment of a stylised US plan, with the same membership profile as USS but with plan provisions modified to reflect a typical US DB plan design. We employ an economic capital approach to assess risks. Although the term economic capital has been widely used within the banking and insurance sectors, the concept is relatively new in the context of risk assessment of DB pension plans. In this research, we adapt the commonly used definitions of economic capital to appropriately capture the specific risk characteristics of DB pension plans. The analysis was carried out using stochastic economic scenario generators (ESG) calibrated to the UK and US economies. Specifically, we use a graphical model approach to ESG, proposed by Oberoi et al. (2019), alongside the well-known Wilkie model, to capture the sensitivity of the results to the choice of ESGs employed. The analysis also used a stochastic mortality model, similarly calibrated to the UK and the US. Results are shown for the full distribution of outcomes, but emphasis is given for certain percentile levels in line with the selected degree of confidence. We find that as a percentage of starting assets, the US stylised plan is more volatile than the USS plan. Moreover, the benefits of a larger allocation to long bonds are greater in the US stylised plan than the USS plan. In general, the effect on economic capital (for both plans) is much larger for changes in asset allocation than for changes to plan contributions. The full distribution of results provided should assist plan sponsors to understand the full range of uncertainties that they are assuming in the financing of their DB pension plans. An economic capital framework provides pensions regulators with another tool to consider their exposure to benefits guaranteed by the Pension Protection Fund and the Pension Benefit Guaranty Corporation. The analysis can also help the DB pension plan members to understand the uncertainties that the sponsor faces in the financing of DB pension plans.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.003
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.225
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2019
Admission routes1
Has abstractyes

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