Some considerations about roll forward for pension funds
Bibliographic record
Abstract
Nowadays, most of the companies offer their employees a wide range of benefits, including a post-retirement benefit, which is usually accumulated during the years of service rendered by the employee.Most of these benefits are accrued in pension funds that the company holds.The underlying objective of this study is to analyse the behavior of the value of total liabilities with roll forward method.Typically, the roll forward have much applicability in different sectors, however, in this work is intended to deepen the concept in pension funds sector, and, compare results with an actuarial valuation.This method was done for three consecutive years: 2013, 2014 and 2015 with a real population.The results of applying the roll forward method are obtained in three steps.Initially, the formula of roll forward was applied without considering its gains and losses.In a second step, the results were analysed taking into account the previously mentioned gains and losses.Finally, two deterministic scenarios were analysed regarding to the discount rate, thereby, increasing the roll forward with the impact that the sensitivity has in its responsibilities; thus obtaining the third step of the roll forward.Finally, for validating the method for its effectiveness when used for the purpose of accounting of liabilities it was compared to the results using an actuarial valuation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".