A lattice-based approach for life insurance pricing in a stochastic correlation framework
Bibliographic record
Abstract
We propose a new implementation approach in insurance product valuation to capture the stochastic correlation between financial and demographic factors. This is important to accommodate the prevailing situation where the interest rate and mortality intensity move jointly and randomly. A stochastic correlation model is considered where it follows a diffusion process that may assume the form of a bounded Jacobi process or of a transformed modified Ornstein–Uhlenbeck process. Our contributions strengthen the general modelling set up of dependent financial and actuarial risks. We put forward a discrete-time pricing model that supports the valuation of a relatively wide class of insurance products. Specifically, the pricing of contracts, with an embedded surrender option for which no explicit formulae are available, is facilitated with ease. We customise the construction of lattice discretisations that admit a large set of risk processes having appropriate specifications. In particular, the interest rate, mortality and correlation dynamics are discretised via three different binomial lattices that are then assembled to create a trivariate lattice structured with eight branches for each node. Numerical experiments involving some stylised insurance contracts are conducted. Such experiments confirm the accuracy and efficiency of our proposed approach with respect to two benchmarks: the Monte-Carlo simulation method, and the method and results by Devolder et al. (2024). • The model is useful to evaluate insurance products under stochastic correlation. • The interest rate process and the mortality intensity process are correlated. • The correlation dynamics is described by a stochastic process. • Recombining binomial lattices discretizes the three processes. • The three lattices are combined in a trivariate lattice having eight branches.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".