How the interpolation of life tables affects the decomposition of life insurance surplus

Battle Count: 108

The paper is primarily focused on life insurance surplus decomposition and actuarial risk management rather than quantitative trading. However, the Shapley value-based decomposition framework and the treatment of multi-frequency data (daily financial vs. yearly biometric) could have tangential relevance to multi-asset risk attribution in trading portfolios. The financial risk component (discount factor from bond yields) connects to fixed-income markets but is not analyzed from a trading perspective.

The IASU/ASU decomposition requires understanding of stochastic calculus (Itô's lemma, semimartingales) and the Shapley value framework. The interpolation methods (Lee-Carter, linear, constant) are straightforward to implement. The main complexity lies in correctly handling the multi-frequency data alignment (daily bond prices vs. yearly life tables) and computing the iterative Shapley value approximation over daily time steps. The numerical experiments are relatively simple (single contract, 16-year window).

All data sources are publicly available with URLs provided (mortality.org for German life tables, data.ecb.europa.eu for government bond yields). The methodology (IASU/ASU decomposition, interpolation formulas) is clearly specified with equations. However, no code repository is provided, and the specific contract parameters (N=100,000, x=45, T=25) are stated but implementation details for the numerical experiments are somewhat sparse.