Spouse-Protected Tontines: Household Decumulation via Neural-Network Optimization

Battle Count: 50

While the paper uses advanced quantitative methods (NN optimization, CVaR) relevant to portfolio management, its primary focus is on actuarial science and retirement product design (tontines) rather than high-frequency trading or general asset allocation strategies for institutional investors. It is highly relevant to insurance-linked securities and longevity risk transfer.

High complexity due to the multidimensional stochastic control problem, the need for custom neural network architectures with constrained outputs, and the extensive Monte Carlo simulation required for evaluation and training. Requires expertise in both actuarial modeling and deep learning.

The paper provides detailed specifications for the neural network architecture, training hyperparameters, data sources (Human Mortality Database, ASFA Retirement Standard), and return generation methods (stationary block bootstrap). Algorithms for simulation are provided in appendices. However, the code itself is not explicitly linked in the provided text, though the methodology is fully described.